Integrated dynamic energy management for steel production (DYNERGYSteel) : final report.
Date: 2019-03-13
Institutions: Directorate-General for Research and Innovation
Subjects: electrical industry, electricity supply, energy efficiency, energy research, iron and steel industry, iron and steel product, metal product, report, research project

Integrated dynamic energy 
management for steel 
production 

(DYNERGYSteel) 

FINAL REPORT 

Research 
and 
Innovation 

EUR 29538 EN 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Integrated dynamic energy management for steel production (DYNERGYSteel) 
European Commission 
Directorate-General for Research and Innovation 
Directorate D - Industrial Technologies 
Unit D.4 — Coal and Steel 
Contact   Hervé Martin 
E-mail   RTD-PUBLICATIONS@ec.europa.eu 

European Commission 
B-1049 Brussels 

Manuscript completed in 2019. 

This  document  has  been  prepared  for  the  European  Commission  however  it  reflects  the  views  only  of  the  authors,  and  the 
Commission cannot be held responsible for any use which may be made of the information contained therein. 

More information on the European Union is available on the internet (http://europa.eu). 

Luxembourg: Publications Office of the European Union, 2019 

PDF  

ISBN 978-92-79-98335-1  

ISSN 1831-9424 

doi: 10.2777/027322 

KI-NA-29-538-EN-N 

© European Union, 2019. 
Reuse is authorised provided the source is acknowledged. The reuse policy of European Commission documents is regulated by 
Decision 2011/833/EU (OJ L 330, 14.12.2011, p. 39). 

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All pictures, figures and graphs © Rina Consulting Centro Sviluppo Materiali S.p.A., RFSR-Ct-2014-00029 DYNERGYSteel 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
European Commission 

Research Fund for Coal and Steel 

Integrated dynamic energy management 
for steel production 

(DYNERGYSteel) 

Francesca Marchiori 
Andrea Belloni, Marco Lupinelli 
Rina Consulting Centro Sviluppo Materiali S.p.A. - Via di Castel Romano 100, IT-00128 Roma (Italy) 

Bernd Kleimt, Alexander Ebel, Fabien Nkwitchoua, Holger Rosemann 
VDEh–Betriebsforschungsinstitut GmbH - Sohnstrasse 65, DE-40237 Düsseldorf (Germany) 

Valentina Colla, Silvia Cateni, Antonella Vignali, Gianluca Nastasi 
Scuola Superiore di Studi Universitari e di Perfezionamento Sant'Anna, Piazza Martiri della  
Libertà 33, IT-56127 PISA (Italy) 

Uggero De Miranda, Roberto Bontempi 
O.R.I. MARTIN - Acciaieria e Ferriera di Brescia SpA - Corso Garibaldi 49, IT-20100 MILANO (Italy) 

Massimo Pennesi 
ACCIAI SPECIALI TERNI SpA - Viale Benedetto Brin 218, IT-05100 TERNI (Italy) 

Ralf Sternitzke 
HOESCH HOHENLIMBURG GmbH - Oeger Strasse 120, DE-58119 HAGEN (Germany) 

Michele Benini 
RICERCA SUL SISTEMA ENERGETICO - RSE SpA - Via Raffaele Rubattino 54, IT-20134 MILANO (Italy) 

Michael Mayer, Philipp Stahlhofen 
LECH-STAHLWERKE GmbH - Industriestraße 1, DE-86405 MEITINGEN (Germany) 

Grant Agreement RFSR-Ct-2014-00029 
01.07.2014 – 31.12.2017 

Final report 

Directorate-General for Research and Innovation 

2019 

EUR 29538 EN

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table of contents 

1 

2 

3 

4 

5 

6 

Final summary ....................................................................................................... 5 

Scientific and technical description of the results ....................................................... 12 

2.1  Objectives of the project ........................................................................................ 12 

2.2  Description of activities and discussion .................................................................... 12 

2.2.1  Work package 1: Power market characterization: tariffs, negotiation, 

fluctuations, events ...................................................................................... 12 

2.2.2  Work package 2: Power demand by the steel plants: monitoring the actual 

engaged power and electric energy consumptions ............................................ 21 

2.2.3  Work package 3: Forecast of power engagement ............................................... 51 

2.2.4  Work package 4: Development of online devices to react on electric grid events .... 70 

2.2.5  Work package 5: Strategies for production redefinition to align and re-align 

actual power engagement with the forecast ..................................................... 85 

2.2.6  Work package 6: Dynamical access to the power market through an Agent 

System aimed to support the Decision Makers ............................................... 100 

2.3 

Conclusions ........................................................................................................ 120 

2.4 

Exploitation and impact of the research results ....................................................... 120 

2.4.1 

Actual applications ....................................................................................... 120 

2.4.2 

Technical and economic potential for the use of the results ............................... 120 

2.4.3 

Publications / conference presentations resulting from the project ..................... 121 

2.4.4 

Any other aspects concerning the dissemination of results. ............................... 122 

List of figures ..................................................................................................... 123 

List of tables ....................................................................................................... 127 

List of acronyms and abbreviations ........................................................................ 128 

Full list of references ........................................................................................... 132 

Annex 1 - D 1.1 Description of the general and local electricity markets and tariffs in Italy 

and in Germany 

Annex 2 - D 1.2 Description of the grid characteristics (events, time response, etc.) and 

interaction with large consumers 

Annex 3 - Statistical Imbalance Data Analysis 

Annex 4 - Mathematical formulation of Production Re-scheduling model 

3 

 
4 

 
 
1  Final summary  

WP 1  Power market characterization: tariffs, negotiation, fluctuations, events 

The aim of this work package is the determination of the power market and network characteristics 
influencing monitoring, forecasting and management of the power engagement in the steel plants. 

There  are  several  Electricity  markets  depending  on  when  the  energy  is  booked  with  respect  of 
when is utilized. In Figure 1 the Electrical Energy Market models is shown highlighting the markets 
depending on the energy demand forecasting time.. 

Figure 1 Price models for the purchase of electrical energy  0F

1 

The project is focussed on the markets from the day ahead to real time.  

Task 1.1 

Electricity market characterization and supply profile determination 

In this task the Day ahead & Intra Day markets have been deeply analysed leading to the following 
conclusion. 

The  company  can  access  directly  the  Day  ahead  &  Intra  Day  markets.  This  represent  the  most 
flexible option allowing to take full profit from hourly price differences and allowing to change the 
consumption profile nearer to real-time. It can be risky because it expose to the volatility of Power 
Exchange  prices  without  forward  contracts  that  fix  the  price  for  the  bulk  of  energy  consumption 
and it requires an internal structure that only larger companies could afford. In Germany, Belgium, 
Austria negative prices are present both in day-ahead and balancing markets. 

Task 1.2 
dynamical characteristics coming from the power network 

Characterization  of  grid  events  (slots,  power  fluctuations  etc.)  with  their 

The activities in this task aim  at  the  categorization of events due to the grid and the grid status; 
this  is  necessary  to  determine  the  dynamical  response  to  be  activated  by  the  steel  industry  in 
terms  of  typical  time  shifts  between  event  and  reaction.  Moreover,  the  economic  value  of  the 
ancillary services  that, in principle, could be provided  by a flexible electricity demand in the steel 
industry, is assessed. 

Information  about  the  technical  regulation  of  ancillary  services  in  Italy  and  in  Germany,  with  a 
particular focus on balancing services, such as primary, secondary and tertiary regulation, as well 
as interruptible loads has been collected. 

According to the required technical performances, tertiary regulation and interruptible loads seem 
the  two  services  most  suited  for  the  provision  by  large  industrial  loads,  even  if  in  Germany  also 
secondary  regulation  can  be  provided  by  flexible  loads.  Italy  start  some  pilot  projects  for  these 
markets in 2017. 

An analysis of the situation in several other European countries with reference to the participation 
of the demand in the balancing markets and the presence of negative prices has been carried out. 

1 FOREnergy: “Ergebnispapier – Flexibilisierung der Energienachfrage von industriellen Verbrauchern”, 
Ergebnisse des Arbeitskreises 1 “wirtschaftliche Synchronisation von Energieangebot und –nachfrage” des 
Forschungsverbundes FOREnergy, January 2015 

5 

 
 
 
                                                
 
Task 1.3 
steel manufacturing demand and grid offer 

Elicitation  and  definition  of  the  negotiation  rules  and  protocols  between 

The  analysis  carried  out  in  this  task  leads  to  the  following  opportunities  for  steel  industries  to 
participate in energy markets. 

The  first  opportunity  (day-ahead  &  Intra-day)  is  based  on  the  possibility  to  take  advantage  from 
the  oscillation  of  the  energy  price  during  the  day  and  between  working  days  and  weekends  / 
holidays.  It  can  be  noticed  that  the  hourly  price  profile  tends  to  be  similar  during  different  days 
therefore  the  production  can  be  scheduled  accessing  the  day  ahead  market  privileging  the 
utilization of energy during time slots that have lower cost. This means not only money savings but 
also less environmental impact  due to the fact that when  the price is lower electricity  is normally 
generated by less expensive and less polluting (possibly renewable) production sources. 

The  second  opportunity  (Secondary  and  Tertiary  Control  Reserve  SCR-TCR)  is  related  to  the 
possibility to dynamically react  to grid events providing ancillary (typically  balancing)  services  for 
the secure operation  of  the power system. This means both: decreasing or increasing the energy 
utilization. In Germany consumers are able to participate at the balancing market, both as a single 
user  or  as  a  consortium,  while  in  Italy  the  regulatory  authority  is  working  on  the  possibility  of 
enabling also the consumers to participate (up to now it is only possible for dispatchable generators 
with an installed power greater 10 MW). 

The  interruptible  loads  (AbLa-IL)  are  the  third  opportunity,  more  adopted  in  Italy  where  is  paid 
only based on power (MW) offered by the plant  and  not on energy (MWh). This situation is more 
related  to  grid  security,  i.e.  very  critical  situation,  therefore  the  reaction  time  should  be  faster 
compared  to  the  second  opportunity.  Since  in  our  experience  the  plant  offers  for  interruption  a 
percentage of the total power capacity, it is very important to control the overall energy utilization 
in the plant during the disconnection time in order to avoid an increment of non-interruptible loads 
that could partially offset the interrupted amount leading to the payment of fees and the possible 
exclusion from the programme.  

WP 2  Power demand by the steel plants: monitoring the  actual engaged power 
and electric energy consumptions. 

The aim of this Work Package is the collection of available data with reference to the process route 
of the industrial partners plants, the analysis of data related to events and the relevant data model 
derivation,  the  implementation  of  the  online-monitoring  system,  using  existing  data  and  the 
additional measurement devices installed. 

Task 2.1 

Manufacturing routes and layout analysis 

The  four  plants  of  the  industrial  partners  considered  in  this  project  can  be  regarded to  provide  a 
view  on  different  types  of  steelmaking  plants.  In  fact  one  stainless  steelmaking  plant  for  flat 
products with the complete production lines from EAF melting to finishing (AST), one plant for low 
and medium alloyed construction and engineering long products (LSW), one plant for special steel 
long  products  for  automotive  and  mechanical  industry  (ORI)  and  one  plant  for  hot  rolled  narrow 
strip production mainly for cold rollers (HHO) are covered. 

From the analysis performed in this task EAF and LF have been individuated as the most relevant 
processes to be studied for LSW and ORI, while at AST EAF and HRM will be the main objective of 
Electricity Flow Sheet Model (EFSM). HHO’s work is devoted to the development of additional load 
flexibilities in form of additional electricity usage by reacting on grid demands and the definition of 
the relevant processes/machines taken into consideration is done in WP4. 

Task 2.2 
consumption analysis and tune up of existing DBs 

Definition  of  the  Data  Model  for  power  engagement  and  electric  energy 

According to the analysis of the reference plants characteristics done in Task 2.1 the general Data 
Model has been defined in order to describe the most important sources of power engagement and 
electricity  use,  considering  the  role  in  the  project.  A  description  of  the  available  processes  and 
energy  data  at  each  of  the  industrial  partner  plant  is  provided  together  with    the  eventually 
necessary enhancement.  

The energy Data are available at least on a 15 min base, but mostly on a faster sampling rate, on 
all  the  involved  plants  globally  and  specifically  for  the  processes  individuated  in  Task  2.1. 
Improvement for energy  measurements  were individuated and implemented during the project to 
enhance the monitoring capabilities necessary for the implementation of DYNERGYSteel.  

6 

 
Task 2.3 
characterization; preliminary data acquisition campaigns  

Monitoring  devices  adjustment,  power  load  characteristics  analysis  and 

This  task  aimed  at  the  preliminary  characterization  of  both  internal  (process  data)  and  external 
data  (grid  behavior).  Data  from  plant  have  been  collected  and  statistically  evaluated  in  order  to 
quantify the energy necessary to each process identified in previous tasks.  

Four  different  aspects  were  monitored  and  analyzed  within  the  reporting  period  of  the  project. 
These aspects are 

 
 
 

 

an analysis of the EAF power load characteristic of single heats  
a statistical analysis of the EAF heats and operation mode, 
an  analysis  of  the  main  electrical  energy  consumers  of  the  steel  mill  (yearly  energy 
report), 
an analysis of the power load characteristic of the steel mill with a focus on short term 
deviations of the 15 minutes intervals. 

With  the  aim  of  better  understanding  the  possibilities  to  access  the  Control  Reserve  market  an 
analysis of the global imbalance of network have been performed based on data from Italian TSO. 
The aim is to develop a prediction algorithm whose result are taken into consideration in calculating 
the  KPI  and  integrated  in  the  agent  system.  Several  sophisticated  and  advanced  techniques  in 
order  to  predict  sign  of  imbalance  of  the  National  Transmission  Network,  average  selling  price  at 
the  Dispatching  Services  Market  and  average  purchase  price  at  the  Dispatching  Services  Market 
have been applied. 

Task 2.4 

Monitoring the actual power engagement and historical event frequency 

The monitoring systems have been enhanced or implemented in the reference plants improving not 
only the number of processes monitored but also the ease to use. 

WP 3   Forecast of power engagement 

In this WP the actual procedures for forecasting the power demand has been studied with the aim 
of  enhancing  the  accuracy  of  the  booked  profile.  Historical  data  analysis  has  been  carried  out  in 
order  to  evaluate  relations  among  energy,  process  and  product  data  to  determine  the  electricity 
needs based on product and process characteristics. 

An electricity flow-sheet model that can forecast the energy consumption related to those plants of 
a  steel  industry  characterized  by  a  relevant  energy  demand,  in  order  to  estimate  the  energy 
requirement in the more plausible manner has been developed.  

Based on those result control loop to manage the energy adjustment has been developed. 

Finally, an analysis of the flexible disconnectable loads in steel plants has been carried out taking 
into consideration the specificity of the industrial partners but aiming to have general remarks for 
the steel companies participation in energy markets. 

Task 3.1 
the steel plant according to heat / process and product mix 

Development of the methodology to predict the electric energy demand in 

The accuracy and reliability of the day-ahead forecast of the electrical energy demand is enhanced 
by  considering  energy  consumption  related  not  only  to  the  specific  process  but  also  to  the 
characteristics  of  the  product.  The  activities  are  therefore  focussed  on  the  determination  of  the 
electrical  energy  consumption  depending  on  the  product  type.  In  particular  EAF  energy  demand 
depending  on  the  steel  grades  to  be  produced  and  rolling  mill  energy  demand  based  on  product 
characteristics  (such  as  geometry,  process  line  and  steel  type)  has  been  analysed  based  on  data 
gathered from the industrial partners plants. Prosecution of the project. 

Task 3.2 
Development  of  Electricity  Flow  Sheet  Model  (EFSM)  of  the  process/plant 
systems;  identification  of  disconnectable  loads,  energy  storage  and  possible  switching 
between gas and electric sources 

An electricity flow-sheet model that can forecast the energy consumption related to those plants of 
a steel industry characterized by a relevant energy amount consumed has been developed in order 
to estimate the energy requirement in the more plausible manner. The flow sheet model has been 
developed  in  order  to  be  easily  generalized,  by  specializing  the  energy  time  consumption  of  a 
production item for each particular plant. 

Task 3.3 

Forecast model development and validation 

Different  time  frames  have  been  taken  into  considerations  to  develop  the  models  for  forecasting 
the  power  engagement:  Medium  (for  the  next  shift  and  up  to  3  days),  short  (for  the  next  90 

7 

 
minutes)  and  very  short  (for  the  next  15 minutes).  The  forecasted  electricity  demand  in  medium 
term, integrated with information from the production schedule, is used for the preparation of the 
profile to be shared with the energy provider the day before, the short term can be used to adjust 
the  energy  needs  participating  in  the  intra-day  market  while  the  very  short  term  is  used  to 
minimize  the  15  min  peaks,  to  participate  in  the  balancing  market  or  to  recover  from  an 
unpredictable event both internal or external. 

Several  approaches  have  been  developed  depending  on  the  time  frame,  the  process  and  the 
variability  of  its  behaviour,  and  the  levels  of  precision  necessary  ranging  from  analytical  to 
statistical  approaches.  In  EAF  in  medium  time  frame  the  processing  time  and  more  the  energy 
demand profile can vary a lot even for same type of product; so in this case a detailed profile might 
not  be  reasonable  in  all  cases,  but  an  average  value  for  time  and  energy  demand  with  some 
security  margin  gives  already  a  good  assumption.  When  considering  short  to  very  short  period  a 
detailed model has been set up able to evaluate the melting progress in order to be able to decide 
whether  is  possible  to  reduce  the  power  to  limit  the  actual  energy  demand.  In  case  of  HRM  the 
profile  for  each  type  of  products  is  modelled  with  a  good  precision,  because  there  is  not  much 
variance  between  different  products  of  the  same  type  but  the  energy  profile  itself  has  a 
characteristic course. 

Task 3.4 
events 

Methods  and  control-loops  to  adjust  the  power  engagement  according  to 

Control  loop  methods  was  identified,  implemented  and  tested  aiming  at  modifying  the  loads  in 
order to adjust the power engagement according to the request. In particular the focus is identified 
in  the  reduction  of  power  load  in  EAF  process  at  the  industrial  partners  plant  where  EAFs  are 
present. 

The  aims  of  controlling the  melting  power  engagement  of  the  EAFs  is  to  reduce  power  peak  load 
within the 15 min time interval reducing consequently costs for power supply. This can be achieved 
by: 

-  controlling the maximum load, avoiding to exceed the power peak load limit within the 15 

min time interval 

-  shifting the load to the next time slot, e.g. by delaying the start of a furnace, in order to 

avoid exceeding the peak load 

while: 

-  avoiding process interruptions as much as possible, to keep the productivity high  
-  realizing an energy demand profile as regular as possible 

Task 3.5 
electricity consumption flexibility 

Scenario  analysis  to  define  possible  improvements    aimed  at  increasing 

An  analysis  of  the  flexible  disconnectable  loads  in  steel  plants  has  been  carried  out  taking  into 
consideration the specificity of the industrial partners, but aiming to have general remarks for the 
steel companies participation in energy markets. 

In general the output of a steel plant must not be affected by flexible loads at all. Only a few plants 
or machines of a steel mill have a reserve capacity and may be used as a flexible load. This has to 
be  considered  in  detail  at  the  relevant  plant.  The  following  flexible  loads  were  identified  and 
analysed: 

  Electric Arc Furnaces 

EAF is the main power consumers of an EAF cycle steel plant and main responsible for the 
energy fluctuations. In principle the load of an EAF is adjustable depending on the phase of 
the  process,  however  implication  on  the  productivity  should  be  considered  and  managed. 
EAF can participate in decreasing the overall plant energy demand  giving also contribution 
in the limitation of the peak load. When more than one is present in a plant is fundamental 
to manage them accordingly. In Italy EAF participate in Interruptible Load market. 

  pressurized air production 

Due to the fact that the use of pressurized air is not predetermined and follows a more or 
less stochastic flow this grid system cannot really be used as a disconnectable load. 

  emergency power supply 

These  emergency  power  supply  units  may  be  used  as  an  aggregate  to  generate  positive 
control energy on demand to support the grid. 

  oxygen production 

The possibility to participate in electricity market depends on the type of oxygen prodution. 
Cryogenic  processes  need  hours  to  restart  and  therefore  cannot  be  used,  while  non-
cryogenic type are in general suitable to participate in energy markets. 

  hall lighting, air conditioning including fan, cooling towers 

8 

 
These  loads  can  be  used  as  flexible  loads  and  are  tested  in  the  project  mainly  for  the 
purpose of increasing the energy demand. 

WP 4  Development of online devices to react on electric grid events 

The objective of the work package 4 is to improve the power load flexibility and control capabilities 
through  the  implementation  of  suitable  aggregates  to  increase  (on  time)  instantaneous  electric 
energy demand in order to react on grid signals.  

Task 4.1 

Determination of most suitable process stages for additional electric loads 

The  overall  aim  of  the  task  4.1  is  to  find  suitable  aggregates  for  enhancing  the  energy  demand 
flexibility at HHO by analysing and eventually revamping suitable aggregates. The gained flexibility 
can be commercialized in the electricity market.  

Several  potentially  interesting  aggregates  have  been  inspected  and  were  taken  into  closer 
consideration  and  analysis  considering  the  potential  electricity  consumption  and  the  difficulty  for 
the realization. 

Task 4.2 
stage and estimation of economic benefits 

Feasibility study of additional electrical load for the most promising process 

The focus of Task 4.2 is on the feasibility study  of the most promising process stages to enhance 
electrical  load  and  estimation  of  economic  benefits.  An  economical  investigation  of  revenues  and 
necessary  investments  costs  has  been  carried  out  based  on  negative  secondary  control  market 
analysis and offers from suppliers for new aggregates construction.  

The  unpredictable  decline  in  revenues  for  participants  in  the  electricity  market  leads  to  the  final 
choice,  among  the  ones  foreseen  in  the  proposal,  to  extend  already  existing  aggregates  with 
additional automation possibilities.  

The  solution  foreseen  is  furthermore  easier  to  apply  to  other  plants  in  Europe  due  to  lower 
investments while assuring the possibility to actively participate in the balancing market through an 
intermediary and increase efficiency of the energy consumption inside the plant. 

Task 4.3 

Development, construction and implementation of additional electric loads 

The connections of the aggregates and the management computers have been installed in order to 
enable the participation of HHO on the balancing markets. One virtual machine has been installed 
and put in operation for this  purpose at HHO. Furthermore the data about the chosen aggregates 
have been collected for the use in the de development of the additional loads manager software. 

Task 4.4 

Modelling of electric load availability 

The modelling of the additional loads was essentially based on the load constraints evaluation and 
the cost function. The cost function must take into account all the load constraints and evaluate the 
optimal  decision  for  the  enabling  or  disabling  of  each  additional  load.  Furthermore,  due  to  the 
different  characteristics  of  each  load,  the  agent  based  modelling  lead  to  optimal  use  of  each 
aggregate.  

Task 4.5 

Development of control concepts 

The agent based control has been individuated as a suitable technology to manage all the involved 
aggregates and efficiently ensure the availability of the requested load. 

For  this  purpose  two  types  of  agent  were  designed.  The  load  agent,  which  task  is  the  internal 
management  of  each  aggregate  constraints,  the  evaluation  of  the  cost  function,  and  the 
communication to Manager Agent of its load availability.  

The  manager  agent,  which  task  is  to  manage  the  overall  loads  availabilities  integrating  the 
information  received  from  each  aggregate  agent  and  from  energy  demand  (Power  demand, 
availability  demand,  prediction  period)  in  order  to  ensure  the  load  availability  according  to  the 
energy demand.  

Moreover,  the  manager  agent  has  then  the  duty  to  calculate  the  new  optimal  scheduling  for  the 
production  planning.  The  production  scheduling  closes  the  control  loop  by  giving  the  new  15  min 
forecast. 

Task 4.6 
electrical loads 

Implementation  and  evaluation  of  developed  concept  for  additional 

For  the  implementation  of  the  developed  control  concept  at  HHO  two  aggregates  have  been 
considered.  The  first  was  the  cooling  towers  with  a  capacity  of  800  kW  (cooling  towers,  Pumps, 
fans). The second was the air conditioning switch House Miba with the capacity of 160 kW. 

9 

 
For  security  and  reliability  reasons,  the  additional  load  management  software  was  installed  a  few 
months before the commissioning. The purpose was to collect the data for the offline examinations 
in order to improve the software robustness.  

Once the software was deemed robust and reliable, the software was commissioned and evaluated 
online at HHO with the two aggregates during the production.  

WP 5   Strategies  for  production  redefinition  to  align  and  re-align  actual  power 
engagement with the forecast 

Task 5.1 
companies to calculate and re-schedule a “day-ahead”-forecast of the production  

Analysis  of  the  actual  procedures  and  methodologies  used  in  the  steel 

The forecast of needed energy is provided to the intermediator on an hour basis in the day-ahead. 
The actual procedures used in the industrial partners site was analysed. In most of the steel plant 
the  day-ahead  profile  was  calculated  based on  shift  (i.e.  each  day  was divided  in the  three  shift) 
using Excel files.  

Task 5.2 
booked and consumed electrical energy 

Definition  of  metrics  to  measure  and  estimate  the  deviations  between 

Several  KPI  have  been  proposed  in  order  to  quantify  both  the  deviations  between  the  forecasted 
(i.e. the energy bought at the day-ahead market) and actual consumption and the potential effects 
that  such  deviations  can  lead,  taking  into  account  the  different  regulations  and  opportunities 
provided by the Italian and German electricity markets. In particular the costs for the operation of 
the  distribution  grid  and  balancing  the  power  network  are  calculated  for  each  time  slot  (e.g.  1 
hour) retrospectively at the end of each month and depends not only on the plant but also on the 
neighbours behaviour. 

Task 5.3 

Implementation of KPIs to be used within a calculation algorithm  

The  proposed  KPIs  have  been  implemented.  Since  some  of  them  had  some  limits,  the  last  KPI 
composed  by  the  sum  of  two  terms  quantifying  the  deviation  between  booked  consumptions  and 
actual  consumptions  in  a  given  time  period,  and  taking  into  account  the  maximum  load  for  the 
actual slot has been considered as the most appropriate one, because it is able to express in terms 
of  euro  the  effectiveness  of  the  production  plan  and  also  it  is  suitable  to  be  used  for  the 
optimization of the production re-scheduling. 

Task 5.4 

Production Re-scheduling Criteria and relevant validation 

A model aimed to find an optimal production schedule of a part of a steel making process has been 
developed. The proposed model minimizes the selected KPI while satisfying production constraints. 
The developed model is based on mathematical programing with Mixed-Integer Linear Programing 
(MILP). 

Task 5.5 
validation 

Short  term  power  scheduling  on  line  model  development  and  relevant 

The proposed model is general enough to deal with extraordinary events, such as a machine failure 
during the daily process: in this case, to obtain a rescheduling, it is possible to restrict the inputs of 
the  model  to  the  remaining  products  (not  yet  processed),  taking  into  account  only  the  available 
machines  and  the  remaining  time  slots  of  the  time  production  range.  The  algorithm  is  run  again 
with this restricted input and outputs a new products schedule. 

WP 6  Dynamical access to the power market through an Agent System aimed to 
support the Decision Makers 

Task 6.1 

System requirements and automation landscape  

The aim of this task is to define the requirements originated from point of view of a steel producer. 
Starting  from  the  analysis  of  the  automation  systems  and  the  database  infrastructure,  different 
possible  application  scenarios  have  been  investigated  in  order  to  identify  broad  spectrum  of 
requirements  for  the  system  to  be  developed.  First  of  all  the  automation  landscapes  and  IT 
infrastructures which can be found at the plants has been analysed. Based on this information the 
requirements  for  the  systems  to  be  developed  have  been  defined.  This  includes  general 
requirements like “Integration”, “Extendibility” and “Customization” but also specific requirements. 
The specific requirements are divided into two parts  

• 

• 

functional requirements, describing the functional elements/parts of the solution 

non functional requirements, describing the criteria’s for system operation     

10 

 
Task 6.2 

System architecture and solution strategies 

The goal of this task is the development of the design for the agent software system, including the 
communication  infrastructure,  the  interfaces  and  structural  details  of  the  solution.  Additionally 
different  approaches  for  the  trading  strategies  are  investigated  and  solution  strategies  are 
developed.  

Initially the analysis of core  properties of  an agent approach has been investigated. This includes 
the  definition  of  major  characteristics  like  “Autonomy”,  “Local  view”,  “Decentralisation”  but  also 
identification of different agent types like “passive”, “active” and “cognitive” agents. Subsequently 
the scheme of structural components of agent systems is identified.  

In parallel an investigation of trading strategies is performed. Trading strategies utilise the idea of 
virtual  markets  in  order  to  perform  optimisation  tasks.  The  central  mechanism  of  virtual  market 
approaches  are  the  auctions.  An  analysis  of  different  auction  types  has  been  performed  and 
appropriate types has been identified.  

Finally  an  investigation  of  different  optimisation  tasks  referring  to  opportunities  of  flexible  energy 
management are done. First of all the influences and possible reactions has been identified. Based 
on this a schema for agent approach has been developed.      

Task 6.3 

Software development and implementation of strategies and algorithms 

This task is devoted to the implementation of agent system core components as well as adaptation 
of the algorithms to the different use cases. In general this task can be seen as integrative tasks 
which  combine  the  results  of  previous  work  packages  to  a  common  approach,  providing  generic 
algorithms, which then are adapted to corresponding application scenarios.  

Task 6.4 

Installation of the software tools into the plants infrastructure 

This task is devoted to the installation of the developed solutions at the infrastructure of industrial 
partners.  

Use cases has been implemented with the aim to verify the dynamical access to the markets and 
the plant reactions to both internal and external  events.  In particular the possibilities to  limit the 
15 minutes peak has been implemented on all the industrial partner site. These use cases mainly 
implies controlling the loads related to processes. AST and ORI have also focused on the limitation 
of the imbalance implying a better forecast for energy demand and possibility to access Intra Day 
markets.  LSW  and  HHO  tested  the  possibility  to  access  the  balancing  market,  enabling  additional 
loads at HHO and disabling loads at LSW. 

Task 6.5 

Test and validation phase 

Within this task the evaluation of the developed systems under practical conditions at the plant has 
been  performed.  The  specific  results  are  reported  based  on  the  use  cases  described  in  task  6.4. 
Limiting the 15 minutes peak lead to positive results in all the plants already achieved during the 
project  or  potentially  evaluated  based  on  industrial  tests.  Lowering  imbalance  also  shows  good 
initial  results  from  the  tests,  however  needs  to  be  further  improved.  Participation  in  balancing 
markets downwards was tested at LSW and founded potentially positive, however there were some 
problems that could be resolved in the future i.e.:  missing information on the end of an interrupt 
and  necessity  for  the  oxygen  plant  to  be  equipped  with  automatic  ramp  up  after  an  interrupt. 
Participation in balancing market downwards was tested by HHO, however according to the actual 
state of balancing markets was considered unattractive, until the markets conditions will change. 

Task 6.6 

Adaptation of the system 

This  task  is  devoted  to  the  adaptation  of  the  systems  in  order  to  eliminate  the  malfunctions  and 
faults observed during the test and validation phase. Accordingly, the operation of the systems has 
been  monitored  both  in  term  of  functionalities  and  problems  encountered.  In  cases  when 
malfunction  has  been  observed  the  analysis  of  reasons  and  effects  has  been  performed  and 
solution for the elimination of the errors has been developed. 

11 

 
 
2  Scientific and technical description of the results 

2.1  Objectives of the project 

According  to  several  sources,  as  the  Smart  Grid  Initiative  or  International  Energy  Agency,  the  energy 
demand side management  is considered one of the strategic key  for  a reliable  management of systems 
where variable renewable energy is present  

The rise of variable renewable energy sources increases power fluctuations in the power network due to 
daily  and  seasonal  effects  which  have  sensible  impacts  on  pricing  and  tariffs.  This  context  open  the 
possibility  for  steel  sector  to  implement  demand  side  flexibility  through  “smart”  demand-response 
management. Within the steel industry, especially the electric steelmaking plants have an extremely high 
potential  that  needs  the  implementation  of  active  actions  devoted  to  the  intelligent  access  to  the  grid, 
introducing technologies and procedures for flexible load control. The main objective of the project is to 
develop such approaches able to help steel plants in reducing electricity costs.  

The reach this general goal, a clear picture of the electricity markets, with a special focus in Germany and 
in Italy where the partners operate, has been taken with the aim to find out the opportunities to enhance 
the participation of steel industries on the markets in order to save money when buying electricity. 

On the other side  a clear picture  of available data,  with reference  to the  process route  of the industrial 
partners  plants,  was  also  taken.  The  analysis  of  data  related  to  events  and  the  relevant  data  model 
derivation  is  necessary  to  identify  the  specific  improvements  necessary  in  fundamental  areas  not  yet 
served by proper measurements. 

Based on this situation the following objectives have been identified: 

production schedule with focus on energy cost 

 
  minimising  the  imbalance  (deviations  between  booked  and  actual  power)  through  a  better 

forecast of energy needs and production adjustment  

  minimising the peak load 
  manage  reaction  on  electric  grid  events  both  ascending  (increment  power  demand)  and 

descending (lower power demand)  

This goals have been reached through: 

 

 
 

definition  of  ways  and  models  to  forecast,  measure  deviations  and  then  adjust  power 
engagement  
the adoption of an agent system  
test  of  the  developed  system  on  several  use  cases  in  four  steel  plants  with  different  production 
type and system requirement. 

2.2  Description of activities and discussion 

2.2.1  Work  package  1:  Power  market  characterization:  tariffs,  negotiation, 

fluctuations, events 

The  aim  of  this  work  package  is  the  determination  of  the  power  market  and  network  characteristics 
influencing monitoring, forecasting and management of the power engagement in the steel plants. 

2.2.1.1  Task 1.1: Electricity market characterization and supply profile determination  
A comprehensive analysis and evaluation of the electricity market and of the opportunities to take profit 
from  demand  flexibility  in  electricity  purchases  by  energy  intensive  industries,  both  in  Italy  and  in 
Germany,  has  been  carried  out.  The  results  are  documented  in  deliverable  D1.1,  that  can  be  found  in 
Annex I; hereafter a synthesis of the work undertaken. 

In particular, after a general description of the electricity market structure in the two countries, the study 
focused on the two main ways to purchase electricity by large industrial consumers, i.e.: 

 
 

through the Power Exchange; 
through contracts with a supplier. 

12 

 
2 electric energy purchases through the Power Exchange can be carried out in the  day-ahead 
Short-term1F
spot  market,  that  is  typically  the  most  important  one,  in  terms  of  volumes  of  energy  traded  and  of 
significance of the clearing prices, or in the intra-day spot market, where adjustments to the day-ahead 
market  schedule  can  be  done,  in  order  to  tackle  with  changes  in  the  situation  that  occurred  after  the 
closure  of  the  day-ahead  market  session  and  to  avoid  imbalances.  For  these  reasons,  the  intra-day 
market typically deals with volumes much lower than the day-ahead one. 

Within this context, a detailed analysis of hourly price profiles in the period between December 2013 and 
November  2014  has  been  carried  out  for  the  day-ahead  spot  markets  of  the  Italian  Power  Exchange 
(IPEX) and of the German one (EPEX Spot). Several charts have been included in the deliverable, such as 
price hourly values, monthly average, hourly average (annual and for each month), for all the days of the 
year and for working-days and non-working days only. 

The charts reported in Figure 2 and in Figure 3, showing the Italian and German day-ahead spot market 
price hourly average difference w.r.t. the overall annual average of the same type of day in working days 
and  in  weekends  and  holidays,  are  the  most  significant  ones.  They  clearly  show  how  cheaper  or  more 
expensive can be to consume energy in each hour of each different type of day. 

Price differences w.r.t. the average can be significant, ranging from about -18 ÷ +23 €/MWh in Italy and 
from about -15 ÷ +12 €/MWh in Germany, showing that there is a great opportunity to take profit from 
demand flexibility in purchasing energy from the day-ahead spot market. 

As  for  the  intra-day  spot  market,  the  Italian  one  is  characterized  by  price  profiles  similar  to  the  day-
ahead profiles, while in the German one, that is a continuous auction, price profiles can be different from 
the day-ahead ones. 

Instead of directly on the Power Exchange, energy can also be bought through a supplier, at an agreed 
price  defined  according  to  specific  consumption  profiles,  or  paying  the  energy  at  the  day-ahead  spot 
market  price.  In  such  a  case,  the  foreseen  consumption  profile  may  have  to  be  communicated  to  the 
supplier two  days  before  the  day  of  delivery,  to  allow  the  supplier itself  to  buy  the  energy  on  the  day-
ahead spot market, one day before the delivery. 

In any case, contracts with a supplier are in general less flexible than purchases on the Power Exchange. 

In case of deviations of consumption from the program agreed with the supplier or deriving from Power 
Exchange  purchases,  imbalance  energy  must  be  settled  at  specific  imbalance  prices.  In  the  Italian 
regulation,  prices  are  penalizing  or  profitable  according  to  the  concordance  or  discordance  of  the 
imbalance sign with the sign of the overall imbalance of the zone where the consumer is located, while in 
Germany imbalance energy is settled, for each quarter of an hour, at the price obtained by dividing the 
control energy costs sustained by the TSO in the balancing market in a specific quarter of an hour, by the 
balance of the deployed amount of control energy in that same time interval. 

Starting from August 2016, the Italian Authority for Electric Energy, Gas and Hydric System changed the 
imbalance regulation. 

As  far  as  electric  energy  consumers  are  concerned,  before  that  change  imbalance  prices  were  defined 
according to a “single pricing” mechanism, shown in Table 1. 

Table 1 - Single pricing mechanism for imbalances. 

Sign of control 
area 
imbalance 

Imbalance price 

+ 

- 

Minimum 
between: 

Maximum 
between: 

- 
- 

- 
- 

day-ahead market price  
average price of downward bids accepted in the balancing 
market 
day-ahead market price  
average  price  of  upward  bids  accepted  in  the  balancing 
market 

2 We focus on short-term (day-ahead / hours-ahead) since it is the typical time horizon over which demand flexibility can more 
effectively be exploited. For this reason, we did not consider longer-term forward and future contracts in the analysis. 

13 

 
 
                                                
 
The new regulation foresees that the “single pricing” mechanism applies only to the imbalances within a 
threshold corresponding to ±15% of the withdrawal program. The imbalances outside that threshold are 
priced according to a “dual pricing” mechanism, shown in Table 2. 

Table 2: Dual pricing mechanism for imbalances. 

Sign of 
control 
area 
imbalance 

of 

Sign 
unit 
imbalance 

+ 

+ 
- 

- 

+ 

- 
+ 

- 

Imbalance price 

Minimum between: 

Day ahead price 
Day ahead price 
Maximum between: 

- 
- 

- 
- 

day-ahead market price  
average  price  of  downward  bids  accepted  in 
the balancing market 

day-ahead market price  
average price of upward bids accepted in the 
balancing market 

With the “single pricing” mechanism, if the sign of the imbalance of the consumption unit is the opposite 
of the sign of control area imbalance, the imbalance is profitable for the consumer, since the imbalance 
energy is bought/sold at a price typically lower/higher than the day-ahead market price. 

On  the  contrary,  with  the  “dual  pricing”  mechanism  the  imbalance  can  never  be  profitable:  in  the  best 
case, the imbalance energy is valorized at the day-ahead market price with neither profit nor loss, while 
in  the  worst  case  the  imbalance  energy  is  bought/sold  at  a  price  typically  higher/lower  than  the  day-
ahead  market  price.  This  aims  at  penalizing  large  imbalances,  outside  the  ±15%  threshold.  This  rules 
variation  implies  for  the  Italian  steel  plants  that  being  in  line  with  the  forecasted  energy  demand  is 
becoming more important from an economical point of view. 

Figure 2: Italian day-ahead spot market price hourly average difference w.r.t. the overall annual 
average of the same type of day in working days and in weekends and holidays (source: RSE 
elaboration on GME data). 

14 

 
 
 
 
Apart  from  reducing  the  cost  of  energy  purchases  and  of  possible  imbalances,  demand  flexibility  can 
contribute  to  reduce  peak  power  consumption  and  the  related  fees.  In  Italy,  the  fee  applied  to  the 
monthly peak power consumption measured in any quarter of an hour of each month is currently equal to 
1.528 €/kW-month, while in Germany the monthly fee ranges from 6 to 9.38 €/kW-month, or an annual 
fee can be paid, ranging from 35.99 to 60.79 €/kW-year.  

Figure 3: German day-ahead spot market price hourly average difference w.r.t. the overall annual 
average of the same type of day in working days and in weekends and holidays (source: RSE 
elaboration on EPEX Spot data). 

2.2.1.2  Task 1.2: Characterization of grid events (slots, power fluctuations etc.) with their 

dynamical characteristics coming from the power network  

The activities in this task aim at the categorization of events due to the grid and the grid status; this is 
necessary to determine the dynamical response to be activated by the steel industry in terms of typical 
time  shifts  between  event  and  reaction.  Moreover,  the  economic  value  of  the  ancillary  services  that,  in 
principle, could be provided by a flexible electricity demand in the steel industry, is assessed. 

Information about the technical regulation of ancillary services in Italy and in Germany, with a particular 
focus on balancing services, such  as primary,  secondary and tertiary regulation, as well as interruptible 
loads has been collected. 

According to the required technical performances, tertiary regulation and interruptible loads seem the two 
services  most  suited  for  the  provision  by  large  industrial  loads,  even  if  in  Germany  also  secondary 
regulation can be provided by flexible loads. 

In  Italy, according to the  current  Grid  Code by the  Transmission System  Operator TERNA, loads  of any 
size are not enabled to provide ancillary services. Nevertheless, the Italian Regulatory Authority intends 
to remove soon this limitation, as stated in recent consultation documents, in line with the provisions of 
the European directive 2012/27/EU on energy efficiency. Moreover, the required technical performances 
of secondary regulation in Italy are more stringent: the whole band must be provided within 200 seconds 
and the holding period is at least 2 hours. Italy start some pilot projects for MSD in 2017. 

The analysed markets are IL (Interruptible Load) only upward, SCR (Secondary Control Reserve) and TCR 
(Tertiary Control Reserve). SCR and TCR was already opened markets in Germany at the beginning of the 
project  while  in  Italy  in  2017  some  pilot  projects  has  been  activated  where  consumers  with  power  less 
than 10MW can participate in these markets being part of a pool with the lower limitation of 10MW on the 
whole  aggregate.  In  Germany  the  companies  can  participate  solitarily  or  as  part  of  a  pool  both 

15 

 
 
downward-negative  (cut-in  of  aggregates,  increase  electricity  use)  and  upward-positive  (shutdown  of 
aggregates, decrease electricity use) ways. The bidding processes are both for capacity and energy and 
are payed as bid.  

Table 3: Reaction time 

Italy 

Germany 

IL/AblaV  < 200 ms Instantaneous 

< 5 s Emergency 

< 1 s Immediately  
< 15 min Quickly 

SCR/SRL  < 200 s 

TCR/MRL  < 15 min 

< 300 min 

< 15 min 

Table 4: Load / generation alteration  

Italy 

Germany 

IL 

1MW 

50-200 MW  

SCR/SRL  ± 10 MW  ±5MW  

TCR/MRL  ± 10 MW  ±5MW  

Table 5: Holding period 

Italy 

Germany 

IL/AblaV 

No limits  1-2-4 h 

SCR/SRL  > 2 h 

< 15 min 

TCR/MRL  No limits  < 4 h 

In Figure 4 more details for German markets are reported. 

Figure 4: Technical characteristics of secondary (SRL) and tertiary (MRL) regulation and of the 
interruption service (AblaV) provided by loads in Germany. 

16 

 
 
 
 
 
The fundamental characteristic to be taken into consideration in order to identify how the steel companies 
can  participate  in  those  markets  are  reaction  time,  Load  /  generation  alteration  and  Holding  period.  In 
Table 3, Table 4, Table 5 the comparisons between these factors in Italy and Germany are shown.  

Moreover,  information  on  the  Italian  and  German  ancillary  services  and  balancing  markets  and  on  the 
characteristics of the products traded on such markets has been collected. 

Figure 5: Hourly average upward balancing price profiles in different Italian market zones in the year 
between November 2013 and October 2014. 

Figure 6: Hourly average downward balancing price profiles in different Italian market zones in the 
year between November 2013 and October 2014. 

17 

 
 
 
As  far  as  the  Italian  ancillary  services  market  is  concerned,  the  bids  submitted  in  the  year  between 
3 have been collected and structured into a data base, in order to carry 
November 2013 and October 2014 2F
out an analysis of price profiles. 

A  first  analysis  has  been  carried  out  on  the  hourly  average  price  profiles  of  upward  and  downward 
balancing in different market zones, i.e. the same prices used to settle load imbalances, shown in Figure 
5 and in Figure 6. 

Figure 7: Development of averaged capacity price of accepted bids for TCR (data base 
www.regelleistung.net). 

As for upward balancing, prices are the lowest in the North zone and are the highest in Sicily, except for 
hours between 18 and midnight, where the Rest of Italy shows the highest prices. 

As for downward balancing, prices are the lowest in the Rest of Italy and are the highest in Sardinia. 

As  for  the  German  ancillary  services  market,  the  volumes  of  secondary  and  tertiary  regulation  have 
significantly  changed  along  the  last  years  due  to  changing  behavior  of  network  users  as  well  as  on 
structural changes of load-frequency control management. 

Also capacity prices  have  undergone  significant variations, as shown in  Figure  7. Nevertheless,  it  is not 
feasible  to  apply  these  average  figures  to  single  use  cases  directly,  since  the  capacity  price  strongly 
varies according to offered boundary conditions of the industrial plant. These requirements comprise: 

•  Time response for providing balancing power (positive/negative) 
•  Amount of balancing power offered in total 
•  Possible duration of activation time 
•  Allowable amount of possible activations (per week/month/year) 
•  Robustness during possible activation (rejection rate, acceptance rate) 

Situation in Other European Country  

A comparison among several European country with reference to the participation of the demand in the 
balancing markets and the presence of negative prices has been carried out and is shown in Table 6. 

3 Same period considered for the analysis of day-ahead spot market price carried out in Task 1.1. 

18 

 
 
                                                
 
Table 6: Comparison among European countries for demand participation and negative prices 

Country 

Demand participation 

Belgium: 

Spain: 

Austria: 

Denmark, 
Finland, 
Island, 
Norway, 
Sweden 
(Nordel 
countries): 

demand can participate to strategic reserve (for 
structural problems during winter time). Specifically 
demand connected to transmission can participate in 
Frequency containment reserves and in Frequency 
restoration reserves; while demand connected to 
distribution can participate in Frequency restoration 
reserves 

demand can be reduced to 5MW to 90MW blocks in three 
ways: 
  Immediate 
  Quick: notified 15 minutes before 
  Hourly: notified 2 hours before 
Maximum holding period is 1 hour. 5MW products can be 
activated not more than 40 times weekly and 240 hour 
yearly, while 90 MW products not more than 60 hours 
weekly and 360 hours yearly 
Demand can participate both in SCR and TCR 

Frequency restoration reserves regulation in Nordic 
countries doesn’t make difference between demand and 
generation when the technical requirements are fulfilled 
(full activation within 15 minutes and minimum of 10 MW 
hourly). Energy Intensive Industries (e.g. papers,  
metals) actively participate in these markets 

Negative prices 

available (both in day-
ahead and services 
markets) 

not available 

available (both in day-
ahead and in services 
markets); downward 
prices have been always 
negative in last years 

not available 

2.2.1.3  Task 1.3: Elicitation and definition of the negotiation rules and protocols between steel 

manufacturing demand and grid offer  

The following paragraph represents the deliverable D1.3. 

After  the  analysis  performed  in  the  first  two  tasks  different  opportunities  to  participate  in  the  energy 
market have been found that are shown in the overview given in Figure 1. 

The  first  opportunity  (day-ahead  &  Intra-day)  is  based  on  the  possibility  to  take  advantage  from  the 
oscillation of the energy price during the day and between working days and weekends / holidays. It can 
be noticed that the hourly price profile tends to be similar during different days therefore the production 
can be  scheduled  accessing the day ahead  market privileging  the utilization  of energy during  time  slots 
that have lower cost. This means not only money savings but also less environmental impact due to the 
fact  that  when  the  price  is  lower  electricity  is  normally  generated  by  less  expensive  and  less  polluting 
(possibly renewable) production sources. 

The second opportunity (Secondary and Tertiary Control Reserve SCR-TCR) is related to the possibility to 
dynamically react to grid events providing ancillary (typically balancing) services for the secure operation 
of  the  power  system.  This  means  both:  decreasing  or  increasing  the  energy  utilization.  In  Germany 
consumers are able to participate at the balancing market, both as a single user or as a consortium, while 
in Italy the regulatory authority is working on the possibility of enabling also the consumers to participate 
(up to now it is only possible for dispatchable generators with an installed power greater 10 MW). Primary 
Control  Reserve  are  mainly  participated  by  power  generators,  besides  them,  some  aggregators  of  both 
generators and loads, begin to participate in the market; moreover several researches on how demand-
side units can participate are still ongoing trying to identify how a very fast and precise load regulation, 
necessary  to  follow  electricity  frequency  variation,  could  be  achieved.  Therefore  this  market  has  been 
considered out of the scope of DYNERGYSteel.  

19 

 
 
A  report  from  USA  asserts  that  the  grid  offer  can  be  divided  into  two  main  parts:  The  incentive-based 
4.  The  first  one  is  mainly  used  to  motivate  changes  in 
offers  and  time-variable  electricity  prices  tariffs 3F
electric use by end-use customers and therefore it’s not applicable to our case. The possible applicability 
to steel industries of the second one is more described in detail within deliverables 1.1 and 1.2.  

The interruptible loads (AbLa-IL) are the third opportunity. This situation is more related to grid security, 
i.e.  very  critical  situation,  therefore  the  reaction  time  should  be  faster  compared  to  the  second 
opportunity.  

Moreover, in Germany the technical requirements are more strictly formulated. The actual law ended in 
5  but  meanwhile  it  has  prolongated  until  01.  July  2016.  The  political  and  industrial 
December  2015 4F
partners are actually discussing about future modifications of this law in order to expand the participation 
of interruptible loads. 

In  Italy is paid only based on power (MW)  offered by  the plant and not on energy (MWh). Since  in our 
experience the plant offers for interruption a percentage of the total power capacity, it is very important 
to  control  the  overall  energy  utilization  in  the  plant  during  the  disconnection  time  in  order  to  avoid  an 
increment of non-interruptible loads that could partially offset the interrupted amount. 

The elicitation of contracts and compensation details are very different for each industrial partner.  

Still, the main important criteria for negotiation rules in this case have been elaborated5F

6: 

•  Load-change:  

How high is the load which can be provided for switching on/off 

•  Duration of preannouncement:  

How long in advance has to be an official request for the load change to be announced 

•  Reaction time:  

How fast can the load be switched on/off 

•  Holding period: 

How long can a load-change be maintained 

•  Compensation: 

Amount  of  compensation  which  can  be  offered  by  various  possible  programs  described  in 
deliverable 1.1 

It  has  been  highlighted  by  the  Italian  partner  that  it’s  very  difficult  to  make  a  good  prediction  of  the 
electricity  necessary  for the  production even  the  day  before.  The imbalance  with  respect to  the booked 
energy can lead both to fees or revenues based on the accordance or discordance with the surrounding 
zone.  This  means  that  there  is  a  correlated  risk.  The  risk  appetite  lead  to  different  approaches  to  the 
electricity  market  and  in  particular  we  found  that  accessing  the  market  through  an  intermediator  is 
preferred according to the following behaviors: 

 

 

 

the  intermediator  can  takes  the  risks  on  its  how  increasing  the  electricity  cost  paid  by  the 
plant by a fixed fee, but avoiding penalties when imbalances occur 
the  smaller  plants  can  create  a  consortium  with  the  surrounding  plants  in  order  to  improve 
the bargaining power 
the plant will take the risks leading to a lower medium price enhancing the possibilities to get 
paid when imbalance but also to pay fees. 

LSW has studied the possibilities to sell the flexibility of disconnectable loads. This is only possible if the 
disconnectable loads are aggregated by a service provider to more than 50 MW in total. Therefore LSW 
has organized a workshop with the company ENERNOC that offers this service for industrial power clients. 
The aggregator organizes the cooperation of the clients with the power supplier and the grid operators in 
order to summarize the flexibility for a start or stop of disconnectable loads.  

4 DOE 2006 Department of Energy: Benefits of demand response in electricity markets and recommendations for 
achieving them – A report to the United States Congress pursuant to section 1252 of the energy policy act of 2005. 
Washington: U.S. Department of Energy 2006 
5 Decree of the Energy Industry Act (EnWG): „Ordinance on Interruptible Load Agreements (AbLaV)”, § 13 Abs. 4a 
EnWG, 20.12.2012 
6 FOREnergy: “Ergebnispapier – Flexibilisierung der Energienachfrage von industriellen Verbrauchern”, Ergebnisse des 
Arbeitskreises 1 “wirtschaftliche Synchronisation von Energieangebot und –nachfrage” des Forschungsverbundes 
FOREnergy, January 2015  

20 

 
                                                
 
Figure  8  shows  the  principles  and  rules  of  a  cooperation  between  an  aggregator  and  the  power  clients 
that want to sell their flexibility. The aggregator cares about all agreements with power supplier and the 
operators  of  the  transmission  and  distribution  network.  He  also  supports  the  client  within  the 
prequalification that is required. 

Figure 8: Principles of a cooperation between an aggregator and a power client (ENERNOC) 

Within  this  cooperation  different  tasks  have  to  be  fulfilled  by  the  client  and  the  demand  response 
aggregator that are listed below. 

Tasks of the power client: to sell the flexibility of the production 

Prequalification phase 
  Gives the aggregator all allowances to contact the grid operators 
 
  Supports the prequalification tests 

Implements the interface for plant control 

Commercialization phase 
  Signals the availability of flexibility 
  Cares about the reliability of the plant 
 

answers on automatic requests 

Tasks of the demand response aggregator: to operate as a service company with the grid 

Identifies flexibility 

Prequalification phase 
 
  Cares for approvals of all stakeholders of the energy net 
 
  Configures the pool of participants 
Performs the prequalification tests 
 

delivers interface for communication 

Commercialization phase 
  Operates the pool-platform (24/7) 
  Configures the product and sells the flexibility 
 
  Cares for unplanned non availability of flexibility 
  Cares about all activities with grid operators 

Forwards the requests for disconnections to the participants 

2.2.2  Work  package  2:  Power  demand  by  the  steel  plants:  monitoring  the  actual 

engaged power and electric energy consumptions 

The aim of this Work Package is the collection of available data with reference to the process route of the 
industrial partners plants, the analysis of data related to events and the relevant data model  derivation, 
the implementation of the online-monitoring system, using existing data and the additional measurement 
devices installed.  

21 

 
 
2.2.2.1  Task 2.1: Manufacturing routes and layout analysis  
The four plants of the industrial partners considered in this project can be regarded to provide a view on 
different  types  of  steelmaking  plants.  In  fact  one  stainless  steelmaking  plant  for  flat  products  with  the 
complete  production  lines  from  EAF  melting  to  finishing  (AST),  one  plant  for  low  and  medium  alloyed 
construction  and  engineering  long  products  (LSW),  one  plant  for  special  steel  long  products  for 
automotive and mechanical industry (ORI) and one plant for hot rolled narrow strip production mainly for 
cold rollers (HHO)  are  covered.  Hereafter description  of the plant layout and analysis of  data related to 
energy utilization are given. From this analysis EAF and LF have been individuated as the most relevant 
processes to be taken into consideration  for LSW  and ORI,  while at AST EAF  and HRM  will  be  the  main 
objective of Electricity Flow Sheet Model (EFSM). HHO’s work is devoted to the development of additional 
load flexibilities in form of additional electricity usage by reacting on grid demands and the definition of 
the relevant processes taken into consideration is done in WP4.  

Being  the  analysis  performed  in  this  Task  intrinsically  needed  for  each  industrial  partner  the  following 
chapters are  also organised in  this way.  For each  of them  a description of the situation on the relevant 
plant is reported. 

Manufacturing route and relevant data at Acciai Speciali Terni 

Acciai  Speciali  Terni is  the  Italian  leading  company  for  stainless  steel  and  forging  production.  The  plant 
produces  1,2  Million  tons  of  hot  products  and  650,000  tons  of  cold  products.  Moreover  it  is  one  of  the 
world’s leading producers of stainless steel and is known, thanks to its experience of over 100 years, as a 
global leader because of its size and modern and sophisticated machinery, for its innovation in technology 
and production processes. 

Figure 9: Hot Area Layout at AST 

The  Hot  Area  is  equipped  with  one  150t  EAF  and  one  165t  EAF,  followed  by  secondary  metallurgy 
composed by three LF (Ladle Furnace), two AOD (Argon oxygen decarburization) and one VOD (Vacuum 
oxygen  decarburization);  the  liquid  steel  can  then  be  solidified  through  two  CCM  (Continuous  Casting 
Machine)  or  used  for  forging  products.  The  Hot  Rolling  Mill  (HRM)  is  composed  by  1  Walking  Beam 
furnace and 1 HSM (Hot Strip Mill) (see Figure 9). 

22 

 
 
Figure 10: Typical Electrical Energy utilization at AST 

The hot coils can  then be processed through three annealing and pickling lines for hot-rolled strips and 
six Sendzimir mills. The Finishing Lines are composed by two bright annealing lines for cold-rolled strips, 
two annealing and pickling lines for cold-rolled strips, two Skin-pass rolling mills, and one stretching line.  

The  products  can  be  further  be  processed  in  a  Finishing  Centre  composed  by  slitting  and  cutting  to 
measure, Scotch Brite or Butter Finish and a coating line for VIVINOX. 

In  Figure  10  a typical Electrical Energy profile  utilization is shown. As can be seen the two EAFs  (green 
and  blue  bars)  and  the  HSM  (orange  bar)  are  responsible  for  about  50%  of  the  total  electrical  energy 
consumption.  This  percentage  depends  more  on  the  EAF  consumption  itself  than  on  the  other  plant 
necessity  in  fact,  from  a  first  analysis  of  the  available  data,  the  rest  of  the  plants  have  a  more  or  less 
constant utilization of electrical energy. For this reason the main effort will be focused on the two EAFs, 
being responsible for the fluctuation of the electrical energy demand.  

For  these  plants  not  only  the  data  related  to  energy  management  but  also  all  the  necessary  data 
necessary  to  model  the  process  and  to  predict  the  electrical  energy  demand  will  be  taken  into 
consideration. 

Manufacturing route and relevant data at ORI Martin 

ORI Martin is specialized in high quality long products steel production, the ORI Martin Brescia plant is a 
modern  EAF  steel  mill  for  production  of  continuous  casting  billets  and  hot  rolling  of  wire  rod,  bars  and 
bars  in  coils  (Figure  12)  for  special  applications  in  the  automotive  business  as:  fasteners,  spring 
suspension, torsion bars. It is also equipped with thermal treatments for annealing and Q&T (Figure 13). 
The production in 2013 was around 700.000 tons.  

The  Steel  Making  Area  is  equipped  with  a  Consteel®  EAF,  two  LF,  one  VD  (Figure  11)  followed  by  one 
continuous casting machines with 5 strands 140x140mm. 

One  hot  rolling  mill  (bars/bars  in  coils/wire  rod),  three  straightening  and  controls  lines,  twelve  bell 
annealing furnaces for wire rod, one continuous annealing furnace for wire, and one annealing furnace for 
bar and two Q&T treatment for bars complete the Brescia plant. 

23 

 
 
Figure 11: Steel Making Area at ORI Martin 

Figure 12: Rolling Area at ORI Martin 

Figure 13: Quenching & Annealing Area at ORI Martin 

A typical behavior of electricity use during the day is shown in Figure 14. 

24 

 
 
 
 
Figure 14: Example of electricity profile during a working day at ORIMartin 

In  Figure  15  the  distribution  of  energy  use  in  ORIMartin  Brescia  plant  is  shown.  More  than  60%  of  the 
energy is utilized by the EAF while 8% by the two LF, together with fume treatment this leads to around 
75% in steel making area. 

Figure 15: Distribution of the electrical energy used by the different processes in the ORIMartin plant 
in 2015 

Manufacturing route and relevant data at Lech-Stahlwerke LSW 

Lech-Stahlwerke  GmbH  (LSW)  is  a  leading  company  in  Germany  for  production  of  low  and  medium 
alloyed construction and engineering  steel. The yearly production of 1,1 Million tons of steel (800.000 t 
quality steel and 300.000 t structural steel) is achieved in the steel plant equipped with two UHP Electric 
Arc Furnaces  with a tap  weight of  100  t each. The secondary metallurgy treatment is performed in two 
Ladle Furnaces and two VD degassing plants, which are equipped with mechanical vacuum pumps using 
electrical energy. The liquid steel is casted with two 4 strand continuous casting plants. 

The billets are cooled down and then reheated in gas-fired reheating furnaces for rolling. However, within 
the  project  the  activities will  be  focused  only  on  the  steel  plant  with  their large  consumers  of  electrical 
energy, i.e. the two EAFs and the two LFs.  

The  steel  plant  consists  of  two  process  routes,  both  of  them  equipped  with  one  EAF,  one  LF  and  a  VD 
vacuum degassing plant, see Figure 16. 

25 

 
 
 
Figure 16: Production routes for structural (BST) and quality (QST) steel at LSW 

The  production  of  Line  1  focusses  on  structural  steel  along  EAF  1  and  LF  2,  and  on  quality  steel  with 
additional vacuum degassing treatment in the VD plant 2. In Line 2 mainly quality steel is produced along 
the process route EAF 3, LF 4 and VD 1. About 300.000 t of the total production of 1.1 Mt are structural 
steels.  

Figure 17 shows the process route for the production of structural steel in the grid for energy supply. 

Figure 17: Process route at LSW for production of structural steel with energy grid 

At the scrap yard of the LSW steel plant, ca. 30 different scrap types are stored in separate boxes. For 
the production of structural steels, the lower quality scrap types are used.  

For the yearly steel production of 1,1 Mt, about 1,2 Mt of  scrap and about 0,1 Mt of alloy materials and 
slag formers are used. The internal recycling rate of home return scrap and re-use of slag is about 96 %.  

26 

StirringstationLF 2                               EAF 1Continuouscastingplant 1 Pusher furnace1Rolling mill1: ConcretesteelPVSADistribution   Storage                                 CoolingbedThermexScrapyardSteel plant Rolling millLogisticsTrafo 13Trafo 14Trafo 17Trafo 15Dynamic VARcompensationTrafo 11Trafo 12Trafo 16Meltingcurrent20 kVOperating current3 kVOperating current20 kVArgon andNitrogen220 kV220 kV110 kV110 kVNatural gasO2Substation  
 
 
In the steel plant, primary steelmaking is performed by scrap melting in an AC Electric Arc Furnace (EAF 
1) with a capacity of 100 t and a nominal connecting power of 75 MVA. The electric power is distributed 
to three graphite electrodes. Furthermore natural gas burners with a total power of 12 MW are used for 
chemical energy input. The scrap is charged in 2 – 3 portions via scrap baskets, which needs 1-2 min. for 
each  charging  process.  The  tap-to-tap  time  is  lower  than  60  minutes.  For  each  charge  about  45  MWh 
electrical energy, 600 Nm³ natural gas and 3.300 Nm³ of oxygen (coming from an own  VPSA plant) are 
used.  The  average  tapping  temperature  is  1650  °C.  The  heat  is  tapped  into  the  ladle  via  an  eccentric 
bottom  tapping  system,  which  takes  about  10  –  15  minutes.  During  tapping,  first  alloy  materials  are 
added, and the heat is deoxidised. 

Secondary  steelmaking  for  the  structural  steel  grades  is  performed  in  a  ladle  furnace  and  a  stirring 
station. The ladle furnace has a nominal connection power of 24 MVA and an average active power of 10 
MW.  Secondary  metallurgy  treatment  comprises  desulphurisation,  deoxidation  and  adjustment  of  the 
chemical  composition  and  the  casting  temperature.  For  mixing  and  stirring  of  the  steel  melt  and 
acceleration of the chemical reactions, inert gas (Argon or Nitrogen provided externally by Linde) is used. 
The liquid steel is casted to billets in a 4 strand continuous casting plant. 

The  dedusting  plant  comprises  4  filter  systems  (2  direct  dedusting  systems  for  the  EAFs,  two  for  the 
furnace halls) with 6 x 800 kW fans.  

Figure 18 shows the process route for production of quality steel in more detail.  

Figure 18: Process route at LSW for production of quality steel 

The process route dedicated for the production of quality steel includes EAF 3 with a nominal connecting 
power  of  82  MVA,  a  ladle  furnace  LF  4  and  a  vacuum  degassing  (VD)  plant.  To  produce  steel  qualities 
with  highest  cleanness  demands,  the  steel  melt  is  treated  in  the  VD  plant  under  vacuum,  to  remove 
dissolved  gases  like  hydrogen  and  nitrogen.  Such  a  vacuum  degassing  plant  is  also  included  in  the 
process route Line 1. 

As  shown  in  Figure  19,  the  main  consumers  of  electrical  energy  in  the  plant  of  LSW  are  located  in  the 
steel plant. Together with the auxiliary plants, the liquid steelmaking  aggregates in the steel plant cover 
93 % of the total electrical energy consumption. 73 % of this electrical energy consumption is covered by 
the  two  EAFs  as  the  by  far  largest  consumers  in  the  steel  plant.  They  are  followed  by  the  two  ladle 
furnaces with only 8% of the electrical energy consumption in the steel plant. 

For LSW, monitoring and management of the electrical energy demand will focus on the most important 
consumers, i.e. the two EAFs and  the two  LFs  in the steel plant. For this purpose, two kinds of process 
data groups will be acquired and evaluated:  

27 

ScrapyardSteel plantRolling millLogisticsContinuouscastingplant 2 2StirringstationVD plant 1Ladlefurnace4EAF 3Walking beam furnaceRolling mill2: Quality steelCoolingbed2      Coolingbed3Distribution              ManufacturingDistributionStraighteningmachineStorageAnnealingfurnaceBillet storageAdjustage/Testing 
 
Cyclic process data for dynamic, short-term monitoring and prediction (15 minute intervals) of electrical 
energy demand:  

Electrical energy input (EAF, LF) 

  Active power (EAF, LF) 
 
  Oxygen and natural gas flow via injectors and burners (EAF)  
 

Further  data  to  monitor  the  energy  demand  of  the  connected  auxiliary  aggregates  in  the  steel 
plant (Primary and secondary dedusting, PVSA plants, water treatment)  

Consumption figures for complete heats, to determine the quality dependent energy demand for medium-
term prediction (one production day):  

Electrical energy consumption (EAF, LF) 

  Steel quality and quality group 
  Scrap charge (weights of the different scrap types) incl. charge coal (EAF) 
 
  Natural gas consumption (EAF) 
  Oxygen consumption (EAF) 
 
 
 
 
 

Foam coal consumption (EAF) 
Lime consumption (EAF, LF) 
Tapping temperature (EAF) 
Tap-to-tap time (EAF) 
Treatment time (LF)  

Figure 19: Distribution of the electrical energy supply to the different consumers in the LSW plant 

Manufacturing route and relevant data at Hoesch Hohenlimburg HHO 

Hoesch Hohenlimburg GmbH (HHO) is specialized on hot rolled narrow strip production. The plant mainly 
consists  of  the  narrow  strip  mill  and  a  pickling-/length-  and  annealing  adjustage,  as  well  as  the 
appertaining auxiliary aggregates.  

The processing stages are: 

  Slab storage, 
  Walking beam furnace, 
  Reversing roughing mill, 
  Cropping shear, 
 

Intermediate stands, 

28 

 
 
 
Finishing mills, 

 
  Cooling section (laminar), 

The subsequent process stages, namely the pickling-/length-/annealing adjustage, are structured by the 
following subsequent main aggregates: 

  Continuous pickling line with stretch leveller, 
Push pickling line, 
 
 
Leveller machines with edge trimmer, 
  H2-high convection bell type furnace. 

Moreover, the auxiliary aggregates play  an important  role, especially regarding WP4  within this project. 
Important auxiliary aggregates for our purposes are e.g.: 

Feed water preheating, 
Temperature control of server rooms, 

  Compressed air network, 
 
 
  Hall heating, 
  Acid-/annealing preheating, 
  Steam generation, 
 
 
 
  Hall lighting, 
  Hot water preparation. 

Energy distribution station cooling, 
Enhanced cooling power of cooling tower, 
Increased flow rate into the cooling tower via pumps, 

2.2.2.2  Task 2.2: Definition of the Data Model for power engagement and electric energy 

consumption analysis and tune up of existing DBs   

This chapter represents the Deliverable D2.1. 

According to the analysis of the reference plants characteristics done in Task 2.1 the general Data Model 
has  been  defined  in  order  to  describe the  most  important  sources  of  power  engagement  and  electricity 
use, considering the role in the project. A description of the available process and energy data at each of 
the industrial partner plant is provided and depending on the specificity of each the eventually necessary 
enhancement are described.  

The energy Data are available at least on a 15 min base on all the involved plants globally and specifically 
for the processes object of the project (i.e. at least EAF for LSW, AST & ORI). The eventually necessary 
improvement  for energy measurements  are  detailed  below  for  each  industrial  partner.  No  improvement 
was foreseen for product/process data at any plant. 

Already available data bases at Acciai Speciali Terni 

A preliminary analysis of available data at Acciai Speciali Terni has been performed.  

Two main types of data need to be considered:  

 
 

Electrical energy utilization  
Product/process data to be used in Electricity Flow Sheet Model (EFSM) and forecast model 

Detailed information regarding Electric Energy on both EAF and HSM were available (Figure 20). The data 
are acquired directly from the PLC and then merged for historical storage. Therefore they are available at 
different timescale depending on the necessity and the time of the measure. Data from soft sensors able 
to  correlate  the  energy  demand  to  the  specific  product  is  available  and  will  be  enhanced  based  on  the 
requirement of the project. Moreover, because the steel making area as the higher request of electricity, 
new meters have been identified to be added in this area and in particular the two LFs and the AOD.  

29 

 
Figure 20: Example of Electrical Energy utilization at Steel making Area (blue), HSM (pink), others 
(green) and Total (red) 

Regarding the Product/process no improvement are foreseen. 

Already available data bases at Lech-Stahlwerke LSW 

The  following  cyclic  process  data  for  dynamic,  short-term  monitoring  and  prediction  of  the  (electrical) 
energy  demand  at  the  two  EAFs  and  the  two  LFs  are  stored  in  a  data  base  with  a  time  interval  of  1 
second: 

Electrical energy consumption since first power-on, active power, cos Phi (EAF, LF) 

 
  Arc Voltage and Current for 3 phases (EAF, LF) 
 
  Consumption of natural gas and oxygen for three post combustion injectors (EAF)  

Flow rates of natural gas and oxygen for three Cojet burners (EAF) 

In  Figure  21  and  Figure  22  the  parameters  of  the  electrical  energy  input  in  the  EAF  are  shown  for  an 
example heat. Figure 23 shows the gas flow rates and consumption figures of the chemical energy input 
for the same heat. In Figure 24 and Figure 25 the parameters of the electrical energy input in the LF are 
shown. 

Figure 21: Parameters of electrical energy input at the EAF for an LSW example heat: Electrical 
energy, active power and cos Phi 

30 

00.10.20.30.40.50.60.70.80.9101000020000300004000050000600007000080000900001000005:556:006:056:106:156:206:256:306:356:406:45Cos PhiElectrical energy in kWh, Active power in kWTimeEAF 1 Electrical energyElectrical energyActive powerCos Phi 
  
 
Figure 22: Parameters of electrical energy input at the EAF for an LSW example heat: Arc voltages 
and currents of the three phases of the AC EAF 

Figure 23: Parameters of chemical energy input at the EAF for an LSW example heat: Oxygen and 
natural gas flow rates for the Cojet injectors 

31 

020406080100120140160180200010020030040050060070080090010005:556:006:056:106:156:206:256:306:356:406:45Arc current in kAArc voltage in VTimeEAF 1 Arc voltage and currentVoltage Ph-1Voltage Ph-2Voltage Ph-3Current Ph-1Current Ph-2Current Ph-3020040060080010001200140016005:556:006:056:106:156:206:256:306:356:406:45Gas flow rate in Nm³/hTimeEAF 1 Injector 1Natural gasShielding oxygenInjector oxygen 
 
 
Figure 24: Parameters of electrical energy input at the LF for an LSW example heat: Electrical energy, 
active power and cos Phi 

Figure 25: Parameters of electrical energy input at the LF for an LSW example heat: Arc voltages and 
currents of the three phases of the AC LF 

In addition the following data and consumption figures for each produced heat are stored in a production 
data base: 

  Steel quality and quality group 

32 

00.10.20.30.40.50.60.70.80.9101000200030004000500060007000800090001000013:5814:0314:0814:1314:1814:2314:2814:3314:3814:43Cos PhiElectrical energy in kWh, Active power in kWTimeLF2 Electrical energyElectrical energyActive PowerCos Phi0510152025303540455005010015020025030035040045050013:5814:0314:0814:1314:1814:2314:2814:3314:3814:43Arc current in kAArc voltage in VTimeLF 2 Arc voltage and currentVoltage Ph-1Voltage Ph-2Voltage Ph-3Current Ph-1Current Ph-2Current Ph-3 
 
 
  Scrap  charge  (weights  of  the  different  scrap  types,  no.  of  scrap  baskets)  incl.  charge  coal  and 

dololime (EAF) 
Electrical energy consumption (EAF, LF) 

 
  Average Cos Phi (EAF) 
  Natural gas consumption (EAF) 
  Oxygen consumption (EAF) 
Foam coal consumption (EAF) 
 
 
Lime consumption (EAF, LF) 
  Graphite electrode consumption 
 
 
 

Tapping temperature and liquid steel weight (EAF) 
Power-on and Tap-to-tap time (EAF) 
Power-off  times  with  differentiation  regarding  reasons  (charging,  tapping,  disturbances  etc.) 
(EAF) 
Treatment time (LF) 

 
  Alloy material consumption (weights of different types at EAF tapping and LF) 
  Ar and N2 stirring gas consumption (LF)  

The existing data bases at LSW can be regarded as complete for the purpose of the project.  

Already available data bases at ORIMartin Brescia 

For ORIMartin, monitoring and management of the electrical energy demand was initially focused on one 
of  the  two  LFs,  Nitrogen  production  and  thermal  treatment  accounting  for  less  than  10%  of  the  total 
amount of electricity needed. A gradual increase of the processes under monitoring was  individuated as 
necessary  and  therefore  during  2015  the  EAF  the  other  LF  and  the  fume  treatment  was  added  while 
further improvement in order to have a complete mapping of electricity needed is still on going. Actually 
the Electrical Energy data are available every 15 min. 

Product/Process data necessary to the project are available and doesn’t need improvement.  

Already available data bases at Hoesch Hohenlimburg HHO 

As depicted in Figure 26, the main consumers of electrical energy in the plant of HHO are located  in the 
steel plant. Together with the auxiliary plants, the rolling mill aggregates in the plant cover about 50% of 
the  total  electrical  energy  consumption.  45  %  of  this  electrical  energy  consumption  is  covered  by  the 
main drives. The second main consumer is the spring plant with about 30%. Subsequently, pickling line 
and the furnaces of the hot rolling mill have to be included as a mentionable part of the overall electricity 
distribution as well. 

The Energy data are available on a 15 min basis. For the purpose of the project the available data doesn’t 
need any enhancements.  

Figure 26: Distribution of the electrical energy supply to the different consumers in the HHO plant 

33 

 
 
2.2.2.3  Task 2.3: Monitoring devices adjustment, power load characteristics analysis and 

characterization; preliminary data acquisition campaigns  

This task aimed at the preliminary characterization of both internal (process data) and external data (grid 
behavior). Data from plant have been collected and statistically evaluated in order to quantify the energy 
necessary  to  each  process  identified  in  previous  tasks.  With  the  aim  of  better  understanding  the 
possibilities  to  access  the  Control  Reserve  market  an  analysis  of  the  global  imbalance  of  network  have 
been performed. 

Plant Data Analysis 

At LSW, AST and ORI the behavior of EAFs have been evaluated from different points of view. For HHO a 
specific analysis has been performed due to the completely different plant structure. 

As described in Task 2.1 LSW has two AC EAFs, ORI one Consteel® EAF, and both produce carbon steel, 
while AST has two EAFs and produces stainless steel. The EAFs represent a high percentage of the total 
electricity needed in the production. 

From  a  theoretical  point  of  view  the  total  energy  required  to  melt  the  scrap  and  to  superheat  it  to  the 
typical  tap  temperatures  requires  from  350  kWh/t  of  steel  for  a  Consteel®  to  380  kWh/t  of  steel  for  a 
conventional EAF. Actual electricity use in EAFs are reported to range between 360 – 590 kWh/t.6F

7  

Four  different  aspects  were  monitored  and  analyzed  within  the  reporting  period  of  the  project.  These 
aspects are 

 
 
 
 

an analysis of the EAF power load characteristic of single heats, 
a statistical analysis of the EAF heats and operation mode, 
an analysis of the main electrical energy consumers of the steel mill (yearly energy report), 
an  analysis  of  the  power  load  characteristic  of  the  steel  mill  with  a  focus  on  short  term 
deviations of the 15 minutes intervals. 

a.  Analysis of the EAF power load characteristics 

The power load characteristics at the electric steelmaking plant of LSW were analysed on the basis of the 
acyclic process data listed under Task 2.2. Figure 27 shows the treatment of a typical EAF heat together 
with the power-on and power-off times. 

The tap-to-tap time of 57 minutes can be first divided into a power-on time of 40.3 min and a power-off 
time  of  16.7  min.  The  power-off  time  itself  mainly  consists  of  the  times  for  scrap  basket  charging  and 
tapping, and operation stoppages due to waiting times, electrode nippling or other unplanned disruptions.  

7 Steel production - energy efficiency working group Final report, January 2014 

34 

 
                                                
 
Figure 27: Power load characteristics of a typical EAF heat at the LSW steel plant 

In Figure 28 a similar behavior is shown for a heat where three scrap baskets were charged to one of the 
two EAFs in AST. The measured tap to tap time was 79 minutes with 57 minutes of power on. 

In Figure 29 a heat with two scrap baskets of the same EAF in AST is shown.  

Figure 28: Power load characteristics of an EAF heat with three baskets at the AST steel plant 

35 

ChargingPower onDisruptionsTappingTaptotap 
 
 
Figure 29: Power load characteristics of an EAF heat with two baskets at the AST steel plant 

b.   Statistical analysis of the EAF operation  

Statistical analyses of data coming from LSW, AST and ORI have been performed.  

A statistical analysis for a 8-months-period of the LSW EAF-operation shows that the EAFs have been in 
operation in 86-88 % of the available time. The remaining 12-14 % of the available time have been spent 
for  stoppages  and  shut  downs  of  the  plant,  e.g.  for  planned  maintenance  shifts  within  a  four  weeks 
period.  

The results of the statistical analysis of the operation time are shown in Figure 30. 

Figure 30 shows that the power-on-time of the two EAFs has been determined with a mean value of 43,9 
minutes  respectively  41,1  minutes.  The  tap-to-tap-time  shows  a  total  variation  between  50  -  100 
minutes with a mean value of 69,1 respectively 63,7 minutes. This means that the actual time sequence 
of a single heat may vary to a high extent due to different reasons of quality management and unplanned 
stoppages. 

The  maximum  load  of  both  EAF  has  been  controlled  within  the  control  periods  of  15  minutes  and  has 
usually  been  set  to  125  MW  as  a  maximum  mean  value  for  the  melting  power  of  EAFs  and  LFs  in  this 
period. The two EAFs have been shut down for a short time if this set point could not be met. The results 
are  shown  in  Figure  30  in  the  diagrams  “no  power”  for  the  EAF1  and  EAF3.  Both  EAF  have  been  shut 
down  simultaneously  for  a  short  time  period  between  0  -  3  minutes.  The  total  time  spent  for  this  shut 
down was about 0,15 % of the total operating time. About 13 to 15 % of the heats were influenced by 
this short shut down to control the maximum load of the factory. 

Figure  31  shows  the  specific  electrical  energy  measured  in  the  two  EAFs  in  AST  during  one  month 
production. The EAF5 shows a worse performance in comparison to the other EAF. To improve the EAF5 
yield plant and operative modifications have been planned for the beginning of 2016.  

36 

 
 
Figure 30: Statistical analysis and mean values of the EAF cycle times per heat at Lech-Stahlwerke 
LSW 

Figure 31: EAFs Specific Electrical Energy at AST during one month production 

37 

 
 
 
Figure 32 shows the Tap to Tap time of one of the two EAFs in AST during one month production. As can 
be seen more than 50% of the heats are in the range between 80 and 90 minutes, but the spread is fairly 
wide, which implies, together with the spread of Specific Electrical Energy, more difficulties in making a 
correct  day  ahead  forecast  of  the  electricity  needs  and  therefore  corrective  actions  should  be  taken 
during time to reach the accordance between booked and actual profile.  

Figure 32: Tap to Tap time of one Terni EAF during 6 months production 

A similar behaviour in terms of spread has been recorded also at ORI as shown in Figure 33 and Figure 
34. The mean value is different due to the fact that ORI has a Consteel® instead of a traditional EAF. 

Figure 33: EAF Specific Electrical Energy at ORI during one month production 

38 

 
 
 
Figure 34: EAF process duration at ORI during one month production 

c.  Analysis of the main electrical energy consumers of the steel mill 

The analysis of the main electrical energy consumers of the LSW steel plant on a basis of a yearly energy 
report  has  been  given  in  Figure  19.  The  figure  shows  that  together  with  the  auxiliary  plants,  the  liquid 
steelmaking  aggregates  in  the  steel  plant  including  the  auxiliary  plants  cover  about  93%  of  the  total 
electrical energy consumption. 73% of this electrical energy consumption is covered by the two EAFs as 
the by far largest consumers in the steel plant. They are followed by the two ladle furnaces with only 8% 
of the electrical energy consumption. Due to the fact that the main consumers of electrical energy are the 
two electric arc furnaces it has been decided that the future monitoring and management of the electrical 
energy  has  to  focus  on  the  operation  of  the  two  EAFs  and  those  auxiliary  plants  that  support  the  EAF 
operation. 

As depicted in Figure 26, the main consumers of electrical energy in the plant of HHO are located in the 
rolling plant. Together with the auxiliary plants, the rolling mill aggregates in the plant cover about 50% 
of the total electrical energy consumption.  45%  of  this electrical energy consumption is covered by the 
main drives. The second main consumer is the spring plant with about 30%. Subsequently, pickling line 
and the furnaces of the hot rolling mill have to be included as a mentionable part of the overall electricity 
distribution as well. 

In  Figure  15,  a  distribution  among  the  monitored  machines  at  ORI  including  the  new  measurements 
activated during 2015 is shown. The Steel making Area accounts for more than 70% of the total electrical 
energy and is the major responsible for the fluctuations in the energy requests. The remaining processes 
have almost a flat electricity demand and account for more than 20%.  

d.  Analysis of the power load characteristic of the steel mill with a focus on short term deviations of the 

15 minutes intervals. 

Finally the power load characteristic of the total LSW plant has been monitored. The analysis of the power 
load characteristic of the steel plant has been performed with a focus on short term deviations of the 15 
minutes intervals.  

39 

 
 
 
 Figure 35: Example of the melting power demand of the LSW steel plant in 15 minutes intervals for 
the time period of  08.07.2016 to 14.07.2016. 

It  has  to  be  mentioned  that  at  LSW  the  power  supply  is  separated  into  the  so  called  “Schmelzstrom” 
(Melting  power)  for  operation  of  the  two  EAFs  and  the  two  LFs,  and  the  so  called  “Betriebsstrom” 
(Operating power) for the other consumers (auxiliary aggregates, rolling mill etc.) of the plant. Only the 
melting power is limited to a peak value of 125 MW, which has to be monitored and controlled within the 
15 min time intervals. A typical example of the melting power demand during one week can be found in 
Figure 35 showing that a high scatter over the time occurred due to non-continuous production.  

The big consumers with significantly differing power demand that have been identified were mainly  

• 

• 

the two electric arc furnaces EAF1 and EAF3 and  

the ladle furnaces LF2 and LF4.  

The fluctuating power demand of the steel mill depends to a high extent on different parameters of these 
furnaces like  

• 

• 

the selected operating point (voltage tap) of the EAF and the LFs, 

the charge materials, 

• 

the status of the melting progress and the production process.  

In Figure 36 an example of electrical energy demand during one week is shown. It can be notice that ORI 
has  a  similar  behavior  with  a  high  scatter  due  to  the  non-continuous  processes.  Periods  of  very  low 
electricity demand is due to stop of the production during the week end. In  Figure 37 a typical behavior 
of electricity demand during a working day at ORI is shown. The black line indicates the overall objective 
to  reach  the  more  flat  situation  possible  according  to  production  requirements.  A  comparison  between 
Figure 36 and Figure 37 shows that this scattering is mainly due to the EAF and less to the two LFs, while 
the other processes have a very low influence. 

40 

 
 
 
Figure 36: Electrical energy demand of the ORI steel plant in 1 h intervals for a week 

Figure 37: Effective electricity demand compared to booked one in a day at ORI 

Figure 38 shows the subdivisions of different logical groupings with their electric meter in HHO. The total 
energy  requirement  of  HHO  is  distributed  by  individual  electricity  meters  to  electricity  meter  groups. 
There  are  three  main  subgroups  which  are  the  “Area  of  Responsibility”,  the  “Cost  Center”  and  the 
“Function groups”, the Area of Responsibility are the largest and contain several cost centers, which also 
contain several smaller Function groups. 

The  specified  collection  with  separate  electric  meters  was  realized  in  the  project  and  makes  possible  a 
more exact allocation of the load per aggregates. 

41 

 
 
 
Figure 38: Energy flow and power distributions in HHO 

Figure  39  shows  the  physical  representation  of  the  energy  feed  and  the  energy  distribution  with  their 
meters of some aggregates in HHO. 

Figure 39: Physical representation of the energy data acquisition 

Figure 40 shows the online measurement device view of the current values acquisition of the energy with 
other features like database, statistic or system protocol. For example we can visualize the actual power 
consumption of each line (L1, L2 and L3) and the total consumption of all the lines. 

42 

 
 
 
Figure 40: View of the measurement device for the energy data acquisition 

Grid Data Analysis 

More details are reported in Annex 3 

Data from TERNA 

RSE  has  provided  SSSA  with  data  about  both  energy  production  through  renewable  sources  (such  as 
photovoltaic,  hydroelectric,  geothermal  and  wind  power)  and  power  requirement  in  the  day-ahead  spot 
market  of  the  Italian  Power  Exchange,  as  well  as  other  data  concerning  with  the  imbalances  of  the 
National Transmission Network and with electricity balancing prices at the Dispatching Services Market. 

The period of reference is between November 2014 and September 2015. 

In more detail, the provided data belong to the following categories: 

  wind power production prevision 
 
 
 
 

energy production through some renewable sources 
power requirement in the day-ahead spot market 
signs of imbalances of the National Transmission Network 
electricity balancing prices at the Dispatching Services Market 

Some  of  the  provided  data  are  associated  to  market  zones  or  limited  production  poles,  while  others  to 
macro zones. In both cases, every data item is related to a time stamp, which represents a specific hour 
of a day. No data about foreign virtual zones have been provided.  

Zone data 

  Hourly previsions of wind power production, in MWh, are specified for each market zone or 

limited production pole. The prevision is made at day N for the next day N+1. 

  Hourly energy production actual values, in MWh, are given for each market zone and for each 

type of renewable source taken into account.  
Energy  production  data  are  related  to  renewable  sources  of  the  following  types:  Geothermal, 
Hydro (River), Photovoltaic Estimated, Photovoltaic Measured and Wind. 
Specifically, our attention is directed to Geothermal, Hydro (River) and Photovoltaic Estimated.  
  Hourly power requirement actual values in the day-ahead market are specified for each market 

zone (in MWh). 

Macro zone data 

  Hourly sign of imbalance of the National Transmission Network for each macro zone. A 

positive/negative imbalance means that the actual injection of electric energy is greater/lower 
than the actual withdrawal of electric energy. 

  Hourly average selling price of the Dispatching Services Market (in €/MWh) for each macro zone. 
  Hourly average purchase price of the Dispatching Services Market (in €/MWh) for each macro 

zone. 

43 

 
 
Data preprocessing 

The value of the average selling  or  purchase  price, for a specific hour, can be undefined, meaning that 
TERNA didn’t need  to resort  to the Dispatching  Services  Market for balancing purposes. In these  cases, 
TERNA solved the imbalance of the National Transmission Network with a redistribution of energy among 
the zones. 

However,  for  our  statistical  analysis  it  is  necessary  to  have  a  complete  time  series  for  both  selling  and 
purchase prices. The percentage of undefined price values is quite low, less than 13% for both prices in 
each  macro  zone,  as  shown  in  Table  10.  Therefore,  the  undefined  hourly  values  have  been  filled  up  by 
using linear interpolation procedures on each price time series. 

Furthermore, for each day, the information about the day being a working or a non-working day has been 
computed. 

Data aggregation 

The aim of data aggregation is to obtain the overall energy production data and power requirement data 
for each macro zone. For  this purpose, each  category  of zone data  has  been  aggregated by  sum  on all 
the market zones belonging to a macro zone. 

The limited production pole contributions have been included into the market zone to which they belong. 
Furthermore, in order to find out the overall energy production data related to the  renewable sources of 
interest (Geothermal, Hydro (River) and Photovoltaic Estimated), only energy production data associated 
to the above-mentioned sources have been summed. 

Dataset 

Starting  from  the  preprocessed  and  aggregated  data,  a  dataset  has  been  created  for  each  macro  zone 
(NORTH  and  SOUTH).  The  macro  zone  dataset  has  been  represented  by  a  matrix,  where  each  row 
corresponds to one hour of a day in the reference period, while columns correspond to the different types 
of collected information. 

An example of the dataset organization can be found in Annex 3. 

The dataset has been divided into two parts: input data and target data. 

The target variables correspond to the variables to be predicted: 

  Hourly sign of imbalance of the National Transmission Network 
  Hourly average selling price at the Dispatching Services Market 
  Hourly average purchase price at the Dispatching Services Market 

Statistical Data Analysis 

The main goals of time series analysis are to identify the nature of the phenomenon represented by the 
sequence of observations and to predict future values of the time series target variable.  

SSSA  has  made  a  separate  study  for  each  target  variable  and  for  each  macro  zone.  The  aim  of  each 
study  is  to  build  a  model  that  can  predict  the  future  values  of  the  target  variable,  learning  from  the 
observations  taken  from  the  available  dataset  (supervised  method).  Several  modeling  and  forecasting 
techniques have been tested. 

a.  Network imbalance sign analysis 

The target variable is the network imbalance sign and its possible values are ‘+1’ for positive imbalance 
and ‘-1’ for negative imbalance. 

First  SSSA  analysis  on  the  imbalance  sign  has  been  the  study  of  the  change  of  the  sign  between 
sequential  times  in  the  time  series.  The  imbalance  sign  is  a  constant  function  for  most  of  the  times 
(about 84% of times in the North macro zone and 83% of times in the South macro zone) and only a few 
changes of the sign happen between sequential times during the time period (about 16% of times in the 
North macro zone and 17% of times in the South macro zone).  The results of the change of sign study 
are shown in Table 7. 

44 

 
Macro zone 

Imbalance sign unchanges 
% 

Imbalance sign changes 
% 

North 

South 

84.15 

83.11 

15.85 

16.89 

Table 7: Imbalance sign changing between sequential hours for macro zone 

Therefore, even a very simple and trivial algorithm, which predicts the value of the imbalance sign for the 
next hour (t + 1) equal to the value of the sign at the actual hour (t), allows to gain a quite satisfactory 
prediction  accuracy  (about  84%  for  the  North  macro  zone  and  about  83%  for  the  South  macro  zone). 
Every further predictor is to be considered  significant  only when the prediction accuracy is greater than 
84%, which represents the baseline performance. 

MARKOV CHAIN for imbalance sign 

The Markov chain-based technique has been developed  in  order to  calculate the probabilities of moving 
from the positive state to the negative state represented by a transition matrix. A Markov model can be 
visualized through a state diagram as shown in Figure 41. 

Figure 41: Markov chain state diagram 

The  transition  matrix  of  the  imbalance  sign,  concerning  to  the  macro  zone  NORTH  and  SOUTH,  is 
represented respectively in Table 8 (a) and (b). 

North 

+ 

- 

+ 

0.87 

0.20 

(a) 

- 

0.13 

0.80 

South 

+ 

- 

+ 

0.80 

0.15 

(b) 

- 

0.20 

0.85 

Table 8: Transition matrix related to NORTH (a) and South (b) macro zone 

From Table 8, we can conclude that if the imbalance sign at the time t is positive, it has a probability of 
87% (in the NORTH macro zone) or 80% (in the SOUTH macro zone)  to remain in the same state, while 
it has a probability of 13% (in the NORTH macro zone)  or 20% (in the SOUTH macro zone) to switch to 
the negative state at time t+1; on the other hand, if the imbalance sign at the time t is negative, it has a 
probability  of  80%  (in  the  NORTH  macro  zone)  or  85%  (in  the  SOUTH  macro  zone)    to  remain  in  the 
same state, while it has a probability of 20% (in the NORTH macro zone) or 15% (in the SOUTH macro 
zone) to switch to the positive state at the next time. 

Using the Markov chain, we can also calculate the probability to remain in the same state or to move into 
the other one at the time t+K, knowing only the state at the time t. The results for K=10 are reported in 
Annex 3. 

45 

 
 
 
 
 
 
 
 
 
 
Other advanced techniques for imbalance sign 

In  order  to  increase  prediction  performance,  SSSA  has  experimented  several  more  sophisticated  and 
advanced techniques. The different types of techniques used by SSSA to represent time series data and 
generate predictions are the following: 

  Decision trees 
  Support Vector Machines (SVM) 
  Naïve Bayes classifier 
  Non linear autoregressive neural networks (NAR) 
  Non linear autoregressive neural networks with exogenous inputs (NARX) 

All the considered methods are supervised machine learning methods, meaning that the model is built by 
learning  the  mapping  between  input  and  target  data  from  data  examples.  These  methods  are  used  for 
classification  and  regression,  depending  on  whether  the  variable  to  be  predicted  is  a  categorical 
(qualitative)  variable  or  a  quantitative  variable.  Specifically,  NAR  and  NARX  are  neural  networks 
commonly used for time series modeling and prediction.   

Time series model 

The model, which has been used in time series modeling, can be defined by the following equation: 

𝒕𝒂𝒓𝒈𝒆𝒕(𝒕) = 𝒇(𝒕𝒂𝒓𝒈𝒆𝒕(𝒕 − 𝟏), 𝒕𝒂𝒓𝒈𝒆𝒕(𝒕 − 𝟐), . . , 𝒕𝒂𝒓𝒈𝒆𝒕(𝒕 − 𝒏), 𝒊𝒏𝒑𝒖𝒕(𝒕 − 𝟏), 𝒊𝒏𝒑𝒖𝒕(𝒕 − 𝟐), . . , 𝒊𝒏𝒑𝒖𝒕(𝒕 − 𝒏))   (1) 

This  equation  states  that  the  next  value  of  the  dependent  output  variable  target(t)  is  a  function  of  the 
previous values of the output variable and previous values of an (exogenous) independent input variable.  

This  model  can  be  implemented  by  using  a  statistical  method  to  approximate  the  function  𝑓.  SSSA 
implemented this model by using, in turn, each of the enunciated statistical methods to approximate the 
function 𝑓 

Building the data examples  

For  n  =  6,  each  data  example  has  been  created  with  the  six  previous  values  of  target  and  input  data 
(time t-1, .., t-6) and, moreover, the next value of the target (time t). 

Data Example = [target(t-1), ..,target(t-6), input(t-1), …, input(t-6) target(t)] 

Predicting  the  network  imbalance  sign  at  time  t  is  equivalent  to  classify  the  six  previous  times  data 
instance into one of two classes (+1, -1). 

Building and evaluating the model 

Data examples are divided into two portions: training set and validation set. 

Training data are used to build the model, while validation data are classified using the built model. The 
model is evaluated by measuring the percentage of correctly classified examples in the validation set.  

The classification algorithm 

After  having  randomly  shuffled  input  and  target  data,  data  are  divided  into  training  set  (75%)  and 
validation set (25%), so that the two classes are equally distributed among training and validation sets. 
Each training builds a binary classification model that can classify an input data instance into one of two 
classes (+1, -1). Ten trainings per test have been performed in order to assess and check the stability of 
results. Classification performances have been calculated by using the Balanced Classification Rate (BCR), 
explained in the Annex 3. Afterwards, performance averages have been calculated on the ten trainings. 

Classification performance results 

The classification algorithm has been applied by using the different statistical techniques and by training 
the model in two different ways: 

  with data examples including all the input data variables together with target data variables at 
the previous six times (model with exogenous inputs: the target to be predicted depends both 
from previous values of itself and from previous values of an external input time series) 

  with data examples including only target data variables at the previous six times (model without 

exogenous inputs) 

The results turned out to be comparable in the two approaches, meaning that most of informative 
contents is already included inside the historical target.  

46 

 
Table 9 shows the results achieved by training the model with the different statistical techniques and in 
the two ways (with or without exogenous inputs), respectively for each macro zone. 

NORTH 

SOUTH 

Statistical Method 

BCR  – Only 
historical target 

BCR – Historical 
input and target  

BCR – Only 
historical target 

BCR – Historical 
input and target  

Decision Tree 

SVM  

Naïve Bayes 

NAR /NARX 

0.8374 

0.8350 

0.7865 

0.8338 

0.7434 

0.8348 

0.7711 

0.8316 

0.8306 

0.8311 

0.7879 

0.8222 

0.7381 

0.8241 

0.7574 

0.8257 

Table 9: Imbalance Sign - BCR performance results for North and South macro zone (n = 6) 

SSSA has also experimented different values for the number of delays (n = number of previous times) 
and a number of delays equal to six represents a good tradeoff between the performance and the 
complexity of the model. 

A  remark  about  the  cases  of  neural  networks  (NAR/NARX)  should  be  given.  Since  the  output  of  a 
NARX/NAR  neural  network  is  a  value  in  the  range  [-1    1],  the  predicted  value  has  been  set  to  1  if  the 
network output value is greater or equal to 0, to -1 otherwise. 

Conclusions 

None  of  the  advanced  statistical  methods  examined  has  been  able  to  go  over  the  limit  imposed  by  the 
baseline. 

b.  Analysis of the need to resort to the Dispatching Services Market 

SSSA  made  an  attempt to  predict  the  fact that  TERNA  did  or did  not  need to  resort to the  Dispatching 
Services  Market.  In  fact,  all  the  original  undefined  values  for  the  average  selling  /  purchase  price 
represent the hours when no recourse was needed, while defined values represent those hours in which a 
recourse happened.  

Since  the  percentages  of  no  recourse  are  quite  low,  as  showed  in  Table  10,  SSSA  tried  to  improve 
8,  random 
performance  results  by  using  specific  techniques  for  imbalanced  dataset,  such  as  SMOTE7F
under-sampling and other algorithms 8F

9, without achieving any satisfactory results. 

Macro zone 

No MSD recourse % - Selling  No  MSD 
Purchase 

recourse  % 

- 

North 

South 

13.13 

4.48 

1.78 

0.28 

Table 10: Percentage of recourses to MSD 

c.  Analysis of hourly average selling/purchase price at the Dispatching Services Market 

The  time  series  model  is  still  the  one  defined  by  equation        (1)  and  the  statistical  methods  used  to 
approximate the function f are respectively NAR and NARX neural networks.  In this context, SSSA used 
these  types  of  networks  to  make  a  prediction  of  the  price  for  the  next  ten  hours  (multi-step-ahead 
prediction). 

Multi-step-ahead prediction algorithm 

8 N.V. Chawla, K.W. Bowyer, L.O. Hall, W.P. Kegelmeyer, "SMOTE: synthetic minority over-sampling 
technique", Journal of Artificial Intelligence Research , 16, pp. 321–357, 2002. 
9 S. Cateni, V. Colla and M. Vannucci, "A method for resampling imbalanced datasets in binary 
classification tasks for real-world problems", Neurocomputing 135, pp. 32-41, 2014. 

47 

 
 
                                                
 
 

 
 

 

The data examples time series is divided into two continuous blocks, the first of 75% and the 
second of 25% of data. The training set corresponds to the first block, while the validation set 
corresponds to the second block.  
The model has been trained from the training set. 
The model has been validated on the validation set by predicting ten hours at a time. 
For this purpose, the validation set  has been scanned,  hour  by hour, from  the beginning to the 
end with steps of ten hours.  
Each step of the algorithm makes a prediction for the next ten hours, starting from input and 
target values at the six previous hours, according to the model equation. 

  All predictions, made on all the steps, for the same (next) hour are gathered together and the 

measured and predicted values are visualized in a plot. 

  An estimation of the prediction error is calculated by evaluating all predictions for the same hour 
10. 
and by means of NSRMSE performance function (Normalized Square Root Mean Square Error) 9 F

The  NSRMSE  performance  function  and  other  performance  functions  taken  into  account  in  the  analysis 
are explained in Annex 3. 

The results of Multi-step-ahead prediction on selling and purchase prices are reported in Annex 3. 

2.2.2.4  Task 2.4: Monitoring the actual power engagement and historical event frequency  
The following paragraph represents the Deliverable D2.2   

The monitoring systems have been implemented in the reference plants improving not only the number 
of  processes  monitored  but  also  the  ease  to  use.  Nevertheless  the  importance  of  monitoring  and 
improving the quality of the energy measures is so important for the steel plants that the process is on a 
continuous improvement, which means that further measurements will be added or more performing Db 
server  will  be  installed;  that’s  why  in  the  GANNT  the  end  of  this  task  has  been  prolonged  without 
affecting the development foreseen in the following WPs. 

A selection of the most relevant processes mostly with reference to the volatility of the energy demand 
has been performed in order to identify the more important process to be continuously monitored besides 
the  overall  energy  need  of  the  whole  pant.  Typically  some  processes  like  continuous  casting  show  an 
electricity demand almost constant in time, apart from downtime due to e.g. maintenance and therefore 
it  was  decided  not  to  monitor  independently  their  energy  demand  continuously.  In  any  case  monthly 
reports are available for most of the steel plants processes. 

For AST the focus has been in processes in steel shop (EAF, AOD, LF) and the HSM, similarly also in ORI 
the EAF and the LF together with the HSM has been considered. The new monitoring system of AST and 
ORI are  accessible  through  Web browser  and the profile of the monitored processes and the total plant 
energy is available continuously. In AST data related to energy price (PUN) are displayed and a specific 
interface  has  been  developed  to  monitor  specifically  the  15  minutes  peak.  Figures  of  the  implemented 
system can be found in WP6. 

Lech-Stahlwerke  has  designed  a  new  monitoring  and  visualization  system  of  the  actual  power 
engagement and all energy flows of all aggregates in the electric steelmaking plant. The system is called 
STWISS (STahlWerksInformationsSyStem). It is linked to the data base of a new Level 2 process control 
system.  The  enhanced  system  includes  new  hardware  (server,  work  stations,  user  displays)  and 
adequate software for an energy and power load monitoring.  

10 Shcherbakov, Maxim Vladimirovich, et al. "A survey of forecast error measures." World Applied 
Sciences Journal 24: 171-176, 2013 

48 

 
                                                
 
Figure 42: Structure of the monitoring and visualisation system at LSW 

The structure of the system is shown in Figure 42. 

This  monitoring  system  provides  an  online  overview  of  all  aggregates  of  the  process  route  (EAFs,  LFs, 
VDs,  Stirring  stations,  Continuous  casting  plants)  in  the  electric  steelmaking  plant,  and  visualizes  the 
process flow and the status of the process for every heat, comprising among others the following data: 

Ladle number 

Position within the process route  

  Charge number  
 
  Steel grade 
 
  Steel weight 
  Start time and status of the process at each aggregate  
  Status of the continuous caster (start, weight, temperature) 
  Chemical analysis of each heat 

Figure 43 shows the outline of the user display of the system. 

The  online  terminal  at  HHO  is  able  to  show  the  actual  consumption  of  each  measure  point  including 
Steam, Compressed air, Current, Natural gas and city water for the last 30 days with a resolution of 15 
minutes.  In  order  to  visualize  data  with  higher  resolution  a  special  software  has  been  developed  which 
collects the data on base up to 1 Sec. 

The main events that are taken into consideration are synthetized in Table 11. They can be either internal 
or external, predictable or unpredictable. As it will be  better described in WP6 the events represent the 
trigger to activate the energy demand system.  

In  particular  the  predictable  schedule  maintenance  and  downtime  are  useful  for  the  determination  of  a 
more  reliable  energy  demand  profile  for  accessing  the  day  after  market.  When  a  process  is  faster than 
the  previous  one  or  the  product  requests  are  not  enough  to  justify  a  24hour  a  day  and  7days  a  week 
production the process is stopped for at least one shift during week end. In these case the maintenance 
is  normally  done  before  restarting  the  process  or  just  after  the  planned  stop  resulting  in  a  longer 
downtime.  

49 

 
 
Figure 43: User display of monitoring and visualisation system at LSW 

The interruptibility represents an external unforeseen event, as described in WP1 the involved process is 
automatically disconnected by the TSO. This is a normal practice in Italy; despite it has no upper limit for 
the  duration  it  normally  lasts  at  maximum  some  minutes  (in  2016  at  ORI  the  longest  detachment  was 
less  than  7  minutes  and  the  others  between  1  and  3  minutes,  at  AST  the  only  one  was  less  than  6 
minutes)  and  happens  just  few  time  in  the  year  (in  2016  at  ORI  four  times  and  at  AST  just  one);  this 
means that normally it has little impact on the production. The challenge is that during the detachment 
the  other  loads  cannot  exceed  the  power  measured  at  the  beginning  of  the  detachment  and  therefore 
should be monitored carefully. 

There  are  no  historical  data  on  plant  experience  on  energy  markets  request  because  the  market  is  not 
still  open  in  Italy  and  the  two  German  industrial  partners  are  going  to  test  this  possibility  within  the 
project (see WP3 and WP4 for more details).  

Table 11: type of events 

Internal 

External 

Predictable 
Scheduled Maintenance 
Scheduled downtime 

Unpredictable 
Unplanned process downtime 

Energy market requests  
Interruptibility  

The unplanned process downtime has impact both on the difference between booked and actual energy 
profile  and  in  the  15  minutes  peak  for  the  next  periods  due  to  the  necessity  to  overcome  the  loss  of 
production. 

The  process  data  of  LSW  have  been  analysed  in  order  to  find  the  main  reasons  for  deviations  of  the 
power  engagement.  Figure  44  shows  exemplarily  an  analysis  of  the  operation  of  EAF  No.  1  in  2016.  A 
mean tap-to-tap time of 68 minutes has been determined. However it can be seen that only 57 minutes 
have been spent to the planned operation of the EAF, including the power-on time as well as power-off 
times required for scrap charging, tapping and regular furnace maintenance. Further 11.2 minutes were 
devoted to unplanned events and stops due to machinery failures and required maintenance  work. This 
will also cause deviations within the predictable energy demand. 

50 

 
 
 
 
Figure 44: Time intervals in minutes of the tap-to-tap time needed for planned production and 
unplanned events and maintenance of the production of EAF1 at Lech-Stahlwerke 

2.2.3  Work package 3: Forecast of power engagement 
In  this  WP  the  actual  procedures  for  forecasting  the  power  demand  has  been  studied  with  the  aim  of 
enhancing  the  accuracy  of  the  booked  profile.  Historical  data  analysis  has  been  carried  out  in  order  to 
evaluate relations among energy, process and product data to determine the electricity needs based on 
product and process characteristics. 

An  electricity  flow-sheet  model  that  can  forecast  the  energy  consumption  related  to  those  plants  of  a 
steel industry characterized by a relevant energy demand, in order to estimate the energy requirement in 
the more plausible manner has been developed.  

Based on those result control loop to manage the energy adjustment has been developed. 

Finally,  an  analysis  of  the  flexible  disconnectable  loads  in  steel  plants  has  been  carried  out  taking  into 
consideration  the  specificity  of  the  industrial  partners  but  aiming  to  have  general  remarks  for  the  steel 
companies participation in energy markets. 

2.2.3.1  Task 3.1: Development of the methodology to predict the electric energy demand in 

the steel plant according to heat / process and product mix  

Several statistical analysis, mainly on EAFs, based on the historical data have been performed in order to 
define the methodologies to calculate specific process/product electricity needs to be used for enhancing 
the overall energy profile forecasting.  

Improvement of the electricity demand prediction 

A  first  task  to  improve  the  accuracy  and  reliability  of  the  day-ahead  forecast  of  the  electrical  energy 
demand  at LSW  was to determine the  electrical  energy consumption  at the EAF depending on  the steel 
grades  to  be  produced.  For  that  purpose,  historical  data  of  about  2000  heats  from  469  different  steel 
qualities  produced  in  the  first  6  months  of  2014  were  evaluated.  The  different  steel  qualities  were 
grouped  according  to  the  sum  of  the  most  important  alloy  elements  (C,  Mn,  Si,  Cr,  Mo,  Ni)  which 
influence the electrical energy consumption. Furthermore the tap weight was considered, which is lower 
for  quality  steel  grades  (<  93  t)  according  to  the  required  freeboard  in  the  ladle  for  the  vacuum 
treatment.  Figure  45  shows  the  actual  electrical  energy  consumption  of  the  evaluated  EAF  heats 
depending on the alloy element concentration for the two different groups of tap weights.  

51 

57,030.90.150.80.80.20.4planned productionfailures of fabricationmechanical failureselectrical failureswaiting for castingcentral maintenance failuresdamage of refractoriesmetallurgical reasonsshort stop 
 
Figure 45: Electrical energy consumption of EAF heats vs. alloy element concentration and tap weight 

These  dependencies  were  used  to  set  up  a  grouping  of  the  steel  qualities  produced  at  LSW.  As  can  be 
seen from Figure 46, in total 6 quality groups (quality steel grades) for the lower tap weight (< 93 t) and 
5 quality groups (mostly structural steel grades) for the higher tap weight (> 93 t) were defined. 

Figure 46: Grouping of LSW steel qualities according to alloy element concentration and tap weight 

In addition, the electrical energy consumption of the EAF heats was analysed with the help of a statistical 
model for the electrical energy demand which has previously been developed by BFI [Koehle 2002]. With 
the help of this simple  model,  which  is shown  in  Figure 47, the specific electrical  energy demand of an 
EAF, related to the liquid tap weight, can be predicted depending on the charge materials, the chemical 
energy  inputs  like  natural  gas  and  oxygen,  and  several  further  production  parameters  like  tapping 
temperature and tap-to-tap time. 

52 

y = 8.1306x + 449.04R² = 0.05063504004505005506006500123456Electrical energy consumption in kWh/tAlloy element concentration (C, Si, Mn, Cr, Mo, Ni) in %Tap weight > 93 tTap weight < 93 t  < 0.95%  < 1.25%  < 1.8%  < 2.2%  < 3%4304404504604704804905000%1%2%3%4%5%6%Electrical energy consumption in kWh/tAlloy element concentration (C, Si, Mn, Cr, Mo, Ni) in %Tap weight < 93 tTap weight > 93 t 
 
 
Figure 47: Statistical model to calculate the electrical energy demand of EAFs  

This model was used to predict the electrical energy demand of the heats treated at the EAF 1 of LSW. In 
total  2065  heats  with  information  on  the  different  charged  scrap  types  and  amounts  were  evaluated. 
Figure  48  shows  for  all  evaluated  heats  a  comparison  between  predicted  electrical  energy  demand  and 
actual  electrical  energy  consumption  in  kWh/  t  of  tapped  liquid  steel.  The  accuracy  of  this  prediction  is 
with a standard deviation of around 20 kWh/t quite good. 

Figure 48: Calculated vs. actual electrical energy consumption for EAF heats at LSW 

The evaluation also revealed that the actual electrical energy consumption (WE) depends on the specific 
charge material weight gE, that means the amount of charged scrap in kg related to the tap weight in t. 
Figure  49  shows  that  heats  with  a  very  low  specific  charge  weight  have  a  significantly  lower  specific 
energy consumption. Such heats are produced before repair shifts and other planned maintenance stops, 
when  the  furnace  is  completely  emptied  and  thus  the  liquid  heel  complete  accounts  for  the  liquid  tap 
weight.  On  the  other  hand,  heats  with  a  higher  specific  charge  weight  are  produced  after  such  repair 
shifts  to  build  up  again  the  hot  heel.  Those  heats  have  a  higher  specific electrical  energy  consumption. 
Both effects have to be considered for a precise prediction of the electrical energy demand within a day-
ahead schedule.  

53 

AZAHMAShrAHBI/DRIAERGG1000GG350GG50GG801GG400375t/kWhWt/kWhWWNVt/³mM8.2t/³mM3.4t/³mM8mintt11600CT3.0VmVNLGNSAGATap weightGEMetallic charge weightGDRIDRIGHBIHBIGShrShredder-Scrap GHMHot metalGZSlag formersTATapping temperaturetSPower-on timetNPower-off timeMGBurner gasMLInjected oxygen MNPC oxygen250300350400450500550600650250300350400450500550600650calculated electrical energy demand in kWh/tactual electrical energy consumption in kWh/tMean deviation:Standard dev.:201420.2-1.4kWh/tkWh/t 
 
 
Figure 49: Actual electrical energy consumption depending on specific charge weight 

Figure 50: Specific charge weight, tap weight and specific electrical energy consumption for a 
sequence of EAF heats after (left) and before (right) a planned maintenance stoppage 

Figure  50  shows  on  the  left  hand  side  the  evolution  of  specific  charge  weight,  tap  weight  and  actual 
specific electrical energy consumption for a sequence of heats which were produced at LSW after a longer 
planned maintenance stop. The produced steel qualities are indicated in the top of the figure. It can be 
clearly  seen  that  the  first  two  heats  of  this  sequence  were  performed  with  a  higher  specific  charge 
weight, to build up the amount of the hot heel after start with an empty furnace. For these heats also the 
specific electrical energy consumption is significantly higher as for the other heats. On the right hand side 
of  Figure  50,  the  same  evolution  is  shown  for  a  sequence  of  heats  produced  before  a  planned 
maintenance  stop.  Here  for  the  last  heat  the  specific  charge  weight  was  reduced,  as  the  furnace  is 
completely  emptied  to  perform  the  maintenance  work.  As  the  tap  weight  stays  the  same,  the  specific 
electrical energy consumption is lowered accordingly.  

In  the  following  Figure  51,  for  the  first  sequence  of  heats  additionally  predicted  values  for  the  quality-
dependent tap weight and for the electrical energy consumption, based on the quality grouping illustrated 
in Figure 46 and on the statistical model results shown in Figure 48 are displayed. It can be seen that the 
predicted  electrical  energy  consumption  of  the  statistical  model  follows  the  actual  consumption  quite 
accurately.  However,  this  model  cannot  be  fully  applied  for  a  day-ahead  forecast,  as  it  needs  detailed 
information on the chemical energy supply and the actually charged scrap mix, which is not available in 
advance. The prediction based only on the information of the produced quality groups is of course not so 
accurate. 

54 

40045050055060065070075080001002003004005006007008009001000110012001300140015001600051015202530Actual electr. energy consumption  in kWh/tTap weight  in t * 10  //  spec. charge weight in kg/tConsecutive number of heatsSpecific charge weightTap weightActual electr. energyR15 (5x)                             ST52 (5x)                         ST52SH50CRMO4+HH37CRS4HL"D" (4x) 50CRMO450CRMO4100CR6  (7x)                                        ZF1 (2x)    27MC5JV3003504004505005506006507007508008509009501000105011001150120001002003004005006007008009001000110012001300140015001600247252257262267272Actual electr. energy consumption  in kWh/tTap weight  in t * 10                                                       Specific charge weight in kg/tConsecutive number of heatsSpecific charge weightTap weightActual electr. EnergyR10 (8x)                                           R15 (5x)    ST50ALT11ST52R10 (7x)    PC200ND (5x)     
 
 
Figure 51: Actual and predicted tap weight and specific electrical energy consumption for a sequence 
of EAF heats after a production stoppage 

The accuracy of the prediction according to both approaches is displayed in Figure 52. With regard to the 
whole production day, the statistical model shows a systematic deviation of around 100 kWh/t, whereas 
the prediction solely based on the quality information ends up with a deviation of almost zero. Thus the 
latter  prediction  seems  to  be  the  more  practical  and  accurate  one  for  a  day-ahead  forecast  of  the 
electrical energy demand. 

For  a  day-ahead  forecast,  the  prediction  accuracy  regarding  the  sum  of  the  energy  contingent  of  one 
production  day  is  relevant.  In  Figure  53  the  cumulative  values  of  the  actual  and  the  predicted  energy 
consumption of one EAF are plotted for the evaluated sequence in one figure, together with the deviation. 
It can be seen that the prediction is quite accurate, with a maximum cumulative error of only 8 MWh, i.e. 
less than 1 %.  

Figure 52: Prediction error of two model approaches for specific electrical energy consumption 

55 

40045050055060065070075080001002003004005006007008009001000110012001300140015001600051015202530Actual electr. energy consumption WE  in kWh/tTap weight  GA in t * 10                                                       Specific charge weight gE in kg/tConsecutive number of heatsSpecific charge weightTap weightPredicted tap weightActual electr. Energy WEPredicted WE from modelPredicted WE from qualityR15 (5x)                             ST52 (5x)                           ST52SH50CRMO4+HH37CRS4HL"D" (4x) 50CRMO450CRMO4100CR6  (7x)                                        ZF1 (2x)    27MC5JV-300-1500150300450600750900200250300350400450500550600051015202530Accumulated difference between predicted and actual electrical energy consumption in kWh/tActual electr. energy consumption  WE in kWh/tConsecutive number of heatsActual WESum of Diff Actual WE - Predicted WEQualityPredicted WE from model 
 
 
Figure 53: Prediction error of accumulated electrical energy consumption for a sequence of EAF heats 

At  HHO  the  power  distribution  has  been  determined  by  a  measurement  campaign,  the  average 
consumption  of  all individual  aggregates  is  calculated  on  the  basis of  historical  data  in  the  central  data 
model. The results of these calculations are the forecast of the average consumption of the future energy 
demand for the responsible energy provider. 

At  AST  plant  a  correlation  among  energy,  process  and  product  data  has  been  carried  out  in  order  to 
determine the electricity needs based on product and process characteristics. 

In particular for each of the most relevant plant i.e. both EAFs and HSM the specific electrical needs have 
been elaborated. 

For the two EAFs the data has been  analyzed to individuate the specific needs in term of MWh/ton, i.e. 
material unit at tapping, with relation to the typical steel type produced at AST Terni (304, 316, 430, 409 
and 441). 

Three  months  of  production  have  been  taken  into  consideration.  The  results  show  that  an  appreciable 
difference  can  be  seen  only  in  the  differentiation  between  austenitic  and  ferritic  steel.  Within  these 
classes  the  variation  are  very  small  and  therefore  is  reasonable  to  use  these  classes  and  the  relative 
mean value in the plant electricity needs calculation. 

In Figure 54 and Figure 55 the result of this elaboration is shown. 

The variability range with respect to the mean value is around 4% for both classes.  

In case of the AST HSM the specific electricity needs are directly joined to the steel type and geometry of 
the product (thickness, width, diameter). Specific mean values have been elaborated and will be used for 
the booked power profile determination. 

AT ORI a statistical analysis of the rolling mill  production has been performed to calculate energy needs 
specific to product characteristics (implying also some differences on production route)  

At  HHO  based  on  an  historical  evaluation  of  the  energy  needs  a  medium  value  for  the  booked  energy 
demand of each hour production has been calculated. 

56 

-25-20-15-10-505101520253035404550556065707580859095100-1000-500050010001500051015202530Sum of Difference of Actual-predicted WE in MWhSum of  electr. energy WE in MWhConsecutive number of heatsSum Predicted WE Quality * Predicted tap weightSum Actual WE  * GADifference of both sums 
 
Figure 54: Austenitic Stainless Steel specific electrical energy needs 

Figure 55: Ferritic Stainless Steel specific electrical energy needs 

2.2.3.2  Task 3.2: Development of Electricity Flow Sheet Model (EFSM) of the process/plant 

systems; identification of disconnectable loads, energy storage and possible switching  
The aim is to create an electricity flow-sheet model that can forecast the energy consumption related to 
those  plants  of  a  steel  industry  characterized  by  a  relevant  energy  amount  consumed,  in  order  to 
estimate  the  energy  requirement  in  the  more  plausible  manner.  Among  the  components  that  can 
influence the consumption of energy during steel production, certainly there are the quantity of steel to 
be produced and its steel quality. Both these factors determine the sequence of production processes to 
be  followed  to  produce  the  final  product  and  influence  the  treatment,  and  its  duration,  in  each  single 
plant of the sequence. For each production item, the sequence of plants where the item goes through and 
the undergone treatment in each of them is included in a production plan. 

A  first  simplified  version  of  the  flow  sheet  model  has  been  developed  on  the  basis  of  the  following 
hypotheses: 

 

 

The total consumption and the duration of the treatment of a production item in a plant are both 
priori known. 
The consumption of a production item is constant during the duration of the treatment.  

Configuration of plants 

The single plants of a steel industry, whose contributions to the energy consumption should be taken into 
account,  are  configured  in  an  xml  file.  An  example  of  the  plants  configuration  file  is  shown  Figure  56. 
Each plant is identified by a unique identifier and has other tags to explain the type of the plant and its 
number. 

57 

 
 
 
Production Plan 

The  production  plan  is  a  list  of  elements,  where  each  element  represents  a  production  item,  which  is 
processed  in  a  specific  plant,  for  a  certain  period  and  with  a  certain  amount  of  energy.  The  production 
plan contains all the production items processed in all plants during a day. 

Each production plan item includes the following information: 

 
 
 
 
 

cast number 
plant identifier 
starting time 
duration in minutes 
total energy consumption 

Each  production  plan  item  is  related  to  a  plant  through  the  plant  identifier:  only  items  related  to  the 
configured plants are taken into account. The production plan is stored in an Excel file. An example of a 
test production plan, made of abstract data, which absolutely do not correspond to reality, is represented 
in Table 12. This production plan has been shown only as an example to explain the production item data 
structure. 

Cast Number 
1 
1 
1 
1 
1 
2 
2 
2 
2 
2 
3 
3 
3 
3 
3 
4 
4 
4 
4 
4 
5 
5 
5 
5 
5 

Plant id 
EAF1 
LF1 
AOD1 
CC1 
HRM 
EAF2 
LF2 
AOD2 
CC2 
HRM 
EAF1 
LF2 
AOD2 
CC2 
HRM 
EAF2 
LF1 
VOD 
CC2 
HRM 
EAF1 
LF1 
VOD 
CC1 
HRM 

Starting time  Duration [min] 
13:50 
14:55 
15:30 
15:55 
16:15 
13:55 
14:50 
15:15 
15:45 
16:25 
14:55 
16:05 
16:35 
17:10 
17:35 
14:50 
15:50 
16:30 
16:50 
18:00 
16:05 
17:20 
17:55 
18:10 
18:30 

60 
30 
20 
15 
10 
50 
20 
25 
30 
40 
65 
25 
30 
20 
20 
55 
35 
15 
20 
25 
70 
30 
15 
15 
30 

Energy consumption 
6000 
3000 
2000 
100 
100 
5000 
2000 
2500 
100 
400 
6500 
2500 
3000 
100 
200 
5500 
3500 
1500 
100 
250 
7000 
3000 
1500 
100 
300 

Table 12: An example of a test production plan 

58 

 
Figure 56: An example of plant configuration file for AST steel industry 

The algorithm computing energy consumptions 

For a given production plan, the algorithm computes the energy consumptions of each configured plant of 
the  steel  industry  and  the  total  energy  consumption  of  all  the  configured  plants.  The  energy 
consumptions are  computed for  each minute of a day, both for each single plant and for all the plants, 
and  stored  in  an  output  excel  file.  The  main  steps  of  the  algorithm  computing  energy  consumption  are 
shown  in  Figure  57.  The  algorithm  has  been  developed  in  C#  as  a  class  library,  i.e.  an  independent 
module that can be integrated also in other software. 

59 

 
 
Figure 57: Algorithm calculating energy consumptions 

An example of the excel output file, produced by the algorithm, is shown in Table 13. 

… 

EAF1 

EAF2 
Time 
0.00 
00:00  0.00 
0.00 
00:01  0.00 
   … 
   … 
16:35  100.00  0.00 
16:36  100.00  0.00 
16:37  100.00  0.00 
16:38  100.00  0.00 
   … 
   … 
0.00 
23:58  0.00 
0.00 
23:59  0.00 

… 

LF1 
LF2 
0.00  0.00 
0.00  0.00 
   … 
   … 
0.00  0.00 
0.00  0.00 
0.00  0.00 
0.00  0.00 
   … 
   … 
0.00  0.00 
0.00  0.00 

LF3 
0.00 
0.00 
   … 
0.00 
0.00 
0.00 
0.00 
   … 
0.00 
0.00 

HRM 
0.00 
0.00 
   … 

TOTAL 
0.00 
0.00 
   … 

AOD1  AOD2  VOD 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
   … 
   … 
   … 
100.00  100.00  10.00  310.00 
0.00 
100.00  100.00  10.00  310.00 
0.00 
100.00  100.00  10.00  310.00 
0.00 
100.00  100.00  10.00  310.00 
0.00 
   … 
   … 
0.00 
0.00 
0.00 
0.00 

   … 
0.00 
0.00 

   … 
0.00 
0.00 

   … 
0.00 
0.00 

Table 13: An example of the excel output consumption file 

The simplified flow sheet model has been applied to a test production plan, represented in Table 12, and 
plots for plant energy consumption and total energy consumption have been generated from the output 
file produced by the model. These plots are shown in Figure 58. 

As  future  works,  this  simplified  flow  sheet  model  can  be  generalized,  by  specializing  the  energy  time 
consumption  of  a  production  item  for  each  particular  plant.  An  approximation  of  the  duration  and  the 
consumption  during  the  time  could  be  deduced  from  historical  data.  Furthermore,  the  energy 
consumption model can be extended and specialized in C# by using polymorphism. 

60 

 
 
 
Figure 58: Energy consumption plots 

61 

 
 
2.2.3.3  Task 3.3: Forecast model development and validation  
This  task  is  dedicated  to  the  development  of  forecast  models  and  the  relevant  software  modules.  The 
following paragraph describes the Deliverable D3.2. 

Different  time  frames  have  been  taken  into  considerations  to  develop  the  models  for  forecasting  the 
power engagement: Medium  (for  the  next  shift and up to 3 days), short (for the next 90 minutes) and 
very short (for the next 15 minutes). The forecasted electricity demand in medium term is used for the 
preparation  of  the  profile  to  be  shared  with  the  energy  provider  the  day  before,  the  short  term  can  be 
used to adjust the energy needs participating in the intra-day market while the very short term is used to 
minimize  the  15  min  peaks,  to  participate in  the  balancing  market  or  to  recover  from  an  unpredictable 
event both internal or external. 

The energy profiles generated through the forecast models is integrated when necessary with information 
from the production schedule in order to evaluate the total energy demand.  

Several approaches have been developed depending on the time frame, the process and the variability of 
its behaviour, and the levels of precision necessary ranging from analytical to statistical approaches. For 
example in EAF the  processing time and  more the energy  demand profile can vary a lot even for same 
type  of  product;  so  in  this  case  a  detailed  profile  might  not  be  reasonable  in  all  cases,  but  an  average 
value for time and energy demand with some security margin gives already a good assumption. In case 
of HRM the profile for each type of products can be modelled with a good precision, because there is not 
much  variance  between  different  products  of  the  same  type  but  the  energy  profile  itself  has  a 
characteristic course. 

The approach based on the analysis of historical data able to extract a profile for each type of products is 
handled by “Profile Extractor” agent. For more details please see the description of Task 6.3. 

As explained in task 3.1, at LSW the EAF heats for production of different steel grades have been grouped 
according to alloy element concentration and tap weight. For each of these groups the average tap-to-tap 
and  power-on  times  were  determined.  Within  this  averaging,  standard  process  interruptions  like 
charging, electrode nippling, tapping, refractory repair etc. were considered. Also, unpredictable process 
interruptions  like  mechanic  or  electric  malfunctions,  waiting  for  casting,  actions  for  regrading  or  even 
dilution of heats due to deviations in chemical analysis are included with average figures determined by a 
statistical evaluation, as shown in  Figure 44  exemplarily for  EAF1.  Figure 59  shows the average  tap-to-
tap and power on times for the different steel grade groups. 

Figure 59: Average tap-to-tap and power on times for LSW steel grade groups 

The  product  of  specific  electrical  energy  consumption  in  kWh/t  and  average  tap  weight  gives  the  total 
electrical energy consumption of a heat for a certain steel grade. The average power demand during the 
production of this heat is given by relating the energy consumption to the average tap-to-tap time. 

Thus for the production schedule of one day, the average power demand of both EAFs can be determined 
by consecutively adding the average power demand for each heat according to the produced steel grade.    

At LSW, a daily production plan with a forecast on the peak load per 60 min intervals is sent to the power 
supplier.  Planned  maintenance  interruptions  of  the  different  production  plants  (Two  EAFs,  two  Rolling 
Mills, four Filter plants, two oxygen plants, minor auxiliary plants) are considered within this forecast.  

Figure 60 shows a typical Day-ahead schedule for two production days. For the schedule on the left hand 
side, all plants are in full operation with the exception of one rolling mill which contributes with a power 
demand of 4 MW. The schedule on the right hand side considers a planned maintenance stop of one EAF 
for two hours (7-8), so that the power demand is reduced by 40 MW.    

62 

606162636465666768690%1%2%3%4%5%6%Time in minAlloy element concentrationTap-to-Tap time Tap weight < 93 tTap weight > 93 t35363738394041424344450%1%2%3%4%5%6%Time in minAlloy element concentrationPower ON timeTap weight < 93 tTap weight > 93 t 
 
 
Figure 60: Day-ahead schedule for two exemplary production days at LSW 

In  case  the  actual  energy  consumption  deviates  from  the  schedule,  additional  power  is  automatically 
bought  or  sold  on  the  balancing  market  by  a  corresponding  transmission  provider.  This  may  result  in 
positive  or  negative  deviations  of  the  energy  costs.  However,  the  effective  costs  cannot  be  predicted 
beforehand, as they depend on the current costs on the balancing market in each time interval. For each 
month, afterwards a detailed calculation of these energy costs is provided for every production day. 

Figure 61 shows for the two exemplary production days the deviation between scheduled power demand 
and  actual  energy  consumption.  For  the  production  day  shown  on  the  left  hand  side,  the  actual 
consumption  was  on  the  average  lower  than  the  scheduled  demand,  so  that  negative  energy  costs 
resulting in a refund from the transmission provider occurred. For the production day on the right hand 
side,  extra  energy  costs  had  to  be  paid  as  the  actual  energy  consumption  was  slightly  higher  than  the 
ordered contingent. 

Figure 61: Deviation between scheduled power demand and actual energy consumption for two 
exemplary production days at LSW 

A  draft  for  a  more  detailed  day  ahead  schedule  has  been  set  up  by  BFI  on  the  basis  of  the  quality 
depended forecast described above. For each production day a schedule of steel grades to be produced at 
the two EAFs is available. Thus the forecast of the energy demand for the whole production day can be 
detailed according to the mean values for specific electric energy consumption, tap weight and tap-to-tap 
time which have been determined  for the different  steel grade groups.  Figure  62  shows exemplarily  for 
EAF  1  the  result  of  such  a  prediction  in  comparison  to  the  values  of  the  day  ahead  schedule  and  the 
actual consumption for the same production days as shown in Figure 60. 

63 

E.ON Vertrieb Deutschland GmbHDay-Ahead-Fahrplan-GeschäfteKunde:Kunde kauftKunde verkauftLSWKommentarLieferdatumStundeEET verkauftEET kauftÜbergabestelle:13.03.20161103.00.0D_EO_NWalzwerk 1 steht (Wochenende)13.03.20162103.00.0Walzwerk 1 steht (Wochenende)13.03.20163103.00.0Walzwerk 1 steht (Wochenende)13.03.20164103.00.0Walzwerk 1 steht (Wochenende)13.03.20165103.00.0Walzwerk 1 steht (Wochenende)13.03.20166103.00.0Walzwerk 1 steht (Wochenende)13.03.20167103.00.0Walzwerk 1 steht (Wochenende)13.03.20168103.00.0Walzwerk 1 steht (Wochenende)13.03.20169103.00.0Walzwerk 1 steht (Wochenende)13.03.201610103.00.0Walzwerk 1 steht (Wochenende)13.03.201611103.00.0Walzwerk 1 steht (Wochenende)13.03.201612103.00.0Walzwerk 1 steht (Wochenende)13.03.201613103.00.0Walzwerk 1 steht (Wochenende)13.03.201614103.00.0Walzwerk 1 steht (Wochenende)13.03.201615103.00.0Walzwerk 1 steht (Wochenende)13.03.201616103.00.0Walzwerk 1 steht (Wochenende)13.03.201617103.00.0Walzwerk 1 steht (Wochenende)13.03.201618103.00.0Walzwerk 1 steht (Wochenende)13.03.201619103.00.0Walzwerk 1 steht (Wochenende)13.03.201620103.00.0Walzwerk 1 steht (Wochenende)13.03.201621103.00.0Walzwerk 1 steht (Wochenende)13.03.201622103.00.0Walzwerk 1 steht (Wochenende)13.03.201623103.00.0Walzwerk 1 steht (Wochenende)13.03.201624103.00.0Walzwerk 1 steht (Wochenende)TOTAL2472.00.0in MWE.ON Vertrieb Deutschland GmbHDay-Ahead-Fahrplan-GeschäfteKunde:Kunde kauftKunde verkauftLSWKommentarLieferdatumStundeEET verkauftEET kauftÜbergabestelle:16.03.20161107.00.0D_EO_NVolllast16.03.20162107.00.0Volllast16.03.20163107.00.0Volllast16.03.20164107.00.0Volllast16.03.20165107.00.0Volllast16.03.20166107.00.0Volllast16.03.2016767.00.0Kurzrep.  E-Ofen 116.03.2016867.00.0Kurzrep.  E-Ofen 116.03.20169103.00.0Walzwerk 2 Rep.16.03.201610103.00.0Walzwerk 2 Rep.16.03.201611103.00.0Walzwerk 2 Rep.16.03.201612103.00.0Walzwerk 2 Rep.16.03.201613103.00.0Walzwerk 2 Rep.16.03.201614103.00.0Walzwerk 2 Rep.16.03.201615107.00.0Volllast16.03.201616107.00.0Volllast16.03.201617107.00.0Volllast16.03.201618107.00.0Volllast16.03.201619107.00.0Volllast16.03.201620107.00.0Volllast16.03.201621107.00.0Volllast16.03.201622107.00.0Volllast16.03.201623107.00.0Volllast16.03.201624107.00.0VolllastTOTAL2464.00.0in MW 
 
 
Figure 62: Predicted, scheduled and actual energy consumption for two exemplary production days at 
LSW 

For  the  production  day  shown  at  the  left  hand  side,  the  actual  energy  consumption  is  lower  than  the 
ordered and also the predicted energy contingent, as an unscheduled interruption of production occurred 
between 3:00 and 5:00. For the production day on the right hand side, the actual energy consumption is 
higher than the ordered contingent, although a maintenance stop for EAF1 was scheduled. In this case, 
the quality dependent prediction would have resulted in an increased energy contingent. 

The procedure to  use  the quality  dependent prediction of the power demand on an hourly basis for the 
day ahead forecast instead of the fixed demand of 40 MW for each EAF was tested for the two exemplary 
production days shown in Figure 60. The result is shown in Figure 63. 

Figure 63: Deviation between quality dependent schedule of power demand and actual energy 
consumption for two exemplary production days at LSW 

It can be seen that quality specific day ahead schedule is in both cases closer to the actual consumption, 
which  results  in  lower  absolute  costs  at  the  balancing  market.  For  the  production  day  on  the  left  hand 
side,  this  would  have  resulted  in  a  lower  refund,  but  for  the  production  day  on  the  right  hand  side  the 
extra  costs  would  have  been  lower  by  a  higher  amount.  The  actually  achievable  savings  by  using  this 
quality specific day ahead forecast would have to be evaluated for a longer time period, in order to decide 
if  this  procedure  can  be  used  in  daily  operation  for  the  day  ahead  forecast  at  LSW.  However,  as 
unpredictable stoppages of the EAF operation and also short term changes in the production plan would 
overrule  a  more  detailed,  quality  specific  day  ahead  planning,  it  was  decided  at  LSW  to  stay  with  the 
existing, more schematic day ahead schedule. 

The  analysis  on  AST  EAFs  data  shows  that  an  appreciable  difference  can  be  seen  only  in  the 
differentiation between austenitic and ferritic steel. Within these classes the variation are very small and 
therefore  is  reasonable  to  use  these  classes  and  the  relative  mean  value  in  the  plant  electricity  needs 
calculation. 

Moreover  to  calculate  the  energy  profiles  on  different  time  scales  a  software  application  has  been 
developed  which  allows  simulating  the  energy  demand  by  combining  the  demand  of  different  sub 
processes, utilising detailed profiles or just average values to a total profile. 

64 

 
 
 
For  the  simplified  user  interaction  it  has  been  decided  to  organise  the  profile  database  for  the  sub 
processes by  a  CSV  (Comma-Separated  Values)  file  which  can  be  loaded  by  the  simulation  system  and 
within Excel for manual review. The CSV file is organised as shown in Table 14. 

Table 14: Example of profile database 

The column “Type” defines the type of the profile. Foreseen are currently:  

•  NUMERIC:  means  that  the  profile  is  based  on  numeric  sample  points,  which  are  then  stretched 
according  to  the  time  interval.  Giving  only  one  value,  allows  modelling of  a  constant  profile  (e.g. 
average value).     

•  EQUATION: allows providing a model based on equation. This possibility has been foreseen in the 
concept, but was not implemented yet because at the moment no equation based models are used 
in the project. But this can be easily extended. 

The column “Version” allows differentiating between different versions of the same product profile for the 
same  process.  This  has  been  foreseen  for  cases  when  for  example  the  same  product  is  treated  slightly 
different  in  the  same  process  producing  different  energy  profiles,  but  where  the  separation  of  the 
distinctions is not possible due to e.g. missing measurements.  

The column “MaterialID” defines the product type. The column “Plant” defines the part of the process or 
machinery  which  is  assigned  with  the  given  profile.  The  column  “Profile”  provides  the  samples  of  the 
energy profile for NUMERIC type or equation for EQUATION type.  

The  profile  database  in  combination  with  schedule  information  allows  us  to  simulate  the  total  energy 
demand. In order to manage the schedule information also an CSV file has been introduced with a format 
as shown in Table 15 

Table 15: Example of schedule data 

The  column  “ProductID”  identifies  the  product,  the  “MaterialID”  refers  to  the  product  type,  the 
“ProfileVersion”  refers  to  the  profile  type  as  described  in  previous  sections.  “Plant”  defines  the 
corresponding  process  or  machinery  and  “StartTime”,  “EndTime”  define  the  start  and  end  time  of  the 
given process. As described before the profile course is stretched according to the duration of the process 
step within the schedule.     

Figure  64  shows  the  screenshot  of  the  of  developed  software  module  which  calculates  the  total  energy 
profile  according  to  the  schedule  and  the  energy  profiles  of  single  processes  and  products.  In  the 
presented  example  two  products  has  been  defined  which  pass  the  process  route  from  SCRAPYARD  to 
HRM.  The  energy  values  and  times  in  this  example  are  artificial  and  are  not  corresponding  to  the  real 
profiles.  But  the  simple  representation  allows  an  easy  understanding  of  the  function  of  this  software 
application.  

By selecting the products and/or plants it is possible to analyse a subset of the whole simulation. Further 
the Aggregation slider in the toolbar allows to set the time base of the result, which means that the result 
will  be  aggregated  e.g.  to  15  min.  scale  building  an  average  value  over  the  chosen  time  period.  This 
allows to create forecasts on different time frames as requested within this Task. 

65 

TypeVersionMaterialIDPlantProfileNUMERIC0600_505_2,3HRM7439,757037843,437318247,11758NUMERICbase600_505_2,3BaseLoad4074,40122NUMERIC0600_495_2,5HRM7701,555949308,8016710786,4875NUMERICbase600_495_2,5BaseLoad3378,90555NUMERIC0569_406_2HRM5683,513887343,41358906,88495NUMERICbase569_406_2BaseLoad3184,47589ProductIDMaterialIDProfileVersionPlantStartTimeEndTime600_505_2,3600_505_2,30HRM14.12.2016 18:5114.12.2016 18:53600_495_2,5600_495_2,5baseBaseLoad14.12.2016 19:1014.12.2016 19:11569_406_2569_406_20HRM14.12.2016 19:1114.12.2016 19:13569_406_2569_406_2baseBaseLoad14.12.2016 19:1314.12.2016 19:13 
 
 
Figure 64: Example of a simulation 

The software  solution  has been  designed  in such  a way, that it can run as standalone application or be 
integrated in the agent system cp. WP6. 

For the short (for the next 90 minutes) and very short (for the next 15 minutes) in particular and based 
on the experience on the AST plant a model able to foresee the melting progresses have been set up. The 
on-line  model  is  based  on  an  off-line  model  calibrated  for  each  type  of  steel  using  the  data  of  several 
heats. The calibration process is done based on Steel grade with similar charge and Operative Practices. 
This  process  is  continuously  updatable  using  the  log  data  during  the  on-line  running.  The  parameters 
correctness is checked based on the melting status according to the schema reported in  

Figure 65. 

Figure 65: EAF model calibration process  

Figure 66: EAF On-line model validation 

If the operator acknowledge for a complete melting the model predictions are correct. In  
Figure  66  a 
typical behavior of energy  and  melting progress is shown together with the  foresee interaction with the 
operator. 

2.2.3.4  Task 3.4: Methods and control-loops to adjust the power engagement according to 

events 

The  activities  carried  out  in  this  task  have  the  objective  to  identify  control  loop  methods  and  to  realize 
them. The goal is to modify the loads in order to adjust the power engagement according to the request. 

The  focus  in  this  task  is  on  the  reduction  of  power  load  in  EAF  process  at  the  industrial  partners  plant 
where EAF is present while the control concept for increasing power load is described in task 4.5. 

The aims of controlling the melting power engagement of the EAFs is to reduce  power peak load within 
the 15 min time interval reducing consequently costs for power supply. This can be achieved by: 

66 

 
 
 
 
 
-  controlling the maximum load, avoiding to exceed the power peak load limit within the 15 min 

time interval 

-  shifting the load to the next time slot, e.g. by delaying the start of a furnace, in order to avoid 

exceeding the peak load 

while: 

-  avoiding process interruptions as much as possible, to keep the productivity high  
-  realizing an energy demand profile as regular as possible 

At LSW the automatic control of the electrical energy consumption via reduction of the power demand of 
the two EAFs has been realised by installation of a smart power load control software, called Smart-LKA 
(Last-Kontroll-Anlage).  The  software  manages  the  power  load  of  the  two  EAFs  in  the  current  and  the 
following  15  min  time  interval.  The  smart  power  load  control  has  been  implemented  according  to  the 
following strategy: 

-  An  adjustable  delay  of  1-5  minutes  regarding  the  start  of  power-on  of  an  EAF  after  charging  of 
the first and the second scrap basket is inserted. This shall allow to shift the power demand to the 
next 15 min interval. The duration of the delay for one EAF is calculated automatically depending 
on  an  analysis  of  the  current  process  status  of  the  second  EAF.  By  this  the  power-on  mode  of 
each  EAF  may  be  postponed  by  up  to  10  minutes  per  heat.  This  gives  the  opportunity  to 
smoothen the power load of the EAFs in each 15 min interval easily. 
In addition, a reduced power demand by automatic step-down of the transformer voltage steps of 
the EAFs is induced, when the peak load limit in a time interval is expected to be exceeded. 

- 

-  Only if unavoidable, a complete shut down of an EAF will be applied. 

This closed loop control concept applied at LSW is shown in Figure 67.  

Figure 67: Closed loop control concept for power demand control (Smart-LKA) at the LSW steel plant 

The described concept for closed loop power engagement control was be implemented online in the steel 
plant  of  LSW  by  the  plant  supplier  company  Primetals  in  May  2017.  Although  also  BFI  worked  on  this 
topic  within  the  project,  it  was  decided  to  purchase  a  commercial  solution  of  Primetals,  as  this  was 
expected  to  be  more  robust.  Also  this  commercial  solution  includes  a  24  hour  7  day  service,  which  is 
crucial for this closed loop power engagement control system, being in permanent operation at the LSW 
plant.  The  system  and  is  currently  in  a  test  and  validation  phase.  It  will  be  put  in  full  operation  after 
finalisation of the DynergySteel project. 

At  AST  a  control  loop  with  the  aim  to  limit  the  15  min  peak  has  been  implemented  In  each  15  min 
interval  the  energy  used  by  whole  plant  and  the  specific  machine  involved  are  continuously  monitored. 
When  the  overall  energy  demand  approach  the  set  limit  actions  on  plasma  and  EAFs  are  taken. 
Depending on the status of the two EAFs and following a predefined priority, the plasma is stopped for a 
predefined  time  interval  and,  if  necessary,  one  or  both  EAFs.  Different  combinations  of  time  interval 
length, priority and time between two stops were tested in order to identify the configuration that has the 
biggest impact on power reduction while less impacting the production.  

67 

 
 
At  ORI  for  each  15  min  interval  the  whole  plant  power  and  the  main  machines  energy  demand  are 
monitored  in  order  to  forecast  the  mean  power  needed  till  the  end  of  the  interval.  When  the  foreseen 
power is over the set limit the EAF is stooped and, if necessary the two LF in order to reach exactly the 
set  limit.  If  in  the  meantime  the  rest  of  the  plant  lower  its  energy  demand  the  stopped  machines  are 
restarted before the end of the interval. 

2.2.3.5  Task 3.5 Scenario analysis to define possible improvements aimed at increasing power 

engagement flexibility 

The following paragraph represents the Deliverable D3.3. 

An  analysis  of  the  flexible  disconnectable  loads  in  steel  plants  has  been  carried  out  taking  into 
consideration  the  specificity  of  the  industrial  partners  but  aiming  to  have  general  remarks  for  the  steel 
companies participation in energy markets. 

In Sept. 2015 a preliminary analysis of flexible disconnectable loads has been performed at LSW together 
with the company of Entelios (former ENERNOC) as a possible provider of flexible energy. The results of 
this  analysis  can  be  easily  applied  to  all  the  other  steel  plants.  Figure  68  shows  the  characteristics  of 
different electrical power consumers and their possibilities to be used as flexible load. 

Figure 68 Characteristics of disconnectable loads 

In general the output of a steel plant must not be affected by flexible loads at all. Only a few plants or 
machines  of  a  steel  mill  have  a  reserve  capacity  and  may  be  used  as  a  flexible  load.  This  has  to  be 
considered in detail at the relevant plant.  

The following flexible loads were in principle identified: 

 
 
 
 
 
 

Electric Arc Furnaces 
pressurized air production 
emergency power supply 
oxygen production 
hall lighting and air conditioning including fan  
cooling towers 

A)  Electric Arc Furnaces 

The  electric  arc  furnaces  are  the  main  power  consumers  of  an  EAF  cycle  steel  plant  and  moreover  the 
main responsible ones for the energy fluctuations. For a precise calculation and forecast of the electricity 
load of the EAF it is necessary to calculate the status of each heat of the EAFs as a function of time. This 
is only possible for a few upcoming heats because several events may occur within the process and the 
sequence for quality control of the heats that influence the tap-to-tap time. When two EAFs are present 
they  can  work  in  a  synchronized  operation  mode  or  alternatively.  Therefore  their  total  energy 
consumption may vary extremely if both of them, only one or none is in operation. That means that three 
principle process situations may occur and that this variation of the EAF status creates an extremely high 
change of the power load. 

68 

 
 
In  principle  the  load  of  an  EAF  is  adjustable  depending  on  the  phase  of  the  process.  In  any  case  a 
reduction  of  the  voltage  of  an  EAF  will  also  reduce  the  output  of  the  steel  melting  shop.  This  might  be 
possible for a short time if the capacity of the EAFs is still high enough to satisfy the overall need of the 
continuous  casters  in  any  case  without  the  risk  of  a  shortfall  of  the  production  chain.  Therefore  the 
priority and possible sequence of the production chain has to be evaluated case by case with respect to 
changing situations of the quality management of the heats and possible stops of the process because of 
unplanned maintenance. 

In Italy EAFs participate normally in the Interruptible Load market. The plant is automatically shut down 
when TSO needs. Normally it last for less than 5 minutes and therefore the impact on the overall process 
is acceptable. Moreover it happens only few times a year. The difficulty is that the rest of the plant should 
not increment the energy use and therefore it needs to be appropriately managed.  

B)  Pressurized Air  

The production of pressurized air is an auxiliary process with high energy consumption. The compressors 
are operated in an on/off-mode. Vessels for pressurized air are used to store the air at a pressure level of 
usually 6 bar, the tolerable variance of the pressure is about +/- 1 bar. Due to the fact that the use of 
pressurized  air is  not  predetermined  and  follows  a more  or  less  stochastic  flow  this  grid  system  cannot 
really be used as a disconnectable load. 

C)  Emergency Power Supply 

In  every  steel  mill  some emergency  power  supply units  are  used  as  a  stand-by  unit.  These  emergency 
power  supply  units  may  be  used  as  an  aggregate  to  generate  positive  control  energy  on  demand  to 
support the grid. 

D)  Oxygen production  

Oxygen  can  be  both  produced  internally  (AST,  LSW)  or  bought  from  an  external  company  (ORI,  HHO). 
There  are  different  processes  for  the  oxygen  production  having  different  characteristics.  The  two  big 
categories are cryogenic and not cryogenic. AST Oxygen production is of the first type while LSW is of the 
second. 

The  main  difference  with  regards  to  participation  in  energy  markets  is  the  start-up  time.  Cryogenic 
processes  need  hours  while  non  cryogenic  need  only  minutes  [34].  At  AST  the  two  Oxygen  production 
plants belong to the cryogenic type and are therefore not suitable to participate in energy markets, while 
LSW uses the non-cryogenic type which is suitable to participate in energy markets.  

Figure 69: Flow chart of the LSW oxygen plant 

Lech-Stahlwerke operates two non-cryogenic oxygen plants for the production of oxygen. Usually 30 m3 
of oxygen are used per t of liquid steel including oxygen as additive for burners. Figure 69 shows the flow 
chart of this plant.  

The oxygen plants have an installed capacity of 2.800 and 3.400 m3/h. The installed power sums up to 
1,7 MW respectively 2,3 MW, in total 4 MW. The oxygen plants have an overall utilization factor of about 
77  %.  The  produced  oxygen  is  stored  at  a  pressure  level  of  about  12  to  30  bar  in  21  vessels  with  a 
storage capacity of each 100 m3. The two oxygen plants may be operated in an on/off-mode, considering 
the status of the oxygen storage (pressure up to 30 bar) and the oxygen demand of the consumers in the 

69 

 
 
steel  plant  (EAFs,  Ladle  preheating,  pressure  12  bar).  Figure  70  shows  exemplarily  the  flow  rate  of 
oxygen that is needed within one heat produced at the EAF No. 1 of LSW. 

Figure 70: Example for the use of oxygen at the electric arc furnace within one heat 

The  specific  use  of  oxygen  per  heat  is  nearly  proportional  to  the  production  of  steel  and  correlates 
directly with the output of the steel plant. Therefore the methods to forecast these two material flows are 
nearly the same. 

These  basic  conditions  enable  the  flexible  mode  of  operation  of  the  oxygen  plant  and  the  use  as  a 
disconnectable  load  depending  on  the  status  of  the  oxygen  storage.  If  a  lack  of  oxygen  might  occur, 
additional  liquid  oxygen  is  available  at  LSW  in  a  separate  storage  tank.  This  liquid  oxygen  may  be 
delivered  on  demand  by  external  suppliers.  That  means  that  several  flexible  measures  are  available  in 
order to assure the needed oxygen supply.  

LSW decided to go ahead and to use the high flexibility of the oxygen plant as a flexible disconnectable 
load.  

In 2017 PVSA-plant 1 was not in full operation because the filling material (granulate) was damaged. A 
revamp of plant no. 1 took place in April 2017 within a downtime at that time. Therefore LSW decided to 
operate  plant  no.  2  as  a  flexible  load.  A  prequalification  of  plant  no.  2  was  performed  together  with 
Entelios (former ENERNOC) as a provider. The operation of the plant as a switchable load was realized in 
January to June 2017.  

E)  hall lighting and air conditioning included fan, cooling towers, filters cycles 

These  loads  have  been  analysed  as  flexible  loads  mainly  for  the  purpose  of  increasing  the  energy 
demand. Please refer to WP4 for more details.  

2.2.4  Work package 4: Development of online devices to react on electric grid events 

Currently  the  energy  market  in  Europe  is  passing  through  a  transition  period.  The  expansion  of 
renewables  in  European  energy  market  needs  new  flexible  peak  load  technologies  and  a  demand  side 
management (see Annex 2). New flexible mechanisms and technologies have to be developed in order to 
contribute to the balancing and security of the power grids. 

The objective of work package 4 is to improve the power load flexibility and control capabilities through 
the implementation of suitable aggregates to increase (on time) instantaneous electric energy demand in 
order to react on grid signals or to switch between gas and electrical energy. 

Initially  the  activity  has  been  focused  in  the  analysis  of  the  compensation  of  natural  gas  by  electricity 
acquired  from  the  secondary  control  market.  For  this  purpose  large  investment  was  planned  for  the 
development  of  additional  loads  in  order  to  optimize  energy  consumption  and  increase  the  flexibility  of 

70 

 
 
the use of these additional loads during the production. Due to the lowering of the revenues for negative 
control  energy  and  in  order  to  maintain  the  profitability  of  the  investment  while  contributing  to  the 
stability  of  the  growing  fluctuant  renewable  energy  in  Europe  like  wind  and  solar  energy,  the  second 
foreseen solution, i.e. implementation of suitable existing aggregates, has been analysed and chosen for 
this purpose. This means that necessary investment are significantly lower.  

2.2.4.1  Task 4.1: Determination of most suitable process stages for enhancing electric loads 
The following paragraph represents the Deliverable D4.1. 

The overall aim of the task is to find suitable aggregates for enhancing the energy demand flexibility at 
HHO  by  analysing  and  eventually  revamping  suitable  aggregates.  This  newly  gained  flexibility  can  be 
commercialized. This commercialization is the main topic of Task 1.3 and also influences the subsequent 
feasibility study in task 4.2. 

In  Europe  is  emerging  the  “power  to  heat”-concept  (P2H)  that  represent  a  possible  and  cost  efficient 
solution  in  order  to  react  on  grid  peaks.  P2H  is  useful  if  additional  negative  control  energy  is  required. 
The regenerative power that is produced as a peak by renewables is free of CO2 and therefore it might 
be useful to use power for the production of heat as well and to substitute fossil fuels. 

P2H has been installed in Germany mainly by power plants that use a combined heat and power process 
for district heating systems. The P2H installations may extend the operation flexibility of these plants and 
may produce heat if a surplus of power is produced in the grid. In this way negative control energy may 
also  be  used  for  a  production  of  heat.  This  concept  is  called  a  hybrid  heating  system  if  fossil  fuels  are 
used  alternately.  Within  this  project  it  has  to  be  investigated  if  this  kind  of  hybrid  heat  generation  is 
useful for a steel mill as well. 

The following step have been carried out: 

  HHO and BFI had several meetings and a factory visit of HHO’s production facilities 
 

Take  account  of  HHO  major  request  that  suitable  aggregates  have  no  direct  influence  on  the 
production  process  and  therefore  focus  on  the  auxiliaries  of  the  plant  (Batch  annealing  and  air 
pre-heating of acid regeneration no option) 

  Several  aggregates  have  been  inspected  as  suitable  for  enhancing  the  electric  energy  demand 

flexibility of HHO 
Economic evaluation of elaborated aggregates 

 

First ideas for enhancing electric loads, already formulated within the proposal of the project, have been 
the additional heating of the batch annealing (see Figure 71) and the additional generation and storage of 
compressed  air.  Another  aggregate  which  has  been  discussed  was  the  air  pre-heating  of  the  acid 
regeneration (Figure 72). 

Figure 71: Concept for electrically heating batch annealing 

71 

 
 
Figure 72: Concept for additionally pre-heating the air for acid regeneration 

In  the  first  discussions  it  became  obvious  that  HHO  will  be  more  satisfied  with  a  solution  not  directly 
involved  within  the  production  process.  Thus,  during  the  inspection  of  the  plant  the  focus  areas  have 
been the auxiliary aggregates which offer potential for the envisaged enhancements as well. 

Several potentially interesting aggregates have been inspected and were taken into closer consideration 
and analysis. These comprise: 

Feed water preheating 
Energy distribution station cooling 
Enhanced cooling power of cooling tower 
Increased flow rate into the cooling tower via pumps 

  Compressed air network 
 
 
 
 
  Hall lighting 
  Hot water preparation 

The  decision  to  mainly  analyse  auxiliary  aggregates  meant  also  to  bury  the  concepts  for  the  batch 
annealing  and  the  acid  regeneration.  The  batch  annealing  did  not  seem  to  have  the  capability  of  being 
used  in  conventional  production  which  diminished  the  usefulness  of  this  solution.  Furthermore,  the 
integration and development of a new batch annealing has been found to be too expensive considering its 
usage and the risks going together with  it. The concept for the air pre-heating of the  acid  regeneration 
has been rejected due to implementation risks and  limitations. The process  itself cannot be  rapidly and 
easily  adapted  or  held  in  a  continuous  state  when  switching  between  electrical  air  pre-heating  and  the 
heating  by  gas.  Thereby,  unexpected  process  failures  can  occur  and  process  safety  cannot  be 
guaranteed.  

Figure  73  shows  the  result  of  the  feasibility  analysis  of  several  possible  aggregates.  The  second  row 
represents the category of the load (V – load shifting, E-energy carrier surrogate and Z-additional load). 
The  third  row  represents  the  basic  energy  carrier  (Gas,  Steam  and  Electricity).  The  fourth  row  the 
process  and  the  last  rows  represent  the  results  of  the  feasibility  (easy,  medium  and  difficult)  and  the 
potential electricity consumption (small, medium and high). This lead to a list of the possible aggregates 
to be further evaluated. 

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Figure 73: Development of online devices to react on electric grid events 

2.2.4.2  Task 4.2: Feasibility study of enhanced electrical load for the most promising process 

stage and estimation of economic benefits 
The following paragraph represents the Deliverable D4.2. 

Several process stages have been defined in the Task 4.1 for further evaluation taking into account:  

Potential for load flexibility in kW 
Practicality and applicability of adaptations (Process boundary conditions, KO-criteria) 
Economic assessment of necessary aggregate adaptions (Budget for adaptations) 

 
 
 
  Overall potential estimation 

To refine this analysis several subtasks have been carried out by HHO and BFI concerning contacting in-
house  experts  to  facilitate  a  more  accurate  estimation  of  these  parameters,  especially  for  economic 
ratings. 

The  level  of  the  revenues  for  negative  secondary  control  had  increased  from  2008  to  2010,  then 
decreased  in  2011  and  stayed  at  constant  level  in  2012  at  100T€/MW·a  (see  Figure  74)  with  the 
constantly increasing of the amount of volatile renewable energy (Figure 75). In 2013, when the proposal 
was  written,  the  preliminary  economical  evaluation showed  a  possible profitability  when  participating in 
the  negative  secondary  power  control  (that  means  additional  use  of  electrical  load)  constructing  new 
aggregates.  

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Figure 74: Potential of the revenues for the secondary control power from 2008 to 2012 

Figure 75: comparison Balancing reserve and renewable Energy capacity (wind and solar energy) 

The construction of the new aggregates was evaluated asking offers to suppliers. Meanwhile  the number 
of  companies  prequalified  to  supply  secondary  balancing  power  has  consistently  increased  (see  Figure 
76), consequently the profitability for the negative secondary control power, which was at the beginning 
of the project around 100T€/MW·a in the year 2012 (see  Figure 74) decrease significantly up to 60% of 
the  average  of  secondary  control  power  (positive  and  negative)  between  a  year  2009  and  2015 
(approximatively from 82000 €/MW·a in 2009 to 33000 €/MW·a in the year 2015, see Figure 74, Figure 
77 and Figure 78.  

In  2009  an  average  revenues  of  secondary  control  power  (positive  and  negative  balancing)  was 
evaluated as 82000 €/MW while in 2015 it decrease to 33000 €/MW. 

A detailed economical investigation of revenues and necessary investments costs has been carried out.  

The payback period of a total investment of € 200,000, required for the purchase of two loads units each 
with a capacity of 588 kW to achieve the goal of 1 MW, considering an annual profit of about € 100,000 is 
2 year. The falling back of the yearly revenues increased the payback period to more than 5 years, which 
was no longer attractive to HHO. The cancellation of this big investment caused the reductions in overall 
project costs for equipment, maintenance and staff. 

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Figure 76: Number of pre-qualified suppliers of secondary balancing power increased from 2007 up to 
2015 

Figure 77: Revenues of average prices (positive and negative balancing) in secondary are reduced 
about 53% between 2013 and 2015 

75 

 
 
 
Figure 78: Market size by segment from 2008 to 2015 and comparison of the revenues from 2009 to 
2015 (since 2009, revenue has declined by 60% in secondary balancing) 

Because  of  these  unpredictable  changes  of  the  revenues  the  final  choice  is  to  extend  with  additional 
automation possibilities several smaller already existing aggregates as initially foreseen in the proposal as 
one of the possibilities.  

The following aggregates have been selected as suitable loads for this purpose. 

  Hall lighting (160 kW) 
  Air conditioning included fan (150 kW) 
  Cooling towers (up to 1 MW) 
 

Filters cycles (about 100-200 kW) 

Investment  is  necessary  for  each  aggregate  in  order  to  connect  it  to  the  LAN  and  to  allow  a  remote 
control of the device. Moreover a flexible load control station dedicated to the overall management inside 
HHO  plant  of  all  the  connected  aggregate  should  be  installed.  This  control  station  will  be  used  also  for 
managing the information exchange between HHO and the intermediaries (i.e. EnerNoc or RWE). 

The solution foreseen is furthermore easier to apply to other plants in Europe due to lower investments 
while assuring the possibility to actively participate in the balancing market through an intermediary and 
increase efficiency of the energy consumption inside the plant. 

2.2.4.3  Task 4.3: Development, construction and implementation of additional electric loads 
Figure  79  shows  the  schema  of  the  planned  connections  of  the  aggregates  and  the  management 
computers  in  order  to  enable  the  participation  of  HHO  on  the  balancing  market  (both  negative  and 
positive).  The  virtual  machine  in  Figure  79  has  been  installed  and  put  in  operation  for  this  purpose  at 
HHO. The aggregates manager software is implemented in the virtual machine to ensure the availability 
of the power demand, which will come from the intermediary. 

For  the  Hardware  side  the  availability  loads  of  the  aggregates  are  ensured  by  communication  modules 
through  TCP/IP  as  depicted  in  Figure  80:  Connections  between  the  High  load  Calculator  (left)  and  the 
additional aggregates (right) and the conditions of the availabilities are derived from the Table 16. 

As depicted in  Figure 79  all the available loads will be  summed and the  total sum  of  loads must satisfy 
the power demand of the intermediary. 

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Figure 79: Planning of connections between additional loads and management computers for 
participation in the electricity market 

Figure 80: Connections between the High load Calculator (left) and the additional aggregates (right) 

For  each  aggregate  Table  16  gives  the  installed  capacity,  the  flexibility  of  the  load  to  be  connected  or 
disconnected, the type of the connection and needed hardware for his enabling or disabling, the number 
of needed interfaces for this realization and the conditions of load availability at HHO. 

77 

 
 
 
 
 
Table 16: List of the flexible additional loads and their constraints for the participation for the 
Electricity Balancing Market at HHO 

Due to the decision to use only the existing loads, the problem of the position and secure installation and 
of  the  robust  process-integration  of  a  new  aggregate  is  no  longer  relevant.  For  this  reason  only  the 
equipment alteration costs and boundary conditions for construction have been considered. 

In  order  to  enable  the  communication  among  the  HLC,  the  virtual  machine  and  the  participating 
aggregates,  14  Hardware  module  are  needed  (see  Table  16).  The  main  parts  of  this  hardware  are 
multimax  modules  (see  Figure  80  and  Table  16),  The  and  the  accessory  parts  are  the  LAN-connection 
cables. 

2.2.4.4  Task 4.4: Modelling of electric load availability 
The following paragraph represents the Deliverable D4.3. 

The modelling of the additional loads is essentially based on the load constraints and their cost functions. 
Each cost function  must take  into  account  all  the  load  constraints in  Table 16  and evaluate the  optimal 
decision for the enabling or disabling weighting the contributions according to the cost of each constraint. 
Furthermore, due to the different characteristics of each load, the agent based modelling lead to optimal 
use  of  these  characteristics  of  each  aggregate  (more  details  about  the  Agent-Based  modelling  are 
depicted in the deliverable D4.5 in the task 4.5). 

For this reason  each  additional load  module gives his availability to the load Manager  (Agent Additional 
Loads  Manager  in  Figure  82  related  to  deliverable  D4.5.)  based  on  its  own  calculated  cost  function 
independently from the other loads.  

The precise  formulation of each  cost  function will be done  in task 4.6  before  the implementation  of the 
control  system  in  order  to  ensure  that  all  possible  constraints  are  considered  in  the  overall  method 
implementation  in  WP6;  an  easier  method  could  be  considered  (i.e.:  the  sum  of  all  minimal  actual 
available load of each aggregate) to react to the energy demand. 

Figure 81 shows one example of parameter extraction for the Agent-Based modelling of cooling towers.  

78 

Additional LoadsInstalledCapacity (kW) ConnectionDisconnectionConnection artNumber of interfacesConditions of additional load availabilityAir conditioning HV-Lenne(If feasible) setpoints can be raised / lowered100YESYESCoupling HLC -external I / O level -> coupling relay1Of which approx. 60 kW of cooling and 40 kW of ventilation / cooling is controlled by the speed controlDisconnecting when P > 50% of the install capacityWhat condition data can be supplied by the air conditioning system?Air conditioning offices cooling tower(If feasible) setpoints can be raised / lowered20YESYESCoupling HLC -external I / O level -> coupling relay115-20 kW of cold production is speed-regulatedAbschalaten, if p> 50%Which condition data can the air-conditioner supply?Air conditioners generally with fan(If feasible) setpoints can be raised / lowered (Is only roughly estimated)Potential for all air-conditioning systems -YESYESCoupling HLC -external I / O level -> coupling relayAir conditioning Switch house Beize(If feasible) setpoints can be lowered25YESNoCoupling HLC -External I / O level -> SPS3Setpoint reduction is possible. Possible setpoint adjustment is not possible, since in high load phases corresponding waste heat in the switch rooms is establishedAir Conditioning Switch House Miba(If feasible) setpoints can be lowered160YESNoCoupling HLC -External I / O level -> SPS1Setpoint reduction is possible. Possible setpoint adjustment is not possible, since in high load phases corresponding waste heat in the switch rooms is establishedCooling towersSetpoints can be lowered. The potential can only be determined in practical tests and is strongly dependent on the weather conditions (temperature / humidity)800YESNoCoupling HLC -External I / O level -> SPS3Setpoint reduction is possible Setpoint adjustment is not possible, since high heat loads can cause corresponding waste heatInterfaces to cooling tower (old + new) rolling mill and pump controlFilter cycles7 filters are provided here, the start cycles of which are to be shifted into the next period segment with correspondingly present load prognoses. Potential depending on the active filter approx. 100,200 kW average value of 140 kW flushing time about 30 minutes140YEStemporaryCoupling HLC -External I / O level -> SPS1Advance the rinsing process is possibleDelaying of a rinsing cycle which has not yet been started is possibleInterruption of an already running rinsing process is not possibleThe limit value of the minimum duration in the settling tank has to be clarifiedControl unit in SPS must be adaptedHall lighting4 halls at 40 kW -> potential approx. 160 kWEnsure the connection of lighting / work safety160YESNoCoupling HLC -external I / O level -> coupling relay4Can only take place during the day with sufficient daylightTotal Power Consumption1305kWTotal number of interfaces14Interface modules Number of Modules7logical modulesList of the additional flexible loads and their availability constraints at HHO 
 
Figure 81: Example of the extraction of the modelling parameter from the table of loads availabilities 
and constraints 

2.2.4.5  Task 4.5: Development of control concepts 
The following paragraph represents the Deliverable D4.4. 

Each additional load is firstly considered as an independent entity able to be controlled individually due to 
e.g. the difference in capacity. All the loads are then integrated in a controlling system in order to be able 
to  participate  in  the  electricity  market.  The  agent  based  control  has  been  individuated  as  a  suitable 
technology to manage all the involved aggregates and efficiently ensure the availability of the asked load. 

Figure  82    shows  the  Agent-Based  control  to  be  implemented  at  HHO.  For  this  realization  two  types  of 
agent  were  identified.  The  Aggregate  Agent  whose  task  is  the  internal  management  of  each  aggregate 
constraints,  the  evaluation  of  the  cost  function,  and  the  communication  to  Manager  Agent  of  its  load 
availability.  

The  Manager  Agent  manages  the  overall  loads  availabilities  integrating  the  information  received  from 
each  aggregate  agent.  Furthermore  it  receives  the  information  about  the  energy  demand  (Power 
demand, availability demand, prediction period) and use them as constraints to his own cost function to 
ensure the availability of the load demand.  

The  Manager  Agent  has  then  the  duty  to  calculate  the  new  optimal  scheduling  for  the  production 
planning. The production scheduling closes the control loop by giving the new 15 min forecast. 

79 

 
 
Figure 82: Agent based control concept to react on the electricity market 

2.2.4.6  Task 4.6: Implementation and evaluation of developed concept for additional electrical 

loads 

The following paragraphs represent the Deliverable 4.5. 

Commissioning of the developed control concept  

For  the  commissioning of the  developed  control  concept  at  HHO  two  aggregates  have  been  considered. 
The first are the cooling towers with a capacity of 800 kW (cooling towers, Pumps, fans). The second is 
the air conditioning switch House Miba with the capacity of 160 kW (see Figure 83). 

For security and reliability reasons, the additional load management software was installed a few months 
before  the  commissioning.  The  purpose  was  to  collect  the  data  for  the  offline  examinations  in  order  to 
improve the software robustness.  

Once this phase has been validated, a software commissioning test has been carried out at HHO. For this 
purpose  it  was  important  to  consider  the  constraints  of  each  device  particularly  those  cooling  towers 
which  have  two  levels  of  adding  loads.  The  first  level  for  the  pumps  and  the  second  level  for  cooling 
tower fans (see Figure 84). 

Figure 85 shows the cooling towers at HHO during the production. 

80 

 
 
Figure 83: implemented solution at HHO with two devices (Air conditioning, Cooling Towers) 

Figure 84: Switching stage of the cooling towers at HHO 

81 

 
 
 
Figure 85: Cooling towers during the production at HHO 

Implemented component system approach for the additional loads management with Labview 

Figure 86 shows the interactions between the provider, the additional load control software, the database 
and the additional loads at HHO. This interaction works as follows. At the beginning of the chain we have 
the provider that sends to the component system approach (Devices Manager) a load request. Once the 
request  is  made,  the  software  will  be  responsible  for  finding  the  cheapest  combination  to  satisfy  the 
demand  based  on  the  information  received  from  the  additional  charges  on  their  availability.  Once  the 
best  combination  found  the  Device  Manager  will  write  in  the  database  the  commands  for  activation  or 
deactivation of additional loads. 

As  shows  in  Figure  87  and  Figure  88  the  implemented  component  system  approach  (Software  agent 
based at HHO) for the management of the available additional loads at HHO is composed by two Agents. 
The first one is the main agent for the management of additional loads and the second is the load agent 
(each  load  is  responsible  for  evaluating  and transmitting its  availability).  To  satisfy  the  load  demand  of 
the  provider  each  load  agent  evaluates  his  availability  and  gives  his  current  capacity  to  the  loads 
manager  for  the  overall  availabilities  evaluation.  Figure  87,  Figure  88  and  Figure  89  shows  the  Human 
Machine Interface (HMI) of the implemented agent based control of the additional load at HHO. 

Figure 86: simplified diagram implementation of the Devices Manager Software at HHO 

82 

 
 
 
Agent loads manager 

As  shown  in  the  Figure  87  the  load  management  agent  can  be  divided  into  two  parts.  The  first  part 
concerns  the  provider  (Provider  Informations)  and  the  second  part  concerns  the  additional  charges 
(Devices Informations). 

For  the  provider  information  we  have  the  name  of  the  provider,  a  manual  button  to  simulate    the  load 
request of the provider, an input value of the load required in kW, an input value to define the duration of 
the availability of the load, a light indicator that turns lime-green when the load request is validated and 
the load availability condition is filled, otherwise the light remains gray (load required not available). 

Devices  Informations  allows  you  to  enter  the  potential  loads  to  satisfy  the  provider's  request  (name, 
power  consumption)  and  a  light  indicating  the  active  (lime-green)  or  inactive  (dark  green)  state  of  the 
load. 

Cost function 

Once  the  different  potential  loads  have  sent  their  availabilities,  the  costs  function  choose  the  most 
appropriate  combination  for  the  provider  demand  based  on  the  additional  loads  availabilities  to  avoid 
unnecessary energy consumption. 

The  production  planning  does  not  affect  at  the  moment  the  cost  function  calculation  since  at  HHO  only 
the  additional  loads  are  planned  not  to  disturb  the  operation  of  the  production  and  to  increase  the 
required reliability of availability. But for a higher profitability including the optimization of the planning of 
the  use  of  the  loads  during  the  production  the  software  developed  in  the  task  6.4  (WP6)  can  be 
associated to the cost function of the devices manager in order to give the 15min constraints feedback of 
the production planning (see Figure 82). 

Agent additional load 

Figure 88 shows the availability of each additional load agent online and the Figure 89 shows the sum of 
the available loads which allows the agent load manager to find the best combination to satisfy optimally 
the requirement of the provider. 

Figure 87: implemented loads manager (Main Agent) at HHO 

83 

 
 
Figure 88: Implemented agents of additional loads with a sampling rate of 2s with the enabling and 
disabling of the cooling towers 

Figure 89: Total available load for the additional devices at HHO with a sampling rate of 2s 

84 

 
 
 
Since the current income for participation in the energy market is considered from HHO unattractive for 
the  moment  (25000  €  /  MW  per  year  according  to  HHO),  HHO  has  postpone  to  put  the  software  into 
service once the revenues will become more attractive. 

2.2.5  Work  package  5:  Strategies  for  production  redefinition  to  align  and  re-align 

actual power engagement with the forecast 

2.2.5.1  Task 5.1: Analysis of the actual procedures and methodologies used in the steel 

companies to calculate and re-schedule a “day-ahead”-forecast of the production  

The forecast of needed power is provided to the intermediator on an hour basis, while for the calculation 
a shift or an hour is taken into consideration depending on the variability of the processes, the detail of 
the production plan taken into  consideration  or the fact of being  part of  a consortium  for the electricity 
purchase that acts as a pre-balancing before accessing the external electricity market. 

At LSW, currently a weekly production plan for the two lines in the electric steelmaking plant is defined 
and broken down to the three shifts of each production day. For each shift it is indicated if quality steel 
grades  (QST)  or  structural  steel  grades  (BST)  shall  be  produced.  Planned  maintenance  shifts  are 
indicated  separately  (e.g.  ‘Gefäß’  for  EAF  vessel  change  or  ‘SGA’  for  the  continuous  casting  plant). 
Further planned downtimes are indicated for each of the two production lines separately. Figure 90 shows 
a typical example of such a weekly production plan. 

Figure 90: Weekly production plan of Lech-Stahlwerke 

From the production plan, a day-ahead forecast of the electrical energy demand with the expected peak 
load  per  60  min  intervals  for  one  day  is  derived.  The  influences  of  the  different  steel  grades  to  be 
produced, the quality of the scrap or process interruptions cause deviations, are not yet considered within 
the forecast. 

Figure  91  shows  step  by  step  the  development  of  the  method  for  predicting  the  electrical  energy 
consumption by HHO. 

The working layer allocation and the average energy needs of all involved units are recorded in an Excel 
table  (monthly/weekly/15  min.).  This  Excel  table  gives  the  energy  profile  of  the  forecast,  which  is 
transmitted in a standardized interface to energy purchase planning.  

Figure 92 presents an example of the inputs of working layer allocation with the average Energy needs of 
all involved aggregates in one month in HHO. Each column have three workgroups per day (the first one 
in the morning “Früh”, The second one in the afternoon “Spät” and the last one in the evening “Nacht”). 
The  “X”  in  the  table  means  that  the  aggregate  “M”  (in  total  8  aggregates  from  M01  to  M08)  will  be 
activated for the corresponding  workgroup.  The  "H" and "X1" are  special programs that are  different to 
the  normal  program.  Based  on  all  active  aggregates  per  day  the  average  consumption  need  can  be 
calculated. The result of this calculation is the  profile which  is presented  in  the  Figure 93, which  shows 
the forecast profile of a month in HHO. 

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• Measurements 
Availability 
•
Planned Delays 
•
Planned Stoppages 
•

Central data model for 
Electric energy 
Consumption analysis 

Energy profile 
(15 min. basis) 

Forecast: 
Standardized interface 
and data transfer to 
responsible energy 
provider 

Figure 91: Method of calculation of energy consumption profile for the forecast 

86 

 
 
Figure 92: Forecast tool for all working layer with each aggregate in HHO 

Figure 93: forecast profile of a month in HHO 

ORI is part of a buying  electricity  consortium  established among more than 20 steel factories located a 
few kilometres away from each other. The foreseen electricity needs are communicated to the consortium 
very  early  mainly  considering  flat  profile  a  part  from  process  downtime  and  the  changes  are 
communicated  whenever  they  happen.  The  modifications  are  mainly  due  to  the  foreseen  activities  for 
maintenance  or  other  stopping  activities  with  a  duration  between  4  and  8  hours  and  the  holidays  (see 
Figure 94).  The participation of several plant  in a pool  lead to a natural balancing of the overall energy 
demand and therefore no accurate profile was requested by the aggregator.  The planning is done using 

87 

 
 
 
an Excel file based on hours slots and for each of them the booked MW are indicated. In case of variation 
a flag for each hour indicate that a modification exists and which is the new value. 

Figure 94: Booked electricity at ORI when a 8 hours maintenance is foreseen 

At AST a detailed production plan for the steel shop is prepared (see Figure 95). The profile of electricity 
needed is then calculated on this basis adding the contribution from the rest of the plant. A profile of one 
week production is shown in Figure 96. The profile is based on the sum of the electricity needed for each 
process. In particular for EAF and HSM a profile is used based on the evaluation of historical data. For the 
other processes a flat contribution is considered. 

Figure 95: Example of AST steel shop production plan 

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Figure 96: Example of foreseen electricity needs 

In  order  to  perform  a  substantial  step  ahead,  energy  constraints  must  be 
included dynamically into the optimization procedure, coming within the scope of 
the multi-objective optimization procedures. This way,  continuous re-scheduling 
becoming possible, reaching to the following expected benefits:  

  Optimal  grouping,  assignment,  sequencing  and  timing  of  heats  ensure  continuous  caster  feed, 

better integration and coordination between different production processes. 

  Minimize make span and waiting times, thus energy losses too. 
  Maintenance planning. 
  Energy aware scheduling.  
  Possible integration and coordination with the hot rolling mill scheduling in order to increase ratio 

of hot charged slabs and thus reduce natural gas consumption in reheating furnace. 

In the next tasks of this Work package, methods, algorithms and procedures will be realized and tested 
to reach this objective. 

2.2.5.2  Task 5.2: Definition of metrics to measure and estimate the deviations between 

booked and consumed electrical energy  

The following paragraph represents the Deliverable D5.1. 

A  deep  discussion  has  been  developed  among  the  partners  on  the  suitable  ways  to  quantify  both  the 
deviations  between  the  forecasted  (i.e.  the  energy  bought  at  the  day-ahead 
market)  and  actual  consumption  (which  is  derived  from  the  current  production 
plan and from the flowsheet model developed in WP3), and the potential effects 
that  such  deviations  can  lead,  taking  into  account  the  different  regulations  and 
opportunities provided by the Italian and German electricity markets. 

Figure 97: Melt 
Shop Scheduling 
Optimizatin Schema 

Also the need to have indexes [35], which are suitable to validate the proposed 
method  on  historical  data,  has  been  faced,  taking  into  account  that,  when  processing  historical  data, 
some  information  on  the  network  status  could  be  available,  which  are  normally  not  available  when  the 
system runs on-line. 

At  Lech-Stahlwerke,  AST  and  ORI  the  costs  for  the  operation  of  the  distribution  grid  and  balancing  the 
power network are calculated for each day and every time slot of 15 minutes (in Germany) or 1 hour (in 
Italy) retrospectively at the end of each month. The steel mill will be charged with a fee for the operation 
of  the  balancing  work  that  is  done  by  the  grid  operator.  Figure  98,  Figure  99  and  Figure  37  shows  an 
example of the deviations between used and booked power load at LSW, AST and ORI respectively. Table 
17 shows the possible balance situations that might occur each day. During last year the situation in Italy 
for the quantification of the revenues/fees is changed as reported in WP1. 

Table 17: Balancing costs for operating the grid at Lech-Stahlwerke 

89 

 
 
Figure 98: Example of Lech-Stahlwerke showing the balance deviations of the booked power load to 
the measured power load for 15 min intervals of one day (16.03.2016) 

90 

1 (-)2 (+)The grid has to sell power in order to be balanced.The grid has to buy power in order to be balanced.A (-)A1 (-/-)A2 (-/+)The power load is lower than ordered (the day before). The steel mill does not need the ordered energy.The steel mill gets a negative balance fee for not using the ordered energy.The steel mill gets a positive balance fee for not using the ordered energy.B (+)B1 (+/-)B2 (+/+)The power load is higher than ordered (the day before). The steel mill needs additional energy.The steel mill gets a positive balance fee for using more energy than ordered.The steel mill gets a negative balance fee for using more energy than ordered.Situation of the gridSituation of the steel mill 
 
 
Figure 99: Example of AST showing the balance deviations of the booked power load (blue line) to the 
measured power load (read line) of five days 

Figure 100: Effective electricity demand compared to booked one in a day at ORI 

The simplest way to quantify the deviation in a given time period is a sort of weighted error between the 
forecasted consumption and actual consumption derived from the current production plan. Let us define 
∆T  the  typical  time  interval  which  represents  the  basis  for  the  Energy  market  (typically  ∆T=1h, 
∆T=15min). Any period of time comprised between an initial and a final time instant, named respectively 
T0  and  Tf  (e.g.  1  day,  6  hours,  etc..),  can  be  divided  into  N  time  slots  of  identical  duration,  where 
obviously N=(Tf-T0)/∆T. Let us indicate with Ci and 𝐶𝑖̂ , respectively, the consumptions of electrical energy 
that are computed according to the flowsheet model and the ones that have been forecasted in the past 
for the i-th time slot. One can then define on this time slot an absolute error ei and a relative error εi as 
follows: 

ei = |Cî − Ci| 

εi =

|Cî−Ci|
Ci

A  first  trivial  way  to  quantify  the  deviation  between  actual  (or  derived  from  production  plan)  and 
forecasted  (or  bought  at  the  day-ahead  market)  consumption  values  is  provided  by  the  average  of  the 
absolute or relative errors, which are computed as follows [36]: 

M1 =

N
∑
ei
i=1
Tf−T0

=

ei

∑

N
i=1
N

 M2 =

N
∑
εi
i=1
Tf−T0

=

εi

∑

N
i=1
N

The reason for keeping both these indexes lies in the different purposes by which they can be applied. M2 
is  supposed  to  be  more  suitable  in  the  validation  and  development  phases,  in  order,  for  instance,  to 
evaluate  the  accuracy  of  the  models,  as  it  weights  in  a  different  way  the  error  depending  on  its 
percentage  value  with  respect  to  the  real  consumption.  M1  is  more  suitable  for  optimization  purposes, 
e.g.  when  adjustments  of  the  production  schedule  are  searched,  in  order  to  match  at  best  the  actual 

91 

 
 
 
 
 
 
 
consumption values with the consumption plan that has been prepared the day before and on the basis of 
which interventions on the market have been made.  

In  order  to  evaluate  the  effect  of  the  deviations  between  estimated  and  actual  consumptions,  one  can 
also  evaluate  the  “cost”  or  possible  revenues  associated  to  such  deviations  [37].  In  effect,  it  has  been 
already  underlined  that,  if  the  deviation  between  actual  and  estimated  consumptions  allows  to  partly 
recover an imbalanced situation of the network, such deviation, although heavy, can have a null or even 
a positive economic effect. 

For  instance,  when  the  network  suffers  a  lack  of  supply  with  respect to  the  demand  of  energy  and  the 
company  is  consuming  less  energy  than  forecasted  or  when,  in  the  case  of  a  positive  imbalance  of  the 
network (i.e. an excess of energy), the company can increase the load, economic consequences for the 
company could be low or null, or they might even result in a chance for a profit (according to the specific 
imbalance regulation in force). This also depends on the sell/buy price, which must be compared to the 
price  that  has  been  previously  sustained  for  purchasing  that  amount  of  energy.  If  no  sale  of  excess 
energy  is  possible,  in  any  case  the  availability  to  “devolve”  some  amount  of  the  acquired  energy  to 
counteract and compensate the network imbalance can receive an award, that must be always compared 
to the cost sustained to have that amount of energy available. On the other hand, if the network suffers 
for an excessive availability of energy, offering a further amount of energy is penalizing for the company. 
On  the  contrary,  if  the  company  has  a  further  demand  for  energy,  such  energy  can  be  much  cheaper 
than in standard conditions and the company can produce at a lower cost. 

The general policy of the company with respect to the energy availability can also affect the importance 
attributed  to  the  deviations  between  estimated  and  actual  consumption:  e.g.  a  company  that  always 
prefers  to  guarantee  that  enough  energy  is  available  for  the  production,  tends  to  attribute  a  greater 
importance  to  negative  values  of  the deviation,  as  they  refer  to the  conditions  where  there is  a  lack of 
energy  compared  to  the  demand  of  the  production.  This  can  also  be  related  to  the  flexibility  of  the 
production cycle and the time of reaction, the time by which the potential countermeasures are expected 
to have an effect. 

To sum up, in order to evaluate the effect of the deviations between estimated and actual consumption, 
the sign of the  deviation  𝑠𝑒=sgn(𝐶𝑖̂ − 𝐶𝑖), is relevant as well  as the sign  sn  of  the network  imbalance. In 
the  following  sn=+  refers  to  the  situation  where  there  is  a  surplus  of  energy  production  on  the  local 
network while sn=- refers to the situation where there is a demand of energy which exceeds the  energy 
supply.  If  these  two  signs  are  equal,  the  imbalance  has  heavier  consequences  with  respect  to  the 
opposite case. Clearly, when evaluating the effect of the deviations, also its absolute value is relevant.  

Therefore,  a  further  measure  for  the  deviation  between  actual  and  forecasted  consumption  values  can 
thus  be  depicted  as  a  weighted  sum  of  the  absolute  deviations  on  each  considered  time  interval,  as 
follows: 

where wi is a weighting factor that is computed according to the regulation of each country. An example 
is provided in the following matrix W, shown in Figure 101, referring to the current Italian regulation. 

𝑀3 =

𝑁
∑ 𝑤𝑖 ∙ 𝑒𝑖
𝑖=1
𝑇𝑓 − 𝑇0

=

𝑁
𝑖=1

∑ 𝑤𝑖 ∙ 𝑒𝑖
𝑁

se 

+ 

w++ 

w-+ 

sn 

+ 

- 

- 

w+- 

w-- 

Figure 101: Weight matrix W 

The matrix W exemplifies four possible cases: 

  Case ++: the network has a surplus of energy and the company is consuming less energy than 
the  forecasted.  Therefore,  the  not  consumed  energy  that  has  been  bought  at  the  day-ahead 
market  (MGP),  is  sold  by  the  company  at  the  Dispatching  Services  Market  (MSD)  at  the  mean 
purchase price, which is lower than the MGP price, being paid. 

  Case +-: the network has a surplus of energy and the company is consuming more energy than 
the  forecasted.  Therefore,  the  company  buys  the  missing  energy  at  the  Dispatching  Services 

92 

 
 
 
 
Market (MSD) at the mean purchase price, which is lower than the MGP price, that the company 
would have paid the day ahead. 

  Case -+: the network has a lack of energy and the company is consuming less energy than the 
forecasted. Therefore, the not consumed energy that has been bought at the day-ahead market 
(MGP),  is  sold  by  the  company  at  the  Dispatching  Services  Market  (MSD)  at  the  mean  selling 
price, which is greater than the MGP price, being paid. 

  Case --: the network has a lack of energy and the company is consuming more energy than the 
forecasted. Therefore, the company buys the missing energy at the  Dispatching Services Market 
(MSD)  at  the  mean  selling  price,  which  is  greater  than  the  MGP  price,  that  the  company  would 
have paid the day ahead. 

The cases ++ and --, where sn and se are both positive or both negative, are indeed the worst cases and 
represent loss cases. Vice versa, the case -+ is a case where the company could make profits, while the 
case +- is a case where the company could have savings with respect to the MGP. 

The basic idea is that M3 should represent a profit indicator, with the following meaning: 

  M3 > 0 indicates a profit situation; the greater is the indicator, the greater the profit is. 
  M3 = 0 indicates a balanced situation. 
  M3 < 0 indicates a loss situation.  

The elements of matrix W have been evaluated in terms of the average selling and purchase prices (from 
TERNA) at the Dispatching Services Market and of the average price at day-ahead market. 

The average MGP price in the period from January 2014 to December 2015 is about 52 €/MWh. 

The average selling and purchase prices have been computed for each macro zone, North and South, and 
are shown in Figure 102. 

    Avg  

    MSD 

    Avg  

    MSD 

   Price 

Selling 

Purchase 

   Price 

Selling 

Purchase 

Sn 

+ 

- 

21.22 

21.22 

106.56 

106.56 

Sn 

+ 

- 

10.45 

10.45 

112.32 

112.32 

(a) 

(b) 

Figure 102: Mean selling and purchase prices related to the North (a) and South (b)   macro zone 

The weight elements of matrix W have been evaluated with the following formula: 

𝒘∗∗ =

(𝑺𝒆𝒍𝒍𝒊𝒏𝒈_𝑷𝒓𝒊𝒄𝒆 − 𝑷𝒖𝒓𝒄𝒉𝒂𝒔𝒆_𝑷𝒓𝒊𝒄𝒆)
𝐀𝐯𝐞𝐫𝐚𝐠𝐞 𝐌𝐆𝐏

Selling_Price is the selling price of energy at MSD, which appears only when Se is positive. 

Purchase_Price is the purchase price of energy at MSD or MGP, which appears only when Se is negative. 

An example of the calculations for the north macro zone is as follows: 

w++ =

(21.22−52) 
52

 = - 0.6 

w+− =

(0− 21.22) 
52

 = - 0.4 

w−+ =

(106.56−52) 
52

 = 1.0 

w−− =

(0− 106.56) 
52

 = - 2.0 

93 

 
 
 
 
 
 
 
 
 
 
 
 
The matrix W has been evaluated for each macro zone, North and South, and both of them are shown in 
Figure 103. 

     Se 

+ 

- 

       Se 

+ 

- 

Sn 

+ 

- 

-0.6 

-0.4 

-2 

1 

(a) 

Sn 

+ 

- 

-0.8 

-0.2 

1.16 

-2.16 

(b) 

Figure 103: Matrix W related to the North (a) and South (b) macro zone 

When training the whole system by processing historical data, both the sn and some average information 
on costs, prices and penalties can be available in order to compile the above reported matrix. When an 
optimization  strategy  for  the  future  must  be  elaborated,  sn  is  not  known.  In  this  case,  a  worst  case 
philosophy  can  be  adopted,  by  assuming  sn=-.  As  an  alternative,  one  can  use  a  model  to  attribute  a 
probability to the two signs of sn based e.g. the Markov model or some historical data. The main limit of 
KPI3 (M3) lies in the fact that the sign of imbalance of the network is unknown in real time and for this 
reason another KPI has been introduced which takes into account the economic aspects. 

KPI4 (M4) is defined in formula (1). It is composed by the sum of two terms: the first term quantifies the 
deviation between booked consumptions and actual consumptions  in a given time period, the second one 
takes into account the maximum load for the i-th slot. The slots where actual consumption is near to the 
maximum load, with a threshold of 𝜏, are penalized.  

In practice, minimizing this KPI we want on one hand to ensure that the energy consumed is as close as 
possible to the booked energy and on the other hand to penalize those consumptions that are near to the 
virtual peak load. The regulations for the Italian market provide that the monthly bill is calculated on the 
basis of maximum consumption reached during the month. Therefore, the purpose of the second term of 
the  KPI  is  to  penalize  the  slot  whose  consumption  is  near  or  exceeds  the  highest  peak  had  during  that 
month before the current time slot. Of course, the penalty is higher if such a peak is exceeded. Thus, the 
second term represents an estimation of the cost of the fee due to exceeding the peak so far reached: it 
has been built so that only the highest slot consumption near or exceeding the peak load is considered, 
by means of the max function.  

𝑀4 =

∑ 𝑀𝐺𝑃𝑃𝑟𝑖𝑐𝑒𝑖

𝑁
𝑖=1

𝑁

 ∗|𝐶𝑖̂  −𝐶𝑖 | 

 +   max𝑖=1..,𝑁 𝜇 (𝐶𝑖 )          

(1) 

𝜇(𝑥)

= {

0 ,         𝑥 < 𝑀𝐿𝑖  − 𝜏
𝑓𝑒𝑒 ∗ 𝑀𝐿𝑖 , 𝑀𝐿𝑖  − 𝜏 ≤ 𝑥 ≤ 𝑀𝐿𝑖 
𝑓𝑒𝑒 ∗ 𝑥,   𝑥 ≥ 𝑀𝐿𝑖 

where MGPPricei is the price at the MGP (Day-Ahead Market) for the i-th slot and it is measured in MWh, 
N represents the number of time slots of identical duration  (typically 15 minutes), Ci is the consumption 
of electrical energy computed according  to the flowsheet model (Actual) for the i-th  time slot and  𝐶𝑖̂    is 
the forecasted consumption of electrical energy, that is the booked at the Day-Ahead Market for the i-th 
time slot. Both consumptions are measured in MWh. 

MLi is the maximum Load over all time slots of the current month previous to the i-th time slot, while 𝜏 is 
the threshold on the maximum load depending on consumption models accuracy for the time slot.  Both 
MLi and 𝜏 are measured in MWh. 

Finally the parameter  𝑓𝑒𝑒  is the penalty due  to  exceeding  the maximum reached peak  and its measure 
unit  is  €/MWh.  Therefore,  the  unit  measure  of  the KPI4  is  euro  and  this  KPI  defines  an  economic  index 
that should be minimized in order to optimize the control of energy consumption. 

94 

 
 
 
 
 
 
 
 
 
 
            
 
 
 
 
 
 
 
 
 
 
 
 
 
2.2.5.3  Task 5.3: Implementation of KPIs to be used within a calculation algorithm  
The four proposed KPIs have been implemented. The first two KPIs take into account only the deviation 
between  actual  and  forecasted  consumption  values.  The  third  KPI  should  be  suitable  to  the  market 
representation  but  until  now  it  is  not  usable  for  scheduling  optimization  because  the  imbalance  sign  is 
actually unknown in real time. The last KPI, composed by the sum of two terms quantifying the deviation 
between booked consumptions and actual consumptions in a given time period,  and taking into account 
the maximum load for the actual slot, is the most appropriate one because it is able to express in terms 
of  euro  the  effectiveness  of  the  production  plan  and  it  will  be  used  also  for  the  optimization  of  the 
production re-scheduling. The  implemented KPI  will be validated  later when they will be embedded into 
the Agent System. 

2.2.5.4  Task 5.4: Production Re-scheduling Criteria and relevant validation 
The following paragraph represents the Deliverable D5.2 

The  objective  of this  task is  to  find  an  optimal  production  schedule  of  a part of  a  steel  making  process 
that  minimizes  the  KPI4  described  above  while  satisfying  production  constraints.  The  developed  model 
has been inspired from the work of Hadera et al. [38] using mathematical programing with Mixed-Integer 
Linear Programing (MILP) and implemented in MATLAB environment. In this work, it is assumed that the 
electricity  consumption  of  any  production  task  is  constant  over  the  time  span  of  the  task:  it  is  a 
reasonable assumption since exact modelling would increase the complexity without obtaining significant 
benefits as demonstrated in [39][40][41]. 

The inputs of the model are the set of stages (e.g. EAF, AOD, LF, CCM … ), the set of machines at each 
stage  (e.g.  EAF1,  EAF2,  EAF3  for  stage  EAF…),  the  list  of  products  to  be  processed  and  some  timing 
parameters needed by the scheduling constraints. A general situation is modelled, where any number of 
machines is possible  at  each stage,  so for  example there might be one EAF, two AOD, two LF and four 
CC. 

The  scheduling  model  should  satisfy  several  production  constraints  deriving  from  the  steel  making 
process, starting  from  the melting phase in  the  EAF stage and ending with the heat casting in the CCM 
stage.  Moreover,  the  process  passes  through  intermediate  stages,  such  as  AOD,  in  order  to  reduce  the 
carbon content of the molten steel, and LF, in order to adjust the chemistry and the temperature before 
the final stage of casting. 

Some  considerations  on  the  steel  making  process  are  given  in  order  to  introduce  the  nature  of  the 
scheduling constraints.   

There  is  a  certain  time tm,m′  needed  to  transport  products  from  machine  m  to  machine  m’.  In  order  to 
avoid  a  too  large  decrease  of  the  temperature  of  the  molten  steel,  the  waiting  time  spent  by  a  heat  p 
max. Specific rules about the sequences of 
after stage st is restricted by a maximum allowed delay time  tp,st
heats  are  applied  in  the  continuous-casting  stage.  Heats  are  divided  in  groups  and  all  the  heats  of  the 
same  group  need  to  be  assigned  to  the  same  continuous  cast  and  are  casted  subsequently  without 
setup has  to  be  performed  in 
waiting  times.  For  all  stages,  except  for  CC,  a  machine  specific  setup  tm
advance  in  order  to  carry  out  the  processing  of  the  next  heat.  On  the  other  hand,  for  the  continuous 
casting,  a  setup  is  needed  only  for  the  first  heat  of  the  sequence  or  group  of  heats.  The  processing 
duration θp,m depends both on the product p and on the machine m where it is processed. Among input 
parameters there is the power consumption hp,m, which is specific of products and machines. 

Task start times variables are related to the energy part of the model, leading to the computation of the 
total electricity consumption within a given time interval.  

These  variables are used to find the  contribution  of a task to the electricity consumption within a given 
time interval by considering four possible cases: 

  Case 1: a task is processed entirely within the time slot. 
  Case 2: a task starts before and finishes within the time slot. 
  Case 3: a task starts within and finishes after the time slot. 
  Case 4: a task over-spans the full time slot. 

95 

 
Figure 104: Task contribution to electricity consumption of a time slot 

Figure  104  describes  the  four  possible  cases  considered  to  compute  a  task  consumption  of  energy  in  a 
time  slot:  in  the  light  blue  boxes  the  process  duration  within  the  time  slot  s-th  is  indicated,  by  putting 
into relation the starting or final time of a heat p on machine m with the lower,  τs−1 , and upper bound, 
τs , of the time slot. For example, when the task is entirely processed within the time slot, as in case 1, 
the complete process duration of the heat on the machine θp,m  is accounted for. 

The  overall  electricity  consumption  qs   within  the  s-th  time  interval  can  be  computed  by  summing  the 
contributions of all tasks in the interval. The total electricity consumption  qs is needed for the evaluation 
and optimization of KPI. 

The specific electricity consumption  hp,m  of process  p on  machine m  is known  for  every  process  and for 
every machine. This consumption is assumed to be constant over time, introducing an approximation that 
creates an acceptable error. 

Using the variables a, b, c, d (see Annex 4) is possible to calculate the electricity consumption qs of each 
time slot s as follows  

𝑞𝑠 =

1
60

∙ ∑ ℎ𝑝,𝑚 ∙ (𝑎𝑝,𝑚,𝑠𝑡,𝑠 ∙ 𝜃𝑝,𝑚 + 𝑏𝑝,𝑚,𝑠𝑡,𝑠
𝑝,𝑠𝑡,𝑚∈𝑀(𝑠𝑡)

+   𝑐𝑝,𝑚,𝑠𝑡,𝑠 +   𝑑𝑝,𝑚,𝑠𝑡,𝑠) 

where the sum is divided by the number of minutes in a the time slots, which in our case is 60. 

Having defined the electricity consumption  qs it is possible to compute the objective function, which is a 
combination of the function M4 defined before and a function that penalizes the scheduling delay, namely 
the sum of all starting times. Therefore the objective function f is as follows 

𝑓 = 𝑘 ∙ ∑ 𝑡𝑝,𝑚

𝑠

  +  

𝑝,𝑚

𝑠

∑ 𝑀𝐺𝑃𝑃𝑟𝑖𝑐𝑒𝑠 ∙ |𝑞̂𝑠 − 𝑞𝑠|
|𝑆𝐿|

+ max
𝑠

 𝛾(𝑞𝑠) 

Where |SL| is the number of times slot and 

𝛾(𝑞𝑠) = {

0         𝑞𝑠 < 𝑀𝐿𝑠 
𝜙 ∗ 𝑞𝑠   𝑞𝑠 ≥ 𝑀𝐿𝑠 

The  parameter  ϕ  is  the  fee  for  exceeding  the  maximum  peak  as  described  in  the  previous  section. 
Moreover k is a parameter  that tunes the tradeoff  between the total scheduling time and the quality of 
the solution in terms of the KPI. 

96 

 
 
 
 
This expression of the objective function f is nonlinear as it contains modules and a maximum calculation. 
In order to obtain a linear expression of the objective function, further equations are needed. To linearize 
modules |q̂ s − qs|, real variables μs are introduced, so that  μs = |q̂ s − qs|.   

The max over the cases function can be linearized by introducing the variable M representing the max of 
the electricity consumption over all the time slots. If this maximum is bigger than the maximum load ML, 
then the real variable Γ = M ∗ ϕ, otherwise, Γ = 0.  

The objective function becomes thus 

𝑓 = 𝑘 ∙ ∑ 𝑡𝑝,𝑚

𝑠

  +  

𝑝,𝑚

𝑠

∑ 𝑀𝐺𝑃𝑠 ∙ 𝜇𝑠
|𝑆𝐿|

+ Γ 

In  order  to  account  for  the  possibility  of  machines  of  the  same  type  having  different  consumption  hp,m 
another  term  is  added  to  the  objective  function,  namely  the  total  consumption.  Therefore  the  final 
objective function is 

𝑓 = 𝑘 ∙ ∑ 𝑡𝑝,𝑚

𝑠

  +  

𝑝,𝑚

𝑠

∑ 𝑀𝐺𝑃𝑠 ∙ 𝜇𝑠
|𝑆𝐿|

+ ℎ ∙ ∑ 𝑞𝑠

+ Γ 

𝑠

The  number  of  variables  and  constraints  in  the  formulation  is  sensibly  high,  and  therefore  the  problem 
cannot be solved in an acceptable running time if the input is too big. An iterative algorithm is designed 
to overcome this problem, when the set of products too large. 

The set of products is divided into smaller subsets, which are iteratively processed: the reduced products 
set is given as input  to the MILP described above. The solution is stored and the booked consumptions 
1. 
vector  is  updated  to  account  for  the  consumptions  qs
Moreover  the  maximum  assigned  start  time  L(m)  of  each  machine  m  is  used  as  a  lower  bound  for  the 
start times on m in the next iteration by adding further  constraints . 

1  of  the  first  iterations.  Hence  q̂ s  becomes  q̂ s − qs

A new solution is found and the process is repeated until all orders are assigned (or until there is no more 
time available for the scheduling). 

Recall that the model selects the machines to be used by each heat and outputs the starting time of all 
the input heats on the associated machines in order to minimize the KPI.  

Examples of input data to the model are shown next. A schema example of the melt section for stainless-
steel is represented in Figure 105, where stages and corresponding machines are represented.  

Figure 105: Melt section for stainless-steel production process 

Table  18  shows  the  list  of  products  to  be  processed,  while  Table  19  defines  the  processing  times  and 
electricity  consumption  for  each  machine.  Table  20,  Table  21,  and  Table  22  contain  timing  parameters 
needed  by  the  scheduling  constraints,  respectively  setup  times,  transportation  times  and  maximum 
waiting times after stages. All data tables are taken from literature [38].  

97 

 
 
Group 
HG1 
HG2 

HG3 
HG4 

HG5 

Heat (product) 
P1-P3 
P4-P7 

P8-P12 
P13-P16 

P17-P20 

Table 18: Heat group definition 

P1-P20 

EAF1, EAF2 

AOD1, AOD2 

LF1, LF2 

85 [min] 
85 [MW] 

8 [min] 
2 [MW] 

45 [min] 
2   [MW] 

CC1, CC2 

60 [min] 
7   [MW] 

Table 19: Processing times and electricity consumption 

Machine 
EAF1, EAF2 
AOD1, AOD2 

Setup time 
9 
5 

LF1 
LF2 

CC1 

CC2 

15 
5 

50 

70 

Table 20: Setup times [min] 

EAF1 

EAF2 

AOD1 

AOD2 

LF1 

LF2 

AOD1 

AOD2 

LF1 

LF2 

CC1 

CC2 

10 

25 

25 

10 

4 

20 

20 

4 

20 

45 

45 

20 

Table 21: Transportation times [min] 

P1-P20 

ST1 

60 

ST2 

90 

ST3 

60 

Table 22: Maximum waiting times after stages 

A number of tests have been performed using the parameters in the previous tables in order to evaluate 
the algorithm performances and tune the parameters for the multi-objective function.  

The chosen solver for the MILP is a freeware program lp_solve. It is the best performing software among 
the free of license software and performs better than the inbuilt Matlab solvers too. 

The  MGP  price  chosen  is  constant  over  all  time  slots  and  equals  123€  per  consumed  MW.  The  chosen 
maximum  load  is  133  MW.  The  fee  for  exceeding  the  maximum  load  is  chosen  as  Φ=1528€  per  extra 
MW. 

For all the tests the maximum allowed running time has been set to around 30 minutes. In this situation 
not all the 20 heats from the previous tables can be scheduled in one day if the KPI has a high weight in 
the objective function: therefore the value of k in (30) is chosen bigger to increase the weight of the total 
scheduling time (namely the first term of the objective function). This yields a solutions scheduling all the 
heats in one day and provides relatively good quality solutions in terms of the KPI. 

98 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 23 shows the starting process time of the products on the assigned machines 

P1 
P2 
P3 
P4 
P5 
P6 
P7 
P8 
P9 
P10 
P11 
P12 
P13 
P14 
P15 
P16 
P17 
P18 
P19 
P20 

EAF2 

4 
99 

371 
465 
559 

980 

EAF1 
189 
283 
377 
0 
95 

556 
650 
744 
838 
932 
462 

1017 

1111 
1205 

AOD1 
284 
378 
472 
95 
190 

661 
745 
839 
933 
1027 
557 

1112 
1125 
1206 
1300 

AOD2 

149 
209 

516 
576 
659,9 

LF1 
324 
390 
484 
127 
202 

759 
819 
879 
945 
1039 
569 

1124 
1184 
1244 
1312 

LF2 

247 
307 

614 
674 
734 

Table 23: Processing time schedule 

CC1 
429 
489 
549 

864 
924 
984 
1044 
1104 

1197 
1257 
1317 
1377 

CC2 

232 
292 
352 
412 

659 
719 
779 
839 

Table 24 shows for each time slot the booked and consumed energy and the market price  

S1 
S2 
S3 
S4 
S5 
S6 
S7 
S8 
S9 
S10 
S11 
S12 
S13 
S14 
S15 
S16 
S17 
S18 
S19 
S20 
S21 
S22 
S23 
S24 

Booked 
73,09 
72,70 
64,49 
80,86 
81,39 
84,83 
113,26 
71,53 
113,36 
91,64 
84,04 
127,75 
96,56 
105,30 
120,27 
97,02 
111,03 
111,76 
104,11 
120,81 
131,77 
100,12 
132,89 
108,61 

Consumed 
164,05 
141,84 
171,76 
80,86 
81,25 
94,69 
150,09 
170,03 
178,77 
153,06 
137,86 
94,03 
81,71 
80,78 
97,82 
80,81 
149,52 
157,52 
80,45 
84,12 
86,54 
51,14 
8,23 
6,66 

Difference 
-90,95 
-69,13 
-107,27 
0 
0,13 
-9,85 
-36,83 
-98,50 
-65,40 
-61,41 
-53,81 
33,72 
14,84 
24,52 
22,45 
16,21 
-38,49 
-45,75 
23,66 
36,68 
45,22 
48,97 
124,65 
101,95 

Market Price 

123,1 
122 
199 
129 
111 
139 
122 
123,1 
111 
124 
142 
133 
135 
109 
109 
190 
133 
122 
120 
199 
118 
110 
123,1 
123,1 

Table 24: Energy consumption 

Even though this are solutions of a relatively good quality, it is possible to achieve better solutions quality 
with faster and more stable optimizing software, such as the MILP optimizer Gurobi. This better software 
does not have a free industrial license though.  

99 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2.2.5.5  Task 5.5: Short term power scheduling on line model development and relevant 

validation 

The  developed  model  is  general  enough  to  deal  with  extraordinary  events,  such  as  a  machine  failure 
during the daily process. In this case, to obtain a rescheduling, it is possible to restrict the inputs of the 
model  to  the  remaining  products  (not  yet  processed),  taking  into  account  only  the  available  machines 
and the remaining time slots of the time production range. The algorithm is run again with this restricted 
input and outputs a new schedule of products. 

2.2.6  Work  package  6:  Dynamical  access  to  the  power  market  through  an  Agent 

System aimed to support the Decision Makers  

This  WP  aims  to  develop  an  integrated  system  based  on  the  concept  of  multi-agents  enabling  a  more 
flexible system which gives the possibility to better exchange information between processes and energy 
markets  in  order  to  build  the  basis  for  a  continuous  adaptation  of  the  production  based  on  the 
opportunity offered from the market and the state of the processes. 

2.2.6.1  Task 6.1: System requirements and automation landscape 
This chapter represents the D6.1. 

The  aim  of  this  task  is  to  define  the  requirements  originated  from  point  of  view  of  a  steel  producer. 
Starting from  the analysis of the automation systems and the database infrastructure different possible 
application scenarios have been investigated in order to identify broad spectrum of requirements for the 
system to be developed.  

Due  to  security  reasons  (from  both  perspectives  IT  security  and  confidentiality  with  respect  to 
competitors) the low level details of the configuration are accessible only to the partners involved in the 
corresponding  use  case  and  are  not  presented  within  the  report.  In  order  to  enable  the  common 
development  and  interchange  between  project  partners  the  description  is  made  on  abstract  and 
generalised level. 

The automation landscape found in the plants is in line  with  Figure 106  where the different level of the 
automation  systems,  both  related  to  processes  and  electricity  management,  are  reported.  As  already 
described  in  the  previous  chapters  the  electricity  part  have  been  (or  will  be  in  2016)  enhanced  by  the 
acquisition of new smart meters to be integrated in the overall architecture. 

Figure 106: Automation landscape 

From  the  databases  point  of  view  the  founded  situation  was  more  heterogeneous  due  to  the  several 
processes  that  should  be  taken  into  consideration,  the  dimension  of  the  plants  and  the  level  of 
implementation. In fact, as shown in the previous chapters, the level of data details available vary from 
one plant to another and therefore a special attention will be put during implementation phase in order to 
realize  the  more  general  approach  as  possible.  Both  BFI  and  CSM  have  already  developed  in  other 
projects a general approach to the management of heterogeneous data sources and this knowhow will be 
applied in this project. 

In  Figure  107  the  general  schema  for  the  integration  of  electricity  data  with  process  data  is  shown 
highlighting the necessary brick for the integration. The Level2 Databases are considered as input to the 

100 

 
 
system  and,  as  depicted  above,  specific  software  module  will  be  developed  in  order  to  decouple 
DYNERGYSteel from the specific installation. 

One  fundamental  point  is  the  synchronization  of  data  time  stamp  coming  from  different  sources  and 
therefore  it  is  necessary  to  add  an  NTP  server  (where  not  available).  Moreover  available  data  have 
several time frames depending whether are part of automation, monitoring or planning systems. For the 
purpose  of  the  project  and  depending  on  which  electricity  markets  is  approaching  is  necessary  to 
maintain  the  same  level  of  granularity.  For  the  purpose  of  accessing  the  day-ahead  markets  it  is  also 
useful to have specific data related to products instead of times. We found that they are not available in 
all  the  plants  and  therefore  when  necessary  to  the  specific  use  case  the  opportune  elaboration  will  be 
added. 

Figure 107: Data management architecture 

The  industrial  partners  identified  first  of  all  general  goals  for  the  systems  covering  major  roadblocks  in 
the current industrial systems. Following goals has been determined as most important: 

 

Integration: one of the major roadblocks during the development of new process optimisations 
is  missing  availability  of  information  from  different process  stages  and  automation  levels.  Often 
such information are encapsulated within proprietary systems without open interfaces. Therefore 
one of the major goals within this project is to break the limitation of the information exchange 
by  providing  a  flexible  agent  based  system  allowing  an  easy  integration  of  different  information 
sources within the common framework.  

  Extendibility: a very important goal for  DYNERGYSteel system is the possibility to easy extend 
the provided solution. In practice often occurs the situation where for a known problem a solution 
has been developed. After some time of usage or based on changes in the process the users gain 
the  knowledge  about  additional  optimisation  potential  for  initial  solution.  To  integrate  these 
additional  potentials  usually  a  complete  redesign  of  the  system  is  required  producing  a  high 
barrier for the practical  realisation.  Therefore one  of the major goals is to provide the ability to 
extend the solution by new functionalities without the need of changes within the initial approach.  

  Customization:  the  easiness  of  integration  of  DYNERGYSteel  system  within  the  plant  is 

important from mainly two point of view: 

o  User interface should be in line with the specific industrial habits with the aim of helping 

the introduction and the customer loyalty of the new system 

o 

interface  to  data  should  be  easy  to  manage  and  independent  from  the  core  part  of  the 
system.  

In addition to the global goals discussions with plant experts have been performed. The intention was to 
analyse the general user needs and expectations on the system with focus to problems originating from 
the practical experience of the users. In this context following specific goals has been identified: 

 

Energy cost optimization by more efficient planning with the inclusion of existing data from all the 
production processes. 

101 

 
 
  Comprehensive consideration of the production process by the decision for rescheduling and/or 

main or ancillary process conduction to catch energy markets opportunities. 

  Decision-making for the production planning by means of strategies taking into account energy, 

resources, delivery time, throughput and costs. 

Complimentary to the goals investigation we analysed different possible application scenarios in order to 
identify the specific requirements for  the  system.  The scenarios have been investigated in collaboration 
with plant experts and refer to typical optimisation challenges. Following use cases has been considered 
covering different aspects of the DYNERGYSteel system. 

  Production Planning evaluation: The base for the following use case is to have a clear idea of the 
energy  necessary  for  the  production  based  on  the  actual  planning.  For  this  purpose  based  on 
historic data and/or specific model the electricity necessary should be evaluated.  

•  Rescheduling: Target schedule can be modified to catch markets opportunities. Data for replanning 
is accessed, using material flow (rest), order situation and plant state. The system generates a new 
optimised schedule which is close as possible to the original one. Some possible measures are: 

  Modification of EAF conduction:  

increase the power for complete actually production 

o 
o  decrease  the  power  and  increase  the  residence  time  without  compromising  the 

engagement in casting machine 

 

Task change:  

o  Select another compatible production for current heat in EAF that requires less energy 

to produce (ex: change steel grade) 

o  Select  another  compatible  production  that  requires  less  energy  (ex:  change  heat 

routing or final product) 

  Machines switching on or off:  

o  delay the scheduled tasks without affecting the quality of current production. 
o 

change actually production ( ex: turn off VD and change final quality, turn off Mill and 
produce billet for stock, turn off EAF if production is started recently or if it’s possible 
manage the heat in LF)  

The integration of such a new system necessitates the possibility to interact with several systems which 
works  at  different  time  scales:  at  the  automation  the  time  is  the  milliseconds,  the  production  is  in 
seconds and minutes, and at higher level for programming and logistics are hours and days. The system 
should  be  able  to  manage  these  different  time  scales  with  the  right  intercommunication  services  and 
agents.  

Moreover the system will be used in Brown Field, the system should not disturb the existing system and 
slow down the production due to the wait.  

Based on this analysis we specified the functional requirements including following aspects: 

 

”Yellow  pages”:  specifies  the  requirements  for  “yellow  pages”  functionality  within  the  agent 
system. Yellow  pages provide the  ability  to  the agents to search  for  each other or to search for 
services provided by agents.  

  Software agents: specifies the requirements for the agents themselves  
  Agent  marketplace:  specifies  requirements  for  infrastructure  which  regulates  the  agent 

negotiations 

  Communication  with  external  systems:  specifies the  requirements for the integration  of the 

agent system in foreign environment  

  Communication  between  installations:  specifies  the  requirements  for  the  collaboration  of 

different installations of the system 

  Communication  between  agents  and  the  market  place:  specifies  the  requirements  for 

negotiation procedure 

  Persistence: specifies the requirements for the possibility to store data generated by the agents 
  Licensing: specifies the requirements for ensuring the legality of the installation 
  Configuration: specifies the requirements for the management of agent configuration 
  Logging:  specifies  the  requirements  for  the  possibility  to  store  log  messages  generated  by 

agents  

  User interface: specifies the requirements for interaction with operators 
  Debug: specifies the requirements for the monitoring of agent communication allowing searching 

bugs. 

  Recovery: : specifies the requirements for recovery of the system after a failure 

For non-functional requirements we considered following aspects: 

102 

 
 
  Performance: the response time for the communication between agents 
 
Interoperability: the ability to run in inhomogeneous IT environment 
  Availability, Reliability, Recoverability, Robustness, Fault Tolerance: stability of operation 
  Usability: ability to integrate new functions in the system 
  Extendibility, Modifiability: ability to add new agents at run-time 
  Testability: ability to perform automated tests 
  Deployment and Commissioning: requirements on installation procedure 
  Maintainability: requirements on documentation and programming conventions  
  Security: ability to encrypt the communication between external agents 
  Scalability: ability to handle a growing demand on work  

2.2.6.2  Task 6.2: System architecture and solution strategies  
This chapter represents the D6.2. 

The  goal  of  this  task  is  the  development  of  the  design  for  the  agent  software  system,  including  the 
communication  infrastructure,  the  interfaces  and  structural  details  of  the  solution.  Additionally  different 
approaches  for  the  trading  strategies  are  investigated  within  this  task  and  solution  strategies  are 
developed.  

A multi agent system (MAS) is a software system which consists of independent proactive entities, called 
agents. The objective of these agents is to find a solution for their own goals through communication with 
other agents. The agent based solutions are often used for complex problems which are very difficult to 
handle  by  single  monolithic  system.  The  idea  behind  the  agent  based  approach  is  to  break  down  a 
complex problem into a quantity of small problems which are easier to handle by single piece of software. 

That means MAS is a paradigm of a software design which aims to subdivide complex software solutions 
in  simple  pieces  of  software,  where  the  complexity  arises  through  not  coordinated  communication  of 
these pieces.  

Generally a MAS consists of number of agents and their environment. An agent in a multi agent system is 
defined by some important characteristics: 

  Autonomy: the agents are independent from each other and follow always their own objectives 
  Local  view:  agents  don’t  have  the  global  view  of  a  problem;  they  always  try  to  solve  their 

individual problems  

  Decentralisation: there is no central instance which control the communication and activities of 

the agents 

Further the difference is made between those types of agents: 

  Passive Agents: agents without own goals, this type of agent provide typically some services to 

other agents 

  Active Agents: agents with own goals, those agents try to find a solution for the own problem 

by asking the others for services, an active agent can also offer services to other agents.  

  Cognitive Agents: agents which change their behaviour in dependence to the current situation. 

This type of agents have typically an internal state  

As  already  mentioned  in  order  to  handle  the  communication  between  the  different  agents  an  agent 
environment  is  required.  Within  out  conceptual  design  this  environment  is  called  Agent  Management 
System (AMS) cp. Figure 108. 

103 

 
Figure 108: Scheme of the agent management system design 

In our approach we followed  the  idea of “everything is an agent”. This means that in our concept even 
structural components of the system are represented by dedicated agents. These agents utilize the same 
communication methods as the normal functional agents.  

In doing so we differentiate between following types: 

  Structural agents: This type of agents represents the structural components of the system. To 

this type belong following agents: 

o  Agent store: this is an agent which manages the available agents inside the system. The 
goal of this agent is to analyze the existing libraries and to collect the information about 
the availability of the agents. This agent is also responsible for the creation of the other 
agents. 

o  Agent administration: is an agent  which manages the living agents. That means after 
the creation of  an agent it is registered within agent administration and is consequently 
findable  by  other  agents.  Additionally 
the 
communication  interface  allowing  existing  agents  to  exchange  messages  between  each 
other’s.  Furthermore  agent  administration  provides  the  functionality  of  “yellow  pages” 
allowing the agent to search for available services and for existing agents.  

the  agent  administration  provides 

o  View  store:  is  an  agent  which  manages  visual  components  of  the  agents.  That  mean 
each agent can have different GUI (graphical user interface) components which represent 
the  state  of  an  agent.  “View  store”  collects  automatically  the  information  about  the 
available user interfaces and is responsible for their administration.  

  System Agents: This type of agents represents some special system relevant functionalities. To 

this type belong following agents: 

o  Debug agent: This agent provides the possibility to analyze the communication between 
other  agents.  Once  it  is  started  it  collects  the  information  about  messages  exchanged 
between  the  different  agents  and  shows  this  information  graphically  allowing  users  to 
analyze and to debug the function of the other agents. 

o  Log agent: provides the logging capability to other agents. It receives the log messages 

of other agents and takes care for their storage. 

o  Configuration  agent:  This  agent  manages  the  configuration  of  other  agents.  It  is 
responsible  for  the  storage  of  the  configuration  during  the  shutdown  phase  and  for 
loading of the configuration during startup phase.  

104 

 
 
o  External  communication  agent:  This  agent  provides  an  interface  for  external 
application,  which  can  be  either  external  agent  or  a  coupled  agent  system.  It  manages 
the  communication  between  the  external  systems  and  internal  agents  including  the 
communication protocols and data coding.  

  Functional Agents: represent the user defined agents which provide the functional scope of the 

system according to the usage scenario. 

For  the  realization  of  structural  and  system  agents  dedicated  interfaces  are  foreseen  inside  the  AMS. 
These interfaces allow the structural and system agents to couple with the AMS and to offer the desired 
functionalities.  This  concept  provides  a  high  flexibility  as  far  through  this  loose  coupling  the  system 
relevant agents can be easily replaced when new functionalities are required.  

Besides  the  conceptual  design  of  the  agent  system  also  some  trading  strategies  has  been  investigated 
within this task. This project aims to utilize the trading strategies of virtual markets in order to optimize 
or to balance the usage of the electrical power according to the situation on electrical power market or in 
dependence on requests of the power providers.  

The  idea  behind  the  usage  of  virtual  market  strategies  for  optimization  tasks  is  similar  to  the  general 
objective of the agent system; the complexity reduction. That means the idea is to subdivide a complex 
optimization  problem  into  simplified  parts  where  the  virtual  market  represent  the  platform  for  the 
combination  of  the  different  aspects.  Further  the  market  based  approach  is  easier  to  understand  for 
human  users  and  enable people  to  follow  the  activities  and  decision  of the  market  much  easier  than  of 
complex algorithms.  

The  central  mechanisms  of  virtual  market  approaches  are  the  auctions.  The  market  is  by  definition  the 
place where the auctions are taking place. That means it is a virtual entity which will be represent by a 
functional agent inside our system. The auction is a process of buying and selling of goods or services.  

In our concept an agent initiate an auction by asking the market place agent to establish an auction for 
desired  good;  this  can  be  for  example  the  release  energy  demand.  In  this  case  e.g.  the  energy 
consumers like lights or parts of machinery which are represented also by agents can participate on the 
auction  by  providing  bids  for  their  own  energy  release.  Each  particular  agent  can  consider  the  own 
current  situation,  e.g.  production  state,  the  possible  risks  when  disabling  this  parts  and  weight  the  bid 
with a personal factor or cost function.  

Doing  so  at  the  end  of  the  auction  the  initiating  agent  receives  a  list  of  disconnectable  devices  with 
information about costs and risks of the detaching. Based on this list the agent can calculate the optimal 
combination of devices to switch off.  

Basically  inside  the  virtual  trading  approaches  different  types  of  auctions  are  known  providing  different 
benefits in dependence to the requirements. In following same most popular auction types are presented: 

  Open  ascending  price  auction:  is  an  auction  type  where  participations  bid  openly  against 
another, where each subsequent bid must be higher than the previous bids. The highest bid wins 
the auction.  

  Open descending price auction: is an auction type where the price of the good is automatically 
descending based on a time scale, the first bidder wins the auction. In case of multiunit auctions 
the auction can continue until all units are distributed.  

  Sealed  first  price  auction:  within  this  auction  the  participants  submit  simultaneously  sealed 
bids, so no bidder knows the bid of any other participant. The highest bid wins the auction paying 
the submitted price.  

  Sealed  bid  second  price  auction:  is  similar  to  the  “sealed  first  price  auction”  with  the 

difference the winner with the highest bid pays the price of the second bid.  

  Uniform  price  auction:  is  a  type  of  multiunit  auctions,  where  multiple  units  have  to  be 
distributed to multiple bidders. Within this auction type each bidder submits the bids (multiple are 
also  possible)  designating  the  quantity  and  the  price.  After  the  end  of  the  auction  the  winning 
bidders  are  selected  based  on  the  highest  price  and  number  of  asked  units  (until  all  units  are 
distributed). The price to pay per unit can then be  either the highest price or the lower price of 
winning bids depending on the type of auction.  

  Double auction: is type of auction where sellers and bidders simultaneously submit their offers 
and bids to the auctioneer (e.g. sell/ask n units for price p). Based on submitted propositions the 
auctioneer  calculates  the  price  per  unit  and  serves  the  trades  according  to  this  price.  (This 
auction type is typically used for stock markets)  

In order to solve the optimisation problem of switchable loads in the meaning of energy balancing a type 
of uniform price auction is the most appropriated solution.  

105 

 
In  general  the  energy  management  has  a  nature  of  a  dynamic  multi-objective  problem  where  different 
events strongly influencing the production processes and changing the optimisation goals partial even in 
oppositional direction.  

Figure 109 shows a scheme on dependencies between the influences and necessary reactions. According 
based on this scheme different optimisation tasks can be identified.  

1.  Energy engagement: defines the possibility to exploit the daily energy price fluctuation in order 
to  shift  the  production  with  high  energy  demand  to  times  with  lower  prices.  In  doing  so  a 
prediction of daily price profiles is necessary allowing adapting the production planning according 
to the price progression.  

2.  Disconnection  event:  describes  the  scenario  where  due  to  the  low  grid  capacity  production 
processes  with  high  energy  demand  must  be  switched  off  in  order  to  stabilise  the  grid. 
Accordingly in order to  minimise  the influence  of the disconnection on the production processes 
the decision must be met disabling of which processes is more favourable. Anyway adaptation of 
production schedule considering the booked energy profile is necessary too. 

3.  Enabling  event:  due  to  the  high  volatility  of  the  renewable  energy  it  appears  that  the  energy 
grid  produces  more  energy  than  necessary.  This  leads  to  lower  prices  on  the  market  up  to 
negative  prices  as  e.g.  in  Germany.  In  order  to  take  an  advantage  of  this  it  is  necessary  to  be 
able  to  increase  the  energy  consumption  on  demand.  The  idea  is  on  one  side  to  create  buffers 
which can be released in high price phases but might be also used in order to shift the production 
of  products  with  more  energy  demand  in  this  phase.  Anyway  an  adaptation  of  production 
schedule might be necessary. 

4.  Surveillance  of  local  limits:  The  calculation  of  energy  cost  is  typically  done  based  on  mean 
values for some time period. For example the cost can be calculated based on maximal value of 
15.Min energy consumption  average  for the whole year as done at HHO. For the steel producer 
that  means  that  they  must  prevent  spikes  in  15.Min  averages.  In  doing  so  a  prediction  of  the 
15.Min  average  is  necessary  allowing  meeting  countermeasure  e.g.  by  disabling  of  energy 
consumers. This also influences the production schedule. 

Figure 109: Influences and Reactions 

As it can be seen from the Figure 109 all events have direct or indirect impact on the production schedule 
but  with  different  objectives,  sometimes  increasing  energy  demand  sometimes  decreasing.  For  this 
reason  in  order  to  handle  this  optimisation  problem  the  target  function  must be  flexible  adapted  to  the 
current situation.  

106 

 
 
Following,  this  optimisation  problem  can  be  seen  as  a  global  problem  consisting  of  separated  sub 
problems which are active or inactive depending on the given situation. This problem is perfectly suitable 
to be solved by an agent system. As it has been written in previous sections the main benefit of an agent 
system  is  that  the  agent  based  approach  allows  to  separate  a  big  problem  into  smaller  sub-problems 
where the global solution is generated through the communication of the agents.  

Figure 110 shows a generalised schema of the agent solution. Here different types of agents are foreseen 
which  are  responsible  for  various  tasks.  Difference  is  also  made  between  active  agents  (blue)  who  act 
autonomously  and  passive  agents  (green)  who  provide  only  some  services  to  other  or  represents  the 
external interfaces.   

Figure 110: Scheme of agent infrastructure 

The passive agents in this scheme are responsible for the communication with external systems and data 
management for example in order to provide price data of energy market or to provide information about 
energy measurements at the plant. 

For the active part following types of agents are designated: 

  Day  ahead  predictor:  this  agent  analyses  the  historical  courses  of  the  energy  price  and 
generates a prediction of daily energy price profile. This information can be used by operators in 
order to make plans for the  production  schedule  utilising the low price  phases  for production of 
products with higher energy demand.  

  Profile  extractor:  is  an  agent  who  is  devoted  to  the  extraction  and  management  of  energy 

profiles for each type of product and each separable process based on historical data.  

  Energy use predictor: calculates the energy demand based on a given production schedule and 
energy  profiles  (managed  by  profile  extractor)  on  a  daily  base.  The  results  can  be  used  by 
operators  in  order  to  generate  an  energy  demand  profile  to  be  ordered  on  day  ahead  energy 
market.      

  15 Min. forecast: is an agent who is responsible for making predictions of energy consumption 
based on already consumed energy and foreseen energy demand defined by production schedule 
and product energy profiles for  the current 15Min slot. This information  can  be  used in  order to 
avoid the 15 Min. spikes through the disabling of flexible loads or shifting of production phases.  

107 

 
 
  Booked  vs.  actual:  This  agent  is  devoted  to  the  monitoring  of  booked  energy  profile  and 
comparison with the actual energy consumption. In cases of bigger deviations this agent should 
meet countermeasures e.g. by asking for enabling or disabling of flexible loads.  

  Enable  Loads  /  Disable  Loads:  both  agents  have  a  similar  functionality  but  with  different 
objectives,  one  is  responsible  for  enabling  of  loads  and  the  other  for  disabling.  From  the 
functional perspective each of  them  utilise the  marketplace  in order to select the desired loads. 
The  trigger  for  the  activation  is  generated  either  by  an  external  system  or  by  another  internal 
agent. In case of the activation the agent creates an auction on the marketplace asking the other 
agents  (here  representatives  of  the  machines)  to  generate  the  bids  for  disable/enable  loads 
providing also cost function for the utilisation of the bid.  

  The  machine  agents  as  the  representatives  of  the  real  energy  consumer,  knows  intrinsically 
about  the  available  load  and  are  able  to  provide  the  bids  to  the  load  managers.  Based  on 
provided  bids  the  agent  is  capable  to  select  best  combination  of  machines  to  be  enabled  or 
disabled.        

  Schedule Adaptation: is an agent which is responsible for the adaptation of the production plan 
in  cases  when  a  change  becomes  necessary  e.g.  by  disable  loads  event.  The  adaptation  is 
performed  considering  the  existing  production  plan,  the  relating  constraints  and  booked  energy 
profile for this day utilising KPI developed in Task 5.3.  

2.2.6.3   Task 6.3: Software development and implementation of strategies and algorithms 
This task is devoted to the implementation of agent system core components as well as adaptation of the 
algorithms to the different use cases. In general this task can be seen as integrative tasks which combine 
the results of previous work packages to a common approach, providing generic algorithms, which then 
are adapted to corresponding application scenarios.  

“ViewStore”, 

Concerning  the  core  parts  of  the  agent  system  following  agents  has  been  implemented:  “AgentStore”, 
“AgentAdministration”, 
“ConfigurationAgent”, 
”DebugAgent”, 
“ExternalCommunications” according to their description presented in previous chapter. Figure 111 shows 
a screenshot of the system, showing different views of the system agents which can be flexible arranged 
within the main window. Mentionable is here the “DebugAgent” who allows tracking the communication of 
other agents. In the presented example two test agents “PingAgent” and “PongAgent” has been created 
which just send a message to each other in order to test the communication interface. The “DebugAgent” 
presents a progress of the communication on a time line allowing the analysis of messages.  

“LogAgent”, 

Figure 111: Screenshot Agent System “Core parts” 

Referring to functional aspects of the global approach following components have been developed: 

108 

 
 
Profile  Extractor  (extracts  energy  profiles  from  historical  data  on  a  product  and  process  base):  This 
Agent  is  mainly  used  for  processes,  e.g.  rolling  mill,  were  the  energy  demand  profile  can  be  predicted 
with  a  sufficient  level  of  reliability  and  is  useful  for  the  evaluation  of  very  short  time  energy  demand. 
Figure  112  shows  examples  of  the  extracted  profiles  from  hot  rolling  mill  process  for  three  different 
products.  The  thin  lines  represent  the  originally  measured  profiles,  while  the  thick  line  represents  the 
average of the measured profiles determining the generic profile for the corresponding type of products. 
The administration of extracted profiles is done currently on file base that means all profiles are stored in 
a CSV (Comma-Separated Values) text file, which can be either imported by excel for manual review or 
evaluated automatically by the described agent.  

Figure 112: Examples of energy profiles for three different products from hot rolling mill 

Energy  demand  profile calculation:  The  energy  demand  predictor  utilizes  the  extracted profiles  or  a 
flat  profile  together  with  a  given  production  schedule  to  predict  the  energy  demand.  The  approach 
developed in Task3.3 has been integrated into the agent.  

Figure 113: Example of energy demand prediction based on generic profiles and production schedule 

Figure 113 shows an example of the production simulation where the green line represents the simulated 
result  based  on  generic  profiles  while  the  grey  line  shows  the  real  measured  signal.  The  blue  line 
represents  the  production  plan  for  corresponding  process  step.  The  developed  component  is  able  to 
simulate energy profiles consisting of arbitrary amount of sub processes.  

15min  forecast:  Calculates  the  already  spent  energy  within  the  current  15min.  slot  and  utilize  the 
energy  use  predictor  to  calculate  the  prediction  for  the  remaining  part  of  the  slot.  In  case  of  “fast” 
processes (less than 15 minutes) the production schedule is also taken into consideration. 

109 

 
 
 
 
 
Figure 114: Example for the 15min. peak forecast 

Figure  114  shows  an  example  for  15min.  peak  forecast.  The  red  line  represents  the  real  15min  peak, 
while the blue line shows the prediction at each time step. 

Booked vs. actual: calculates the KPI based on algorithm defined in Task 5.2/5.3 

Machine Agents: represents the machines participating in auctions for enabling or disabling  

Loads  manager:  utilize  the  marketplace  in  order  to  select  the  machines  to  be  enabled  or  disabled 
depending on request. 

Day ahead predictor: represents the prediction of energy price profile based on historical market data 
and according to the model developed in WP2. 

Schedule  adaptation:  represent  the  adaptation  of  the  schedule  in  cases  when  a  change  becomes 
required according to model developed in Task 5.5 

2.2.6.4  Task 6.4: Installation of the software tools into the plants infrastructure 
Thanks  to  the  agent  architecture  adopted  the  specific  agents  necessary  for  each  plant  has  been 
integrated in the industrial infrastructure.  

In  particular  the  possibilities  to  limit  the  15  minutes  peak  has  been  implemented  on  all  the  industrial 
partner  site.  AST  and  ORI  have  also  focused  on  the  limitation  of  the  imbalance,  while  LSW  and  HHO 
tested the possibility to access the balancing market, enabling of additional loads  at HHO (see Task 4.6 
for more details) and disabling loads at LSW (see Task 6.5 for test results). 

15 Minutes peaks limitation  

Within  this  part  the  agent  approach  has  been  utilized  in  order  to  predict  and  limit  the  15  Min.  peaks 
based on energy profiles and under consideration of production schedule. The general approach has been 
already described in previous chapter (Task 6.3). The final solution covers following functions: 

  Collection and storage of energy consumption data. This part implements the interfaces to 
the  energy  management  systems  capturing  the  energy  consumption  data  in  real-time  regime. 
The data is then stored and utilized by other agents e.g. for profile extraction.  

  Extraction  of  energy  profiles  per  product  (coil).  This  part  includes  the  algorithms  for  the 

profile recognition as well as for the assignment of the profile to the produced product. 

  Management  of  profile  storage.  The  system  stores  up  to  50  profiles  per  product  type  in  a 
bunch  by  continuously  replacing  the  oldest  profile  by  the  newest  one.  The  mean  profile  is  then 
automatically updated based on profiles stored within the bunch.   

  Generation  of  forecasts  for  next  one  or  two  15Min  periods.  The  behavior  of  this  agent 
depends on the specific installation. For rolling mill the creation of forecast is based on production 
schedule  originated  from  the  furnace  allocation  and  historical  profiles  generated  by  profile 
extraction. In cases when a profile is unknown a standard artificial profile is utilized. For EAFs the 

110 

 
 
calculation  is  based  on  the  process  models  implemented  in  WP3.  For  the  other  processes  the 
forecast is based on a projection of the actual energy demand continuously updated.  

  Transmission of the results to the  control systems. The predictions are stored in real-time 
within a production database. These information are accessible to the control systems which can 
induce a delay in production schedule or limit the power for a process or stop a process in cases 
when 15Min limit may be exceed.  

  User  interface,  shows  the  current  energy  consumption  data  as  well  as  the  predictions  

graphically.  

In  Figure  115  a  screenshot  of  the  user  interface  for  15  Min.  peak  prediction  installed  on  a  virtual 
computer at the IT infrastructure  of  HHO and connected with plant databases and energy management 
system is shown. Within the user interface the current energy measures are shown as well as predicated 
profile and corresponding forecasts for 15Min average values for both current and next 15 Min. period.  

Figure 115: Screenshot of the user interface for 15 Min. peak prediction 

In Figure 116 a screenshot of the user interface in AST to monitor the behavior of the system is shown in 
a  15  min  interval  where  the  EAF4  (F4)  is  stopped.  The  actual  15  min  interval  is  presented  where  the 
green line represents the defined limit for the 15 min peak, the red line is the energy used in the whole 
plant and the yellow and light blue lines are the energy in the two EAFs that are continuously monitored.  

111 

 
 
Figure 116 Example of 15 minutes peak control loop 

In  Figure  117  a  screen  shot  of  the  user  interface  of  the  system  at  ORIMartin  is  shown.  Actual  and 
previous 15 minutes interval is shown together with energy monitoring data of the whole plant. 

Figure 117: user interface for 15 min peak management at ORI 

Lowering imbalance  

The aim is the lowering of the imbalance between booked and actual energy profile. For this purpose the 
following agent are necessary. 

  Historical data elaboration. Calculation of energy consumption related not only to the specific 
process but also to the characteristics of the product according to the methodologies developed in 
Task 3.1.  

  Day ahead predictor. Generation of prediction of daily energy price profile 
  Energy  demand  profile  calculation.  Calculation  of  the  energy  profile  interacting  with 
production  scheduler,  historical  data  elaboration.  The  calculation  are  based  on  the  model 
developed in task 3.3. The profile is integrated with the cost profile in order to evaluate also the 
economic point of view. 

  Energy Monitoring. Collects and visualize the actual energy use by all the monitored machines.  
  KPI  calculation.  The  generated  profile  is  continuously  monitored  and  compared  to  the  actual 

energy demand and the KPIs are calculated according to implementation done in task 5.3.  

  User  interface,  shows  the  current  energy  consumption  data  as  well  as  the  predictions  

graphically.  

112 

 
 
 
In Figure 118 a screenshot of AST user interface is shown where the first two lines indicating the hourly 
total  energy  demand  and  the  energy  cost  calculated  by  the  DYNERGYSteel  system  have  been  added to 
the steel shop planning user interface already used by the plant in order to minimize the impact on the 
final  user.  In  the  rest  of  the  plant  downtimes  have  been  taken  into  consideration  according  to  the 
planned process stops inserted by the operators. The overall plant energy profile is then prepared by the 
system and sent to the energy manager who are in charge of interacting with the energy provider to buy 
energy on the day ahead market.  The user  interface  are  web based  and  are accessible  from  any PC on 
the  LAN.  When  the  Intra  Day  (ID)  auctions  are  opened  the  energy  manager  can  rearrange  the  energy 
profile  in  order  to  minimize  the  imbalance.  The  continuous  energy  monitoring  (Figure  119  and  Figure 
120)  together  with  evaluation  of  the  KPI  helps  the  energy  manager  to  decide  whether  to  participate  in 
the Intra Day auction. 

Figure 118: Forecasted AST steel shop energy cost and profile  

Figure 119: Energy monitoring at AST 

113 

 
 
 
Figure 120: Steel shop energy monitoring at AST 

2.2.6.5  Task 6.5: Test and validation phase 
The developed system has been tested  in the  industrial  partner sites according  to the  implemented use 
cases. 

15 Minutes peak limitation 

At HHO for the analysis of prediction quality a data set of 7 days operation has been selected. From this 
original dataset some of 15 [min] intervals have been removed, in particular such where the continuous 
production was considerably disturbed. This is for example the case when change of rolls is taking place. 
Such  intervals  would  essential  falsify  the  analysis  results,  because  in  these  cases  the  prediction  is  of 
course  very bad  (because the prediction algorithm  assumes continuous production). Nevertheless, from 
the point of view of 15 [min]  Peak avoidance  is  this situation uncritical. The energy demand during the 
roll  change  is  much  lower  as  during  the  production  phase,  so  that  the  probability  to  exceed  the  limits 
almost  does  not  exist.  Finally  403  of  15  [min]  intervals  have  been  selected  for  the  analysis,  this 
correspond to more then 4 days of continuous operation and a production of approx. 2400 coils.  

Figure  121  shows  the  course  of  the  error  at  each  time  point  of  15  [min]  interval  for  each  selected 
interval. Looking at that graph it can be seen that the error at the beginning of interval has much higher 
variance as at the end. This is logical, since the prediction at time point t is made for the residual of the 
interval time. That means at the beginning of the interval t=0 [min] the prediction is made for 15 [min] 
residual  time  while  for  example  at  t=10  [min]  the  prediction  is  made  for  5  [min]  residual  time. 
Accordingly  the  accumulation  of  error  is  much  higher  at  the  beginning  of  the  interval  as  at  the  end. 
Nevertheless it can also be seen that the main channel of the error is between +/- 6[%].  

From point of view of 15 [min] peak avoidance the prediction quality at time t=10~12 [min] is of major 
relevance  because  the  remaining  time  of  5~3  [min]  is  the  time  frame  which  allows  to  influence  the 
production for example to delay the production of next coil.   

Looking at course of the average of relative error (see. Figure 122) it can be seen that the average error 
EAVG at the beginning of interval t=0 [min] is equal to EAVG = 5.5 [%] while at the time point t = 10 [min] 
EAVG  = 2 [%].  Also it can  be seen that  the  standard  deviation of absolute error stabilize around t = 10 
[min] which positively influence the applicability of the prediction for avoidance of 15 [min] peaks. 

114 

 
 
 
Figure 121: Error course for each selected interval 

Figure 122: Average and standard deviation of relative errors 

115 

 
 
 
 
Figure 123 15 min peak result at ORI 

Figure 123 shows the daily system intervention at ORI in terms of minutes of EAF interruptions during a 
month reaching the objective to maintain the 15 Minutes peak lower than 97.5 MW. 

In  Figure 124  the intervention of the system in  terms  of  EAF4  and  EAF5  stops at AST on some days of 
production is shown.  

Figure 124: 15 minutes peak interventions at AST 

In Figure 125 the result obtained at AST reducing the 15 min peak is shown. In particular the reduction 
of  the  peak  from  268  to  226  can  result  on  saving  around  160,000.00  €/month  considering  3,800.00 
€/MW/month.  

Lowering imbalance  

The  overall  objective  at  ORIMartin  is  to  maintain  the  imbalance  lower  than  15%.  In  such  a  way  little 
money can be gained but mostly no fees will be charged from the electricity provider in the future for the 
imbalance. In 2017 the paid imbalance account for around 1% of the total electrical energy costs. 
Initially only EAF was taken into consideration for preparing the forecasted energy profile, but the results 
was not convincing, therefore the focus has been put also on the rolling mill. The operators can insert the 
planned  process  stops  due  to  strikes,  unions  meetings,  vessel  refractory  lining,  other  foreseen 
maintenance and the corresponding plant energy demand. . 

The overall plant energy profile is then prepared by the system and sent to the energy manager who are 
in charge of interacting with the energy provider. In Figure 126 the energy profiles for two examples days 
are  shown.  Around  10  o’clock  on  Day2  a  manual  downtime  was  inserted  while  the  other  differences 
between the two profiles are due to the rolling mill programme. 

116 

 
 
 
 
Figure 125: 15 min peak results at AST 

Figure 126 two days examples of energy forecast at ORI 

Still  the  15%  goal  was  not  reached  and,  therefore,  the  developed  system  needs  further  tune  up  for  the 
evaluation of the specific energy needs. A simulation of this tune up based on two months production led 
to the following improvement of the imbalance  
Working day (Mon-Fri) from 17,2% to 14,72%  
Week end (Sat-Sun) from 15,31% to 14,14%  
At AST the adoption of the new way to define the booking energy profile and the participation in ID market 
led to the following improvement of the imbalance  
Hourly imbalance from 15,48% in 2016 to 11,71% in 2017 
Monthly imbalance from 12,91% in 2016 to 10,40% in 2017 

117 

 
 
 
 
LSW oxygen-plant as a flexible load 

Test period with the use of the oxygen-plant no. 2 as a flexible load 

Based  on  a  contract  with  ENERNOC  as  a  demand  response  aggregator  LSW  started  in  January  2017  to 
offer  the  flexibility  of  the  oxygen  plant  no.  2.  Within  their  contract  LSW  and  ENERNOC  defined  the 
principles to sell flexibility of both oxygen plants no. 1 and 2 with the following characteristics: 

 

The available load is a positive load, that means that the flexibility is created by a potential to “switch 
off”  the  plant.  A  negative  load  by  a  “switch  on”-characteristic  could  not  be  offered  in  an  automatic 
mode of operation. 

 

The offered flexible power load of the oxygen plants is limited to the following level 

o  PVSA1: max. 1,7 MW 
o  PVSA2: max. 2,0 MW 

 

In general LSW may offer two different types of flexibility:  

o  SCR+  
o  MR+  

positive secondary control reserve  
positive minute reserve (tertiary control reserve) 

LSW decided to operate plant no. 2 and offer SCR+ to the power market. 

 

 

 

 

 

 

LSW had to announce the disposability of the oxygen plant as a disconnectable load to Entelios  in a 
defined  schedule.  The  availability  or  the  non-availability  of  the  plant  has  to  be  declared  for  well-
defined time slices of 4 hours duration. This declaration takes place at different times  
for SCR+:  at the day before for the next day, latest at 2 o`clock pm 
for MR+:   at Thursday 5 o`clock pm for the following week (Monday-Sunday). 

o 
o 

The time length of a “switch off”-interrupt must not exceed a maximum length of 45 minutes. It was 
defined additionally that a second successive action is only allowed to be realized after a time delay 
of 10 minutes at least. 

The  aggregator  sells  the  flexibility  within  the  market  for  control  reserve  and  may  stop  the  plant 
automatically within that available time slices that have been declared by LSW before. 

The  realized  operation  is  rated  afterwards.  This  calculation  is  always  performed  one  day  after  the 
relevant  time  slot.  At  that  time  the  real  measured  energy  demand  of  the  plant  is  considered  in 
relation to the announced power consumption of the plant within intervals of 15 minutes. Differences 
between the real to the planned data are charged. A withdraw of the announcement of disposability is 
possible but it is penalized.  

LSW  and  the  aggregator  agreed  upon  two  different  fees  for  valuing  the  flexibility,  once  a  Demand 
Charge DC (€  per kW) for  placing the flexibility and  an additional energy  price  EP that is paid for a 
real use of the flexibility.  

The demand charge DCLSW (€) is paid to LSW for placing the flexibility. It is calculated according to 
the following formula 

𝐷𝐶𝐿𝑆𝑊 = 𝑃𝑜𝑤𝑖 ∗ 𝑃𝑃𝑖𝑧 ∗ 𝑆𝐷𝐶 ∗ 𝐶𝑈𝑖 

In this formula the following factors are used: 

DCLSW  
Powi  
PPiz  
SDC 
CUi  
The market price PPiz differs a lot between the HT (Hochtarif) and NT (Niedertarif). 

= Demand charge paid to LSW for placing the flexibility (€) 
= Power used within the flexible measure of the machine i (kW) 
= price paid by market to machine i in time slice z  (€/kW) 
= share of the demand charge earned by LSW (%) 
= capacity utilization of the machine i (%) 

 

The additional energy price EP (€/kWh) is paid to LSW for the time slices when the flexibility is used. 
EP is calculated according to the following formula  

In this formula the following factors are used: 

𝐸𝐶𝐿𝑆𝑊 = 𝐸𝑖𝑧 ∗ 𝑃𝐸𝑖𝑧 ∗ 𝑆𝐸𝐶 

ECLSW  = Energy charge paid to LSW for creating the flexibility (€)                                  Eiz 

= Flexible Energy created by the machine i within the time slice z (kWh)   PEiz  = price paid 

by market to machine i in time slice z  (€/kW) 

118 

 
 
 
SEC  = share of the energy charge earned by LSW (%) 

 

 

The factors SDC and SEC include that both partners may split the total earnings so that every partner 
participates with his share in the results. The factor SDC is getting smaller if LSW declares the plant 
to be less available. Within a separate calculation for the factor SDC different targets can be included. 

LSW was allowed to define a minimum energy charge that had to be paid for an interrupt of the 
process.  

Results of the test period January – July 2017  

In 2017 LSW decided to use the oxygen plant no. 2 for a flexible production and to offer SCR+ to the 
control  market.  PVSA  plant  no.  1  could  not  be  used  for  a  flexible  operation  because  it  was  not 
operating at full capacity at this time. The test period with PVSA2 took place in January to July 2017. 
The results of the single months of the test are shown below in Table 25. 

Table 25: Results of a test period to use the flexibility of the oxygen plant PVSA2 at Lech-Stahlwerke 

Period in 
2017 

January 
February 
March 
April 
May 
June 
Total 

Availability of 
PVSA2 for 
flexibility 
< 70 % 
< 70 % 
92,5 – 96,5 % 
> 99,5 % 
92,5 – 96,5 % 

Power load 
 of PVSA2 
(MW) 
1,796 
1,777 
1,790 
1,788 
1,787 
1,800 

Relative 
Revenues 
(%) 
0,3 
0,4 
26,4 
36,4 
36,5 

100,0 

No. of events  
of shut down 

Total time  
of shut down 

2 
3 
5 

15 min 
24 min 
39 min 

LSW has got the following results and experiences from the test period: 

  During January and February 2017 LSW offered the oxygen plant as disconnectable load only 
during the day time shifts, so that an engineer is available during a possible shutdown of the 
oxygen  plant.  As  a  result  the  availability  as  disconnectable  load  was  less  than  70  %  of  the 
total production. In this case no real economic result was obtained. 

  Starting  with  March  2017  the  disposability  was  announced  on  a  7  day  24  hour  basis.  The 
declared  availability  within  the  months  March  to  June  was  >  92,5  %  and  high  enough  to 
obtain a relatively good demand charge DCLSW. 

 

In May and June in total five interrupts occurred at the PVSA2. The interrupts had a length of 
about 6 -8 minutes and their sequence is shown in Figure 127. 

Figure 127: SCR+ interrupts within the test operation of PVSA2 in May and June 2017 

119 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

LSW found within this period of May and June 2017 that the automatic process of interrupt 
management did not work well. Two main problems occurred: 

o  The  first  problem  was  the  missing  information  about  the  end  of  an  interrupt.  The 
process managers did not get a signal that they could start the plant again. Therefore 
the stop of the plant was in some cases much longer than necessary. 

o  The second problem was that the level of automation of the PVSA2 plant did not allow 
an automatic ramp up after an interrupt. Therefore additional personnel were needed 
in  order  to  start  the  plant  again.  This  caused  sometimes  an  additional  delay  of  the 
production as well. 

Because of the delays in plant operation LSW had to buy liquid oxygen in some cases, which 
caused high costs. It was not profitable to go on in this way. Therefore LSW decided to stop 
the test period. The PVSA plants will now be optimized so that they will be able to start and 
stop automatically in future. 

2.2.6.6  Task 6.6: Adaptation of the system 
The applications developed was running at the industrial partners site for several month. During this time 
the systems have been monitored both in term of functionalities and problems encountered. The results 
and  the  failures  have  been  analyzed  during  the  adaptation  phase  and  the  systems  has  been  corrected 
ensuring the expected results and the stabile operation.  

2.3  Conclusions 

The  analysis  of  the  electricity  markets  highlights  several  opportunities  to  save  money  for  the  steel 
companies.  In  Day-Ahead  &  Intra-Day  markets  the  opportunities  are  based  on  the  possibility  to  take 
advantage  from  the  oscillation  of  the  energy  price  during  the  day  and  between  working  days  and 
weekends  /  holidays.  In  Control  Reserve  markets  the  opportunities  are  related  to  the  possibility  to 
dynamically react to grid events providing ancillary (typically balancing) services. The interruptible loads 
(AbLa-IL) offer another opportunity. 

SCR-TCR markets are  going to  be opened in Italy  (pilot projects in  2017)  and therefore the experience 
gained  in  this  project,  together  with  the  participation  of  RSE,  can  help  the  Italian  authority  to  better 
understand steel factories needs.  

All  the  plants  having  at  least  an  EAF  highlight  the  importance  of  controlling  this  process  and,  more  in 
general, the steel making area, due to the fact that it represents the main consumer of electricity inside a 
plant. A focus of the project has been also on the rolling mill area, not only for HHO, that doesn’t have 
EAF.  Managing  these  processes  can  help  in  minimizing  the  15  minutes  peak  and  differences  between 
booked and used electricity. Opportunities  to spare money are offered also from ancillaries  participating 
in  the  balancing  markets  e.g.  oxygen  production,  hall  lighting,  air  conditioning  including  fan,  cooling 
towers that can be more easily managed without interfering too much with the production. 

The  DYNERGYSTeel  approach  has  been  implemented  and  tested  in  the  four  industrial  partner  plan 
showing  that  the  way  taken  already  gives  positive  results  (e.g.:  control  and  limitation  of  15  minutes 
peaks, imbalance limitation), however still other implementation or adaptations are necessary for further 
lowering 15 minutes peak and imbalance and participating more actively in balancing markets. Moreover, 
the energy markets are in continuous evolution and therefore should be monitored in order to be able to 
catch new opportunities when they arise.  

2.4  Exploitation and impact of the research results  

2.4.1  Actual applications 

The  system  is  actually  installed  in  the  four  industrial  partners  plants  with  the  aim  of  limiting  the  15 
minutes  peaks  load,  minimising  the  imbalance  between  booked  and  actual  electricity  demand,  test  the 
participation of steel plant in the balancing market both upward-positive and downward-negative. 

2.4.2  Technical and economic potential for the use of the results 
Limitation  of  15Min.  peaks  is  of  relevance  for  plant  energy  managers.  This  in  particular important  from 
the  financial  point  of  view.  An  exceedance  of  1MW  leads  to  cost  of  70-90k€  per  year  (assuming  a  kW 
price  of 70-90€ per year)  in Germany. A limitation of 1MW lead  to  save  3,800.00  €/MW/month in Italy 
(40-50k€  per  year).  The  possible  earnings  is  strictly  dependent  from  the  energy  optimization  level  of 
each plant wishing to go in this direction. Considering a plant aiming at reducing the peak by 1 MW and 
producing 1.000 ktonn/year a spare of 3-4 cent/ton could be achieved in Italy, while an increment of 1 
MW for the same plant size in Germany will led to a cost of 7-9 cent/tonn. 

120 

 
In case of HHO a reduction of the max. peak by 1-1.5MW is possible due to the prediction capabilities of 
the developed system. The prediction can now be used in order to introduce delays in the production in 
cases when the limit threatens to be exceeded. This was not possible before, because it is not possible to 
stop the rolling process during the execution. It is only possible to introduce an additional pause between 
the rolling of two slabs. Accordingly, the time frame for the decision is between 5-3 minutes before the 
end  of  the  15-minute  interval.  The  developed  prediction  module  allows  now  to  assume  the  final  15Min 
value and to react accordingly.  

AST succeeded in lowering by 42 MW the peak load during the project. A further objective of 6 MW is in 
place. 

In  case  of  LSW,  the  installed  SMART-LKA  system  will  allow  to  distribute the  peak  load  during  a  15  min 
interval in a smoother way, thus avoiding in the future a complete shutdown of the four furnaces (2 EAFs 
and 2 LFs), by reducing just the power load of the EAF which is expected to exceed the peak limit. This 
will in the first place lead to a higher overall productivity of the electric steelmaking plant. On a long term 
perspective,  the  system  will  allow  LSW  to  take  part  in  a  new  contract  system  for  energy  supply  called 
“Untypical use of electrical energy” (In Germany: Atypische Netznutzung). Here the peak load limit in the 
15 min interval is reduced by 15 MW in the critical months of the year (December – February) during the 
critical times of the day (7:00-9:00 and 17:00-19:00), whereas for the remaining months of the year no 
restrictions  of  the  peak  load  are  applied.  These  restrictions  could  be  easily  taken  into  account  by  the 
SMART-LKA  system  by  running  the  two  EAFs  in  alternate  mode.  The  cost  savings  for  electrical  energy 
supply which can be achieved with this new contact system are significant. Due to the required long-term 
analysis, it was not possible to prove these assumptions within the framework of the project. The system 
have  to  run  for  at  least  one  calendar  year  avoiding  all  the  peaks  in  order  to  provide  the  evidence. 
However, the obtained results confirm the possibility to generate this effect.  

Lowering imbalance offers potential to spare more money, however it is difficult to precisely quantify the 
potential due to the multifactoriality of the cost determination. As an example in 2017 in ORI imbalance 
accounts for 1% of the total electricity costs. 

Regarding flexible disconnectable loads, the results of the tests at LSW showed that it is possible to run 
the  oxygen  plant  in  a  flexible  operation  mode.  Technical  deficits  were  identified  that  still  have  to  be 
optimized  because  the  plant  must  be  able  to  be  stopped  and  to  be  started  again  in  a  fully  automatic 
mode.  This  optimization  shall  be  done  within  a  short  time.  LSW  expects  that  the  economic  potential  of 
flexible  operations  will  increase  due  to  the  development  of  the  power  market  in  future.  This  can  be 
expected not only in the market segment of primary or secondary control reserve but also in the market 
segment  of  untypical  use  of  electrical  energy  due  to  §  19  StromNEV.  Due  to  expected  changes  of  the 
regulatory  framework  new  ideas  are  discussed  actually  in  Germany  [42].  The  result  of  the 
experimentation  in  HHO  shows  that  it  is  possible  to  participate  in  the  market,  however  the  possible 
economic advantage depends strongly from the energy markets. 

2.4.3  Publications / conference presentations resulting from the project 

F. Marchiori, M. Lupinelli, B. Kleimt, H. Rosemann, J. Denker, A. Ebel, V. Colla, M. Benini, M. Pennesi, M. 
Freddo,  U.  De  Miranda,  R.  Bontempi,  R.  Sternitzke,  P.  Stahlhofen,  H.P.  Markus  Integrated  dynamic 
energy  management  for  steel  production,  Workshop  on  EU  Funded  Steel  Projects  2  February  2016 
Brussels 

F.  Marchiori,  A.  Belloni,  M.  Benini,  S.  Cateni,  V.  Colla,  A.  Ebel,  M.  Lupinelli,  G.  Nastasi,  M.  Neuer,  C. 
Pietrosanti,  A.  Vignali,  Integrated  Dynamic  Energy  Management  for  Steel  Production,  8th  International 
Conference on Applied Energy, ICAE2016, 8-11 October 2016, Beijing, China 

F.  Marchiori,  A.  Belloni,  M.  Benini,  S.  Cateni,  V.  Colla,  A.  Ebel,  M.  Lupinelli,  G.  Nastasi,  M.  Neuer,  C. 
Pietrosanti,  A.  Vignali,  Integrated  Dynamic  Energy  Management  for  Steel  Production,  Energy  Procedia, 
Volume 105, May 2017, Pages 2772-2777  

F. Marchiori, M. Benini, S. Cateni, V. Colla, A. Ebel, M. J. Neuer, L. Piedimonte, A. Vignali, An agent based 
approach for steel industries for exploitation of opportunities and challenges provided by energy markets, 
3rd European Steel Technology and Application Days ESTAD 2017, 26-29 June, Wien 

F.  Marchiori,  M.  Benini,  S.  Cateni,  V.  Colla,  A.  Ebel,  M.  J.  Neuer,  L.  Piedimonte,  A.  Vignali  Agent-based 
approach for energy demand-side management, Stahl und Eisen 138(2018) Nr.2:25-29 · Februar 2018  

121 

 
2.4.4  Any other aspects concerning the dissemination of results. 

The project was mentioned in: 

STEEL PRODUCTION & SMART ELECTRICITY GRIDS, Jean-Pierre Birat - ESTEP, Bruxelles, Hervé Lavelaine 
- ArcelorMittal, Maizières, May 2014. 

Smart  Steel  Research  Fund  for  Coal  and  Steel  :  Supporting  steelmaking  and  use  in  the  21st  Century, 
February 2016 

A  description  of  the  project  has  been  published  on  some  of  the  partners  internet  site  and  on  personal 
pages of participants such as Linkedin.com and Researchgate.net. 

122 

 
 
3  List of figures 

Figure 1 Price models for the purchase of electrical energy  .................................................. 5 
Figure  1:  Italian  day-ahead  spot  market  price  hourly  average  difference  w.r.t.  the  overall  annual 
average  of  the  same  type  of  day  in  working  days  and  in  weekends  and  holidays  (source:  RSE 
elaboration on GME data). ................................................................................................... 14 
Figure 2: German day-ahead spot market price hourly average difference w.r.t. the overall annual 
average  of  the  same  type  of  day  in  working  days  and  in  weekends  and  holidays  (source:  RSE 
elaboration on EPEX Spot data). ......................................................................................... 15 
Figure  3:  Technical  characteristics  of  secondary  (SRL)  and  tertiary  (MRL)  regulation  and  of  the 
interruption service (AblaV) provided by loads in Germany. ................................................. 16 
Figure  4:  Hourly  average  upward  balancing  price  profiles  in  different  Italian  market  zones  in  the 
year between November 2013 and October 2014. ............................................................... 17 
Figure 5: Hourly average downward balancing price profiles in different Italian market zones in the 
year between November 2013 and October 2014. ............................................................... 17 
Figure  6:  Development  of  averaged  capacity  price  of  accepted  bids  for  TCR  (data  base 
www.regelleistung.net). ........................................................................................................ 18 
Figure 8: Principles of a cooperation between an aggregator and a power client (ENERNOC)21 
Figure 9: Hot Area Layout at AST ........................................................................................ 22 
Figure 10: Typical Electrical Energy utilization at AST ......................................................... 23 
Figure 11: Steel Making Area at ORI Martin ......................................................................... 24 
Figure 12: Rolling Area at ORI Martin .................................................................................. 24 
Figure 13: Quenching & Annealing Area at ORI Martin ........................................................ 24 
Figure 14: Example of electricity profile during a working day at ORIMartin ......................... 25 
Figure  15:  Distribution  of  the  electrical  energy  used  by  the  different  processes  in  the  ORIMartin 
plant in 2015 ........................................................................................................................ 25 
Figure 16: Production routes for structural (BST) and quality (QST) steel at LSW ................ 26 
Figure 17: Process route at LSW for production of structural steel with energy grid ............. 26 
Figure 18: Process route at LSW for production of quality steel ........................................... 27 
Figure 19: Distribution of the electrical energy supply to the different consumers in the LSW plant
 ............................................................................................................................................ 28 
Figure 20: Example of Electrical Energy utilization at Steel making Area (blue), HSM (pink), others 
(green) and Total (red) ......................................................................................................... 30 
Figure  21:  Parameters  of  electrical  energy  input at  the  EAF for  an  LSW example heat:  Electrical 
energy, active power and cos Phi ........................................................................................ 30 
Figure 22: Parameters of electrical energy input at the EAF for an LSW example heat: Arc voltages 
and currents of the three phases of the AC EAF .................................................................. 31 
Figure 23: Parameters of chemical energy input at the EAF for an LSW example heat: Oxygen and 
natural gas flow rates for the Cojet injectors ......................................................................... 31 
Figure  24:  Parameters  of  electrical  energy  input  at  the  LF  for  an  LSW  example  heat:  Electrical 
energy, active power and cos Phi ........................................................................................ 32 
Figure 25: Parameters of electrical energy input at the LF for an LSW example heat: Arc voltages 
and currents of the three phases of the AC LF ..................................................................... 32 
Figure 26: Distribution of the electrical energy supply to the different consumers in the HHO plant
 ............................................................................................................................................ 33 
Figure 27: Power load characteristics of a typical EAF heat at the LSW steel plant ............. 35 
Figure 28: Power load characteristics of an EAF heat with three baskets at the AST steel plant 35 
Figure 29: Power load characteristics of an EAF heat with two baskets at the AST steel plant36 
Figure  30:  Statistical  analysis  and  mean  values  of  the  EAF  cycle  times  per  heat  at  Lech-
Stahlwerke LSW .................................................................................................................. 37 
Figure 31: EAFs Specific Electrical Energy at AST during one month production ................ 37 
Figure 32: Tap to Tap time of one Terni EAF during 6 months production ........................... 38 
Figure 33: EAF Specific Electrical Energy at ORI during one month production ................... 38 
Figure 34: EAF process duration at ORI during one month production ................................. 39 

123 

 
Figure 35: Example of the melting power demand of the LSW steel plant in 15 minutes intervals for 
the time period of  08.07.2016 to 14.07.2016. ...................................................................... 40 
Figure 36: Electrical energy demand of the ORI steel plant in 1 h intervals for a week ......... 41 
Figure 37: Effective electricity demand compared to booked one in a day at ORI ................ 41 
Figure 38: Energy flow and power distributions in HHO ....................................................... 42 
Figure 39: Physical representation of the energy data acquisition ........................................ 42 
Figure 40: View of the measurement device for the energy data acquisition ........................ 43 
Figure 41: Markov chain state diagram ................................................................................ 45 
Figure 42: Structure of the monitoring and visualisation system at LSW .............................. 49 
Figure 43: User display of monitoring and visualisation system at LSW ............................... 50 
Figure  44:  Time  intervals  in  minutes  of  the  tap-to-tap  time  needed  for  planned  production  and 
unplanned events and maintenance of the production of EAF1 at Lech-Stahlwerke ............ 51 
Figure  52:  Electrical  energy  consumption  of  EAF  heats  vs.  alloy  element  concentration  and  tap 
weight .................................................................................................................................. 52 
Figure 53: Grouping of LSW steel qualities according to alloy element concentration and tap weight
 ............................................................................................................................................ 52 
Figure 54: Statistical model to calculate the electrical energy demand of EAFs ................... 53 
Figure 55: Calculated vs. actual electrical energy consumption for EAF heats at LSW ........ 53 
Figure 56: Actual electrical energy consumption depending on specific charge weight ........ 54 
Figure  57:  Specific  charge  weight,  tap  weight  and  specific  electrical  energy  consumption  for  a 
sequence of EAF heats after (left) and before (right) a planned maintenance stoppage ...... 54 
Figure  58:  Actual  and  predicted  tap  weight  and  specific  electrical  energy  consumption  for  a 
sequence of EAF heats after a production stoppage ............................................................ 55 
Figure 59: Prediction error of two model approaches for specific electrical energy consumption 55 
Figure  60:  Prediction  error  of  accumulated  electrical  energy  consumption  for  a sequence  of  EAF 
heats .................................................................................................................................... 56 
Figure 61: Austenitic Stainless Steel specific electrical energy needs .................................. 57 
Figure 62: Ferritic Stainless Steel specific electrical energy needs ...................................... 57 
Figure 63: An example of plant configuration file for AST steel industry ............................... 59 
Figure 64: Algorithm calculating energy consumptions ........................................................ 60 
Figure 65: Energy consumption plots ................................................................................... 61 
Figure 66: Average tap-to-tap and power on times for LSW steel grade groups ................... 62 
Figure 67: Day-ahead schedule for two exemplary production days at LSW ........................ 63 
Figure  68:  Deviation  between  scheduled  power  demand  and  actual  energy  consumption  for  two 
exemplary production days at LSW ...................................................................................... 63 
Figure 69: Predicted, scheduled and actual energy consumption for two exemplary production days 
at LSW ................................................................................................................................. 64 
Figure  70:  Deviation  between  quality  dependent  schedule  of  power  demand  and  actual  energy 
consumption for two exemplary production days at LSW ..................................................... 64 
Figure 71: Example of a simulation ...................................................................................... 66 
Figure 72: EAF model calibration process   Figure 73: EAF On-line model validation ......... 66 
Figure  74:  Closed  loop  control  concept  for  power  demand  control  (Smart-LKA)  at  the  LSW  steel 
plant ..................................................................................................................................... 67 
Figure 75 Characteristics of disconnectable loads ............................................................... 68 
Figure 76: Flow chart of the LSW oxygen plant .................................................................... 69 
Figure 77: Example for the use of oxygen at the electric arc furnace within one heat ........... 70 
Figure 78: Concept for electrically heating batch annealing ................................................. 71 
Figure 79: Concept for additionally pre-heating the air for acid regeneration ........................ 72 
Figure 80: Development of online devices to react on electric grid events ........................... 73 
Figure 81: Potential of the revenues for the secondary control power from 2008 to 2012 ..... 74 
Figure 82: comparison Balancing reserve and renewable Energy capacity (wind and solar energy)
 ............................................................................................................................................ 74 
Figure  83:  Number  of  pre-qualified  suppliers  of  secondary  balancing  power  increased  from  2007 
up to 2015 ............................................................................................................................ 75 
Figure 84: Revenues of average prices (positive and negative balancing) in secondary are reduced 
about 53% between 2013 and 2015 ..................................................................................... 75 

124 

 
Figure 85: Market size by segment from 2008 to 2015 and comparison of the revenues from 2009 
to 2015 (since 2009, revenue has declined by 60% in secondary balancing) ....................... 76 
Figure  86:  Planning  of  connections  between  additional  loads  and  management  computers  for 
participation in the electricity market .................................................................................... 77 
Figure  87:  Connections  between  the  High  load  Calculator  (left)  and  the  additional  aggregates 
(right) ................................................................................................................................... 77 
Figure  88:  Example  of  the  extraction  of  the  modelling  parameter  from  the  table  of  loads 
availabilities and constraints ................................................................................................ 79 
Figure 89: Agent based control concept to react on the electricity market ............................ 80 
Figure 90: implemented solution at HHO with two devices (Air conditioning, Cooling Towers)81 
Figure 91: Switching stage of the cooling towers at HHO ..................................................... 81 
Figure 92: Cooling towers during the production at HHO ..................................................... 82 
Figure 93: simplified diagram implementation of the Devices Manager Software at HHO .... 82 
Figure 94: implemented loads manager (Main Agent) at HHO ............................................. 83 
Figure 95: Implemented agents of additional loads with a sampling rate of 2s with the enabling and 
disabling of the cooling towers ............................................................................................. 84 
Figure 96: Total available load for the additional devices at HHO with a sampling rate of 2s 84 
Figure 45: Weekly production plan of Lech-Stahlwerke ............................................ 85 
Figure 46: Method of calculation of energy consumption profile for the forecast .................. 86 
Figure 47: Forecast tool for all working layer with each aggregate in HHO........................... 87 
Figure 48: forecast profile of a month in HHO ...................................................................... 87 
Figure 49: Booked electricity at ORI when a 8 hours maintenance is foreseen .................... 88 
Figure 50: Example of AST steel shop production plan ........................................................ 88 
Figure 51: Example of foreseen electricity needs ................................................................. 89 
Figure 98: Melt Shop Scheduling Optimizatin Schema ......................................................... 89 
Figure 99: Example of Lech-Stahlwerke showing the balance deviations of the booked power load 
to the measured power load for 15 min intervals of one day (16.03.2016)............................ 90 
Figure 100: Example of AST showing the balance deviations of the booked power load (blue line) 
to the measured power load (read line) of five days ............................................................. 91 
Figure 101: Effective electricity demand compared to booked one in a day at ORI .............. 91 
Figure 102: Weight matrix W ................................................................................................ 92 
Figure 103: Mean selling and purchase prices related to the North (a) and South (b)   macro zone
 ............................................................................................................................................ 93 
Figure 104: Matrix W related to the North (a) and South (b) macro zone ............................. 94 
Figure 105: Task contribution to electricity consumption of a time slot ................................. 96 
Figure 106: Melt section for stainless-steel production process ............................................ 97 
Figure 107: Automation landscape ..................................................................................... 100 
Figure 108: Data management architecture ....................................................................... 101 
Figure 109: Scheme of the agent management system design .......................................... 104 
Figure 110: Influences and Reactions ................................................................................ 106 
Figure 111: Scheme of agent infrastructure ....................................................................... 107 
Figure 112: Screenshot Agent System “Core parts” ........................................................... 108 
Figure 113: Examples of energy profiles for three different products from hot rolling mill ... 109 
Figure  114:  Example  of  energy  demand  prediction  based  on  generic  profiles  and  production 
schedule ............................................................................................................................ 109 
Figure 115: Example for the 15min. peak forecast ............................................................. 110 
Figure 116: Screenshot of the user interface for 15 Min. peak prediction ........................... 111 
Figure 117 Example of 15 minutes peak control loop ......................................................... 112 
Figure 119: user interface for 15 min peak management at ORI ........................................ 112 
Figure 121: Forecasted AST steel shop energy cost and profile ........................................ 113 
Figure 122: Energy monitoring at AST ............................................................................... 113 
Figure 123: Steel shop energy monitoring at AST .............................................................. 114 
Figure 125: Error course for each selected interval ............................................................ 115 
Figure 126: Average and standard deviation of relative errors ........................................... 115 
Figure 120 15 min peak result at ORI ................................................................................. 116 
Figure 118: 15 minutes peak interventions at AST ............................................................. 116 

125 

 
Figure 128: 15 min peak results at AST ............................................................................. 117 
Figure 124 two days examples of energy forecast at ORI .................................................. 117 
Figure 127: SCR+ interrupts within the test operation of PVSA2 in May and June 2017 .... 119 

126 

 
 
4  List of tables 

Table 5 - Single pricing mechanism for imbalances. ............................................................ 13 
Table 6: Dual pricing mechanism for imbalances. ................................................................ 14 
Table 1: Reaction time ......................................................................................................... 16 
Table 2: Load / generation alteration .................................................................................... 16 
Table 3: Holding period ........................................................................................................ 16 
Table 4: Comparison among European countries for demand participation and negative prices 19 
Table 7: Imbalance sign changing between sequential hours for macro zone ...................... 45 
Table 8: Transition matrix related to NORTH (a) and South (b) macro zone ........................ 45 
Table 9: Imbalance Sign - BCR performance results for North and South macro zone (n = 6)47 
Table 10: Percentage of recourses to MSD.......................................................................... 47 
Table 11: type of events ....................................................................................................... 50 
Table 12: An example of a test production plan ................................................................... 58 
Table 13: An example of the excel output consumption file .................................................. 60 
Table 14: Example of profile database ................................................................................. 65 
Table 15: Example of schedule data .................................................................................... 65 
Table  16:  List  of  the  flexible  additional  loads  and  their  constraints  for  the  participation  for  the 
Electricity Balancing Market at HHO .................................................................................... 78 
Table 17: Balancing costs for operating the grid at Lech-Stahlwerke ................................... 89 
Table 18: Heat group definition ............................................................................................ 98 
Table 19: Processing times and electricity consumption ...................................................... 98 
Table 20: Setup times [min] ................................................................................................. 98 
Table 21: Transportation times [min] .................................................................................... 98 
Table 22: Maximum waiting times after stages ..................................................................... 98 
Table 23: Processing time schedule..................................................................................... 99 
Table 24: Energy consumption ............................................................................................. 99 
Table  25:  Results  of  a  test  period  to  use  the  flexibility  of  the  oxygen  plant  PVSA2  at  Lech-
Stahlwerke ......................................................................................................................... 119 

127 

 
 
5  List of acronyms and abbreviations 

A list of acronyms and abbreviations used in the report and in the annexes 

Acronym 

Definition 

AblaV 

AC 

AEEGSI 

AMS 

AOD 

AST 

AT 

AUC 

BCR 

Abschaltbare Lasten Verordnung (Interruptible loads Regulation) 

Alternating Current 

Autorità per l'Energia Elettrica il Gas e il Sistema Idrico  

(Regulatory Authority for Electricity, Gas and Water) 

Agent Management System 

Argon Oxygen Decarburization 

Acciai Speciali Terni 

Austria 

Area Under Curve 

Balanced Classification Rate 

BF/BOF 

Blast Furnace/Basic Oxygen Furnace 

BFI 

BSP 

BST 

CCGT 

CCM 

CET 

CDE 

CH 

VDEh-BetriebsForschungsInstitut 

BSP Regional Energy Exchange 

BauStahl (Structural Steel) 

Combined Cycle Gas Turbine 

Continuous Casting Machine 

Central Europe Time 

Consegna Derivati Energia  

(Italian platform of financial contracts concluded on the derivatives 
market) 

Switzerland 

CIRCABC 

Communication  and  Information  Resource  Centre  for  Administrations, 
Businesses and Citizens 

CNORTH 

CENTER-NORTH 

CSM 

CSV 

DE 

DIN-EN-ISO 

Rina Consulting - Centro Sviluppo Materiali 

Comma Separated Value 

Germany 

Deutsches Institut Fur Normung(German Institute for standardization)-
Europa- Norm-International Standards Organization 

DISTR 

Tariff for distribution services 

(German Institute for standardization)  

DOE 

DSO 

EAF 

EC 

ECC 

EEG 

Department of Energy 

Distribution System Operator 

Electric Arc Furnace 

European Community 

European Commodity Clearing 

Erneuerbaren Energie Gesetz (Renewable Energy Law) 

128 

 
Acronym 

Definition 

EEGI 

EEX 

EFSM 

ENTSO 

EP 

EPEX 

ESTAD 

ESTEP 

ETS 

EU 

FN 

FP 

FR 

GME 

GUI 

HHO 

HLC 

HMI 

HRM 

HSM 

HT 

HV 

ICT 

ID 

IDEX 

IEA 

IGCC 

IL 

IP 

IPEX 

ISE 

ISSN 

IT 

KO 

KPI 

European Electricity Grids Initiative 

European Energy Exchange 

Electricity Flow Sheet Model 

European Network of Transmission System Operators for Electricity 

Energy Price 

European Power Exchange 

European Steel Technologies and Application Days 

European Steel Technology Platform 

EPEX trading system 

Europa 

False Negative 

False Positive 

France 

Gestore Mercati Energetici 

Graphical User Interface 

Hoesch Hohenlimburg 

High Load Calculator 

Human Machine Interface 

Hot Rolling Mill 

Hot Strip Mill 

HochTarif (HighTariff) 

High Voltage 

Information and Communication Technology 

Intra Day 

Italian Derivatives Energy Exchange 

International Energy Agency 

International Grid Control Cooperation 

Interruptible Load 

Internet Protocol 

Italian Power Exchange 

Institute for Solar Energy 

International Standard Serial Number 

Italy 

Knock Out 

Key Performance Indicator 

KWKG 

Kraft-Wärme-Kopplungsgesetz (Cogeneration Act) 

LAN 

LCA 

LF 

Local Area Network 

Life Cycle Assessment 

Ladle Furnace 

129 

 
Acronym 

Definition 

LKA 

LSW 

MAS 

MB 

MGP 

MI 

MILP 

MIND 

ML 

MOO 

Last-Kontroll-Anlage (Load-control system) 

Lech-StahlWerke 

Multi Agent System 

Mercato del Bilanciamento (Balancing Market) 

Mercato del Giorno Prima (Day Ahead Market) 

Mercato Infragiornaliero (Intra day market) 

Mixed Integer Linear Programming 

Method for analysis of INDustrial energy system 

Maximum Load 

Multi-Objective Optimization 

MR-MRL 

Minutenreserveleistung (Minutes Reserve) 

MSD 

MTE 

NAR 

NARX 

NAV 

NEV 

NRV 

NRW 

Mercato del Servizio di Dispacciamento (Ancillary Services Market) 

Mercato elettrico a termine (Forward Market) 

nonlinear autoregressive 

Nonlinear autoregressive with external input 

Niederspannungsanschlussverordnung 
Ordinance) 

(Low 

Voltage 

Connection 

NetzEntgeltVerordnung (network tariffs regulation) 

Netzregelverbund (System control network) 

North Rhine-Westphalia (federal state of Germany) 

NSRMSE 

Normalized Square Root Mean Square Error 

NT 

NTP 

OCGT 

OH 

ORI 

OTC 

PC 

PCE 

PCR 

NiederTarif (LowTariff) 

Network Time Protocol 

Open-Cycle Gas Turbine 

Operation Handbook 

ORI Martin 

Over The Counter 

Personal Computer 

Piattaforma Conti Energia (platform for electric energy traded Over-The-
Counter) 

Price Coupling of Regions 

Phelix 

Physical Electricity Index 

PLC 

PRL 

PUN 

PV 

PVSA 

QST 

RES 

RFCS 

RSE 

Programmable Logic Controller 

Primärreserveleistung (Primary reserve power) 

Prezzo Unico Nazionale (Uniform National Purchase Price) 

PhotoVoltaic 

Pressure Vacuum Swing Absorption 

Quality Steel 

Renewable Energy Sources 

Research Fund for Coal & Steel 

Ricerca sul Sistema Energetico 

130 

 
Acronym 

Definition 

RWE 

SACOI 

SAPEI 

SCR 

SMOTE 

SRL 

SSSA 

SVM 

SW 

TCP 

TCR 

TG 

TGS 

TN 

TP 

TRAS 

TSO 

UCTE 

UHP 

ÜNB 

USA 

VAT 

VD 

VDEW 

VDN 

VOD 

VPSA 

WP 

Rheinisch-Westfälisches Elektrizitätswerk 

SArdinia–COrsica–Italy cable 

SArdinia- Italian PEninsula cable 

Secondary Control Reserve 

Synthetic Minority Over-sampling TEchnique 

Sekundärreserveleistung (Secondary Reserve Load) 

Scuola Superiore di Studi universitari e di perfezionamento sant'Anna 

Support Vector Machines 

SoftWare 

Transmission Control Protocol 

Tertiary Control Reserve 

Technical Group 

Technical Groups for Steel 

True Negative 

True Positive 

Tariff for transmission services 

Trasmission System Operator 

Union for the Co-ordination of Transmission of Electricity 

Ultra High Power 

Übertragungsnetzbetreiber (Transmission system operators) 

Unites States of America 

Value Added Tax 

Vacuum Degassing 

Verband der Elektrizitätswirtschaft (Electricity Association) 

Verband der Netzbetreiber (Association of network operator) 

Vacuum Oxygen Decarburisation 

Vacuum Pressure Swing Adsorption 

Work Package 

131 

 
 
6  Full list of references 

[1]  FOREnergy:  “Ergebnispapier  –  Flexibilisierung  der  Energienachfrage  von 

industriellen 
Verbrauchern”,  Ergebnisse  des  Arbeitskreises  1  “wirtschaftliche  Synchronisation  von 
Energieangebot und –nachfrage” des Forschungsverbundes FOREnergy, January 2015 

[2]  DOE  2006  Department  of  Energy:  Benefits  of  demand  response  in  electricity  markets  and 
recommendations  for  achieving  them  –  A  report  to  the  United  States  Congress  pursuant  to 
section 1252 of the energy policy act of 2005. Washington: U.S. Department of Energy 2006 
[3]  Decree  of  the  Energy  Industry  Act  (EnWG):  „Ordinance  on  Interruptible  Load  Agreements 

(AbLaV)”, § 13 Abs. 4a EnWG, 20.12.2012 

[4]  Steel production - energy efficiency working group Final report, January 2014 
[5]  EEXSpot: Annual Report 2013 
[6]  www.netztransparenz.de 
[7]  Burger, B.: Stromerzeugung aus Solar und Windenergie im Jahr 2014, Fraunhofer ISE, Freiburg 

2014 

[8]  An Electricity Market for Germany’s Energy Transition. Discussion Paper of the Federal Ministry 

for Economic Affairs and Energy, Oct. 2014 

[9]  Bundesnetzagentur, Annual Report 2013 
[10]  AEEGSI Order no. 111/06: “Condizioni  per  l’erogazione del pubblico servizio di dispacciamento 
dell’energia elettrica sul territorio nazionale e per l’approvvigionamento delle relative risorse su 
base di merito economico, ai sensi degli articoli 3 e 5 del decreto legislativo 16 marzo 1999, n. 
79” 

[11]  GME: “Testo integrato della disciplina del mercato elettrico approvato con D.M. del 19 dicembre 

2003 come successivamente modificato e integrato”. 

[12]  TERNA: “Codice di Rete – Capitolo 4 – Regole per il dispacciamento” 
[13]  EPEXSpot Annual Report 2013: 

http://static.epexspot.com/document/29102/EPEX%20SPOT%20Annual%20Report%202013.pd
f 

[14]  AEEGSI Order no. 525/2014/R/eel: “Modifiche e integrazioni alla disciplina degli sbilanciamenti 

effettivi di energia elettrica”. 

[15]  Federal Ministry for Economic Affairs and Energy (BMWi), Public Relations: Electricity Market for 

Germany’s Energy Transition (Grünbuch). Berlin, October 2014. 

[16]  CONNECT Energy Economics GmbH: Leitstudie Strommarkt. Berlin, July 2014 
[17]  Authority for Electric Energy, Gas and Hydro Services, Deliberation n° 111/06, 13 June 2006 

and successive updates, http://www.autorita.energia.it/it/docs/06/111-06.htm 

[18]  TERNA, Grid Code, http://www.terna.it/default/home_en/electric_system/grid_code.aspx 
[19]  ENTSO-E: “UCTE Operation Handbook (OH)”, v2.5 draft, 24.06.2004 
[20]  Federal Ministry of Justice: „Energiewirtschaftsgesetz - EnWG”, 07.07.05 
[21]  Federal Ministry of Justice: „Renewable Energy Sources Act – EEG 2014”, 21.07.14 
[22]  www.regelleistung.net/ 
[23]  Verband der Netzbetreiber (VDN) e.V. beim VDEW: “TransmissionCode 2007 – Network and 

System Rules of the German Transmission System Operators”, August 2007. 

[24]  Consentec: “The description of load-frequency control concept and market for control reserves”, 

study commissioned by the German TSO’s, 27.02.14, http://www.consentec.de/wp-
content/uploads/2014/08/Consentec_50Hertz_Regelleistungsmarkt_en__20140227.pdf 
[25]  https://www.transnetbw.com/en/energy-market/ancillary-services/interruptible-loads  
[26]  FOREnergy: Flexibilisierung der Energienachfrage von industrieellen Verbrauchern, Jan. 2015 
[27]  Italian regulatory Authority for Electric Energy, Gas and Hydro Services: “Disciplina delle 

procedure per l’approvvigionamento a termine delle risorse elettriche interrompibili e proroga 
semestrale dei contratti vigenti”, order n° 301/14, June 19th 2014. 

[28]  German federal ministry of justice: “Verordnung über Vereinbarung zu abschaltbaren Lasten 

(AbLaV)”, 28.12.12 

[29]  Sokolova, M. and Lapalme, G. 2009. A systematic analysis of performance measures for 

classification tasks. Inf. Process. Manage. 45, 4 (Jul. 2009), 427-437 

[30]  Vannucci, M. Colla, V. Novel classification method for sensitive problems and uneven datasets 

based on neural networks and fuzzy logic. Applied Soft computing Journal, 11(2), 2011, pp. 
2383-2390. 

[31]  N.V. Chawla, K.W. Bowyer, L.O. Hall, W.P. Kegelmeyer, "SMOTE: synthetic minority over-

sampling technique", Journal of Artificial Intelligence Research , 16, pp. 321–357, 2002. 
[32]  S. Cateni, V. Colla and M. Vannucci, "A method for resampling imbalanced datasets in binary 

classification tasks for real-world problems", Neurocomputing 135, pp. 32-41, 2014.  

[33]  Shcherbakov, Maxim Vladimirovich, et al. "A survey of forecast error measures." World Applied 

Sciences Journal 24: 171-176, 2013 

132 

 
[34]  A review of air separation technologies and their integration with energy conversion processes, 

A.R Smith, J Klosek, Fuel Processing Technology 70 (2001) 115–134 

[35]  A. Nagyova and H. Pacaiova., How to build manual for key performance indicators--KPI. DAAAM 

International Scientific Book (2009): 135-143. 

[36]  J. Armstrong, F. Collopy, Error measures for generalizing about forecasting methods: Empirical 
comparisons, International Journal of Forecasting, Volume 8, Issue 1, 1992, Pages 69-80, ISSN 
0169-2070. 

[37]  C. Van Gorp, C. John, Using key performance indicators to manage energy costs. Strategic 

planning for energy and the environment 25(2), 9-25, 2005. 

[38]  H.Hadera, I. Harjunkoski, G. Sand, I. Grossmann and S. Engell. “Optimization of steel 

production scheduling with complex time-sensitive electricity cost”, Computer and Chemical 
Engineering 76, pp. 117-136, 2015. 

[39]   P. Castro, L. Sun and I. Harjunkoski. “Resource-task network formulations for industrial 

demand side management of a steel plant”. Ind eng Chem Res 52 (36), pp. 13046-58, 2013. 

[40]  H Hadera and I. Harjunkoski. “Continuous-time batch scheduling approach for optimizing 

electricity consumption cost”, Comput. Aided Chem Eng, 32, pp. 403-8, 2013. 

[41]  K. Noide and M. Morari. “ Electrical load tracking scheduling of a steel plant.” Comput. Chem 

Eng, 34(11), pp. 1899-903, 2010. 

[42]  Deutsche Energie-Agentur (dena) ”Impulse zur Weiterentwicklung der Netzentgeltsystematik”, 

January 2018. 

133 

 
 
 
Annex 1 

D 1.1 Description of the general and local electricity markets and 
tariffs in Italy and in Germany 
(Responsible RSE) (Dec 2014) 

134 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Summary 

1 

2 

Executive Summary ................................................................................................ 2 

Introduction ........................................................................................................... 5 

2.1  General electricity market structure .......................................................................... 6 

2.1.1 

Italy ................................................................................................................... 6 

2.1.2  Germany ......................................................................................................... 12 

3 

Power Exchange ................................................................................................... 18 

3.1  Day-ahead spot market .......................................................................................... 18 

3.1.1 

Italy ................................................................................................................. 18 

3.1.2  Germany ......................................................................................................... 40 

3.2 

Intra-day spot market ............................................................................................. 57 

3.2.1 

Italy ................................................................................................................. 57 

3.2.2  Germany ......................................................................................................... 61 

3.3  Opportunities  for  the  participation  of  large  industrial  loads  with  flexible  demand  to  the 
Power Exchange energy markets in Italy and in Germany...................................... 63 

4 

5 

Contracts with a supplier ....................................................................................... 64 

4.1 

Italy ........................................................................................................................ 64 

4.2  Germany ................................................................................................................ 64 

Economic regulation of imbalances in Italy and in Germany ....................................... 65 

5.1 

Italy ........................................................................................................................ 65 

5.2  Germany ................................................................................................................ 66 

6 
possibilities to take profit from demand side management ...................................................... 67 

Comparison  of  the  two  options  (Power  Exchange  vs.  contracts  with  a  supplier)  in  terms  of 

Annex1 - 1 

 
 
 
 
 
 
1 

Executive Summary 

A comprehensive analysis and evaluation of the opportunities to take profit from demand flexibility 
in  electricity  purchases  by  energy  intensive  industries,  both  in  Italy  and  in  Germany,  has  been 
carried out. 

In particular, after a general description of the electricity market structure in the two countries, the 
study focused on the two main ways to purchase electricity by large industrial consumers, i.e.: 

 
 

through the Power Exchange; 
through contracts with a supplier. 

11 electric energy purchases through the Power Exchange can be carried out in the day-
Short-term10F
ahead spot market, that is typically the most important one, in terms of volumes of energy traded 
and of significance of the clearing prices, or in the intra-day spot market, where adjustments to the 
day-ahead  market  schedule  can  be  done,  in  order  to  tackle  with  changes  in  the  situation  that 
occurred  after  the  closure  of  the  day-ahead  market  session  and  to  avoid  imbalances.  For  these 
reasons, the intra-day market typically deals with volumes much lower than the day-ahead one. 

Within  this  context,  a  detailed  analysis  of  hourly  price  profiles  in  the  period  between  December 
2013  and  November  2014  has  been  carried  out  for  the  day-ahead  spot  markets  of  the  Italian 
Power Exchange (IPEX) and of the German one (EPEX Spot). Several charts have been included 
in the report, such as price hourly values, monthly average, hourly average (annual and for each 
month), for all the days of the year and for working-days and non-working days only. 

The charts reported in Figure Annex1-1 and in Figure Annex1-2, showing the Italian and German 
day-ahead  spot  market  price  hourly  average  difference  w.r.t.  the  overall  annual  average  of  the 
same  type  of  day  in  working  days  and  in  weekends  and  holidays,  are  the  most  significant  ones. 
They  clearly  show  how  cheaper  or  more  expensive  can  be  to  consume  energy  in  each  hour  of 
each different type of day. 

Price differences w.r.t. the average can be significant, ranging from about -18 ÷ +23 €/MWh in Italy 
and  from  about  -15  ÷  +12  €/MWh  in  Germany,  showing  that  there  is  a  great  opportunity  to  take 
profit from demand flexibility in purchasing energy from the day-ahead spot market. 

As for the intra-day spot market, the Italian one is characterized by price profiles similar to the day-
ahead profiles, while in the German one, that is a continuous auction, price profiles can be different 
from the day-ahead ones. 

11 We focus on short-term (day-ahead / hours-ahead) since it is the typical time horizon over which demand flexibility can more 
effectively be exploited. For this reason, we did not consider longer-term forward and future contracts in the analysis. 

Annex1 - 2 

 
 
 
                                                
 
Figure Annex1-1: Italian day-ahead spot market price hourly average difference w.r.t. the overall 
annual average of the same type of day in working days and in weekends and holidays (source: RSE 
elaboration on GME data). 

Figure Annex1-2: German day-ahead spot market price hourly average difference w.r.t. the overall 
annual average of the same type of day in working days and in weekends and holidays (source: RSE 
elaboration on EPEX Spot data). 

Annex1 - 3 

 
 
 
Instead of  directly  on  the  Power  Exchange,  energy  can  also be  bought through a supplier,  at  an 
agreed price defined according to specific consumption profiles, or paying the energy at the day-
ahead  spot  market  price.  In  such  a  case,  the  foreseen  consumption  profile  may  have  to  be 
communicated to the supplier two days before the day of delivery, to allow the supplier itself to buy 
the energy on the day-ahead spot market, one day before the delivery. 

In  any  case,  contracts  with  a  supplier  are  in  general  less  flexible  than  purchases  on  the  Power 
Exchange. 

In case  of  deviations of consumption from  the  program  agreed with the  supplier  or  deriving from 
Power Exchange purchases, imbalance energy must be settled at specific imbalance prices. In the 
Italian regulation, prices are penalizing or profitable according to the concordance or discordance 
of  the  imbalance  sign  with  the  sign  of  the  overall  imbalance  of  the  zone  where  the  consumer  is 
located,  while  in  Germany  imbalance  energy  is  settled,  for  each  quarter  of  an  hour,  at  the  price 
obtained by dividing the control energy costs sustained by the TSO in the balancing market in that 
specific quarter of an hour, by the balance of the deployed amount of control energy in that same 
time interval. 

Apart from  reducing the cost  of  energy  purchases  and of  possible imbalances,  demand flexibility 
can contribute to reduce peak power consumption and the related fees. In Italy, the fee applied to 
the  monthly  peak  power  consumption  measured  in  any  quarter  of  an  hour  of  each  month  is 
currently  equal  to  1.528  €/kW-month,  while  in  Germany  the  monthly  fee  ranges  from  6  to  9.38 
€/kW-month, or an annual fee can be paid, ranging from 35.99 to 60.79 €/kW-year.  

Annex1 - 4 

 
 
 
 
2 

Introduction 

This report is the deliverable 1.1 of Work Package 1 of the DYNERGYSteel project. 

Aim  of  the  report  is  to  analyze  the  opportunities  to  exploit  demand  flexibility  to  optimize  electric 
energy purchases for large industrial loads in the Italian and in the German electricity market. 

In particular, after a general description of the electricity market structure in the two countries, the 
study will focus on the two main ways to purchase electricity by large industrial consumers, i.e.: 

 
 

through the Power Exchange; 
through contracts with a supplier. 

As for the Power Exchange, the different energy markets will be described and a detailed analysis 
of price profiles will be carried out. 

As  for  the  contracts  with  a  supplier,  the  typical  arrangements  of  the  industrial  partners  of  the 
DYNERGYSteel project will be analyzed, focusing on the flexibility margins that could be exploited 
by an active demand. 

Annex1 - 5 

 
 
 
 
2.1  General electricity market structure 

2.1.1  Italy 

The  electricity  market  was  liberalized  in  Italy  as  a  result  of  the  approval  of  Legislative  Decree 
79/99, as part of the process of transposition of Directive 96/92/EC concerning common rules for 
the internal market in electricity. This decree, which marked the beginning of the structural reform 
of the Italian electricity sector, responded to the following needs: 

  promoting competition in electricity generation, wholesale trading and retail supply; 
  maximising  transparency  and  efficiency  in  the  natural  monopolies,  i.e.  transmission  & 

dispatching and distribution. 

According to an open-market context, the above mentioned activities are carried out by separate 
entities (unbundling).  

The  entity  entitled  to  guarantee  the  promotion  of  competition  and  efficiency  in  the  sector,  with 
regulating  and  monitoring  tasks,  is  the  Regulatory  Authority  for  Electricity,  Gas  and  Water 
(AEEGSI). The Authority mission includes defining and maintaining a reliable and transparent tariff 
system, reconciling the economic goals of operators with general social objectives, and promoting 
environmental  protection  and  the  efficient  use  of  energy.  It  provides  an  advisory  and  reporting 
service  to  the  government  and  parliament,  and  formulates  observations  and  recommendations 
concerning issues in the regulated sectors of electricity, gas and water. 

The structure of the Italian electricity market is shown in  Figure Annex1-3. The entity responsible 
for  managing  the  Power  Exchange  is  “Gestore  del  Mercato  Elettrico”  (GME).  Unlike  other 
European energy markets, GME’s market is not a merely financial market, where only prices and 
volumes  are  determined,  but  a  real  physical  market,  where  physical  injection  and  withdrawal 
schedules are defined. 

The Power Exchange is composed of different markets: 

 
 
 
 

the Day-Ahead Market (MGP), 
the Intra-Day Market (MI), 
the Forward Market (MTE), 
the Ancillary Services Market / Balancing Market (MSD / MB), 

in  addition  to  the  Over  The  Counter  (OTC)  contracts  Registration  Platform  (PCE)  and  to  the 
Platform  for  physical  delivery  of  financial  contracts  concluded  on  the  derivatives  market  IDEX 
(CDE). 

The  Italian  Transmission  System  Operator  (TSO)  TERNA  guarantees  the  security  of  the  system 
through  the  availability  of  an  adequate  amount  of  generation  reserve  by  selecting  bids/offers  of 
variation  of  the  schedules  submitted  by  participants  in  the  framework  of  the  Ancillary  Services 
Market  (MSD).  On  this  market,  also  organized  by  GME,  bids/offers  are  selected  by  TERNA  and 
remunerated according to the pay-as-bid criterion. During the real time TERNA possibly uses such 
reserves for balancing purposes on the Balancing Market (MB) and for guaranteeing a secure and 
reliable operation of the overall power system. 

Annex1 - 6 

 
 
 
Figure Annex1-3: Italian electricity market framework [source: GME] 

The power generation in Italy has changed rapidly due to an increasing development of renewable 
energy sources, especially wind power and solar photovoltaic. Figure Annex1-4 shows the trend of 
the total installed power for wind and PV in the last years, while Figure Annex1-5 shows the total 
installed power including all technologies. 

As a consequence, the energy produced by each generation technology for supplying the demand 
has notably changed. Figure Annex1-6 shows a comparison of the total generation shares in 2008 
and 2013:  the  increase of  RES  generation  has  forced the  marginal  technology  (Combined Cycle 
Gas  Turbine  -  CCGT)  to  reduce  its  power  generation,  while  coal  keeps  unchanged  its  market 
share, as it is a base load service, characterized by lower generation costs.  

Annex1 - 7 

 
 
 
Figure Annex1-4: Net installed power in Italy for wind plants and solar PV. 

Figure Annex1-5: Net installed power in Italy. 

Figure Annex1-6: Generation shares per technologies in 2008 and 2013. 

Annex1 - 8 

 
 
 
 
In  Italy  the  operation  of  the  transmission  system  is  performed  by  TERNA,  as  the  owner  of  the 
transmission  network.  TERNA  provides  grid  access  to the  electricity  market  players  according to 
non-discriminatory and transparent rules and acts as counterpart in the ancillary services market in 
order  to  ensure  the  security  of  supply  and  the  safe  operation  of  the  system.  The  Italian 
transmission  system  is  constituted  by  about  11000  km  of  380 kV  lines, 9500  km  of  220 kV  lines 
and more than 37000 km of HV lines at a voltage level lower than 150 kV.  

On  the  other  hand,  the  distribution  network  is  owned  and  operated  by  138  Distribution  System 
Operators (DSOs), that supply about 37 millions of withdrawal points. 

The  use  of  the  network,  both  at  the  distribution  and  transmission  level,  is  charged  to  the 
consumers separately from the costs of the supplied energy. The tariffs for the use of the network 
are regulated by the authority (AEEGSI) and constantly updated. The tariffs include the use of the 
grid infrastructure including power transforming and switching. Tariffs don’t include losses and the 
balancing  services,  that  are  charged  separately.  Figure  Annex1-7  shows  how  the  revenues  from 
tariffs for the use of the network are transferred from the users to the grid operators. 

The  domestic  users  pay  to  the  DSO  a  single  tariff  (D2  or  D3)  that  includes  the  services  of 
transmission,  distribution  and  measurement.  On  the  other  hand,  the  non-domestic  users  are 
charged  with  two  different  tariffs:  one  for  the  transmission  service  (TRAS)  and  one  for  the 
distribution service (DISTR). Finally DSOs, on behalf of consumers, pay to the TSO a tariff for the 
transmission service.  

Figure Annex1-7: Charge scheme of tariffs for the use of the network. 

Table  Annex1-1  shows  the  tariff  for  transmission  service,  in  force  in  2015  in  Italy  for  a  non-
domestic user. It can be noted that for consumers connected at the high voltage level the tariff is 
charged  both  on  the  peak  power  consumptions  and  on  the  energy  supplied.  In  particular  the 
TRASp  component  is  charged  monthly  (152.8  c€/kW-month)  and  is  applied  to  the  monthly  peak 
power consumption measured in any quarter of an hour in each month. 

In Table Annex1-2 tariffs for the distribution service for non-domestic users are reported. 

Table Annex1-1: Tariffs for the transmission service for non-domestic users. 

Annex1 - 9 

 
 
 
 
  
TRASp [c€/kW-year] 

TRASe [c€/kWh] 

Low voltage user 

Medium voltage user 

High voltage user (<220 kV) 

Ultra high voltage user (≥220 kV) 

- 

- 

1833.60 

1833.60 

0.69 

0.644 

0.105 

0.104 

Table Annex1-2: Tariffs for the distribution service for non-domestic users. 

Low voltage user - maximum power up to 16,5 kW 

≤ 1.5 kW 

1.5 kW - 3 kW 

3 kW - 6 kW 

6 kW - 10 kW 

≥ 10 kW 

Fixed 

Power 

Energy 

c€/year 

c€/kW 

c€/kWh 

503.07 

3'242.26 

0.067 

503.07 

3'070.72 

0.067 

503.07 

3'413.82 

0.067 

553.38 

3'413.82 

0.067 

553.38 

3'413.82 

0.067 

Low voltage user - maximum power ≥ 16,5 kW 

503.07 

3'242.26 

0.064 

Medium voltage user - maximum power up to 100 kW 

47'719.60 

3'666.49 

0.063 

Medium voltage user - maximum power 100 kW - 500 kW 

42'947.63 

3'292.36 

0.057 

Medium voltage user - maximum power ≥ 500 kW  

41'495.30 

2'888.30 

0.049 

High voltage user 

Ultra high voltage user 

2'098'682.3
6 

2'098'682.3
6 

- 

- 

0.021 

- 

Beside  the  network  tariffs,  each  energy  consumer  pays  a  set  of  “system  charges”  to  finance 
activities  of  public  interest  in  the  energy  sector  such  as  renewable  support  schemes,  energy 
efficiency,  public  research.  About  these  system  charges,  a  recent  decree  of  the  Ministry  of 
Economy (5th April 2013) introduced new exemptions for the large industrial consumers connected 
to the medium and high voltage network. For these consumers the system charges for the energy 
exceeding a certain threshold are zero; moreover below that threshold, energy intensive industries 
can  benefit  from  discounts,  whose  amount  depends  on  the  ratio  between  energy  costs  and 
turnover of the industry.  

Annex1 - 10 

 
 
 
  
  
  
  
 
 
 
 
 
 
Finally the general structure of the energy price for the end users is reported. The cost components 
are: 

  Sales services – including the cost of energy, the cost of dispatching services and the cost 

of the retail sale activity; 

  Network services – including the tariffs for the transmission, distribution and measurement 

services; 

  System  charges  –  including  the  costs  of  activities  of  public  interest  such  as  renewable 

support schemes, energy efficiency incentive, public research, etc.; 

  Taxes – including the excise tax and VAT. 

Figure  Annex1-8  shows  the  evolution the  electricity  price  for  a domestic  user  between  2012  and 
2014, with the detail of single cost components. 

Figure Annex1-8: Evolution of electricity price components for household customers in Italy 
[c€/kWh]. 

Annex1 - 11 

 
 
  
 
2.1.2  Germany 

12.  Generation, 
The  structure  of  the  electricity  market  in  Germany  is  shown  in  Figure  Annex1-9  11 F
transmission and distribution of power is today managed by separate companies in order to foster 
the competition for the production and distribution of power. 

Figure Annex1-9: Structure of the Power Market in Germany. 

The  Energy  Act  assigned  the  task  of  regulating  Germany's  electricity  and  gas  markets  to  the 
Federal Network Agency (Bundesnetzagentur, http://www.bundesnetzagentur.de/). The purpose of 
regulation  is  to  establish  fair  and  effective  competition  in  the  supply  of  electricity  and  gas.  The 
responsibilities of the Federal Network Agency therefore include ensuring non-discriminatory third-
party access to networks and policing the use-of-system charges levied by market players. A non-
discriminatory,  reasonably  priced  supply  of  electricity  and  gas  for  all  who  want  it  will  encourage 
greater competition in these important (wholesale) markets. The Federal Network Agency will use 
the regulatory experience it has gained in the telecommunications and postal markets to implement 
a lean and efficient system of regulation.  

Within  Germany  the  federal  electricity  grid  infrastructure  is  subdivided  among  4  TSOs,  namely 
Tennet,  Amprion,  Transnet  BW  and  50  Hertz,  that  operate  the  transmission  grids  independently 
from  other  electricity  market  players.  They  are  responsible  for  the  bulk  transmission  of  electric 
power  on  the  main  high  voltage  electric  networks.  TSOs  provide  grid  access  to  the  electricity 
market  players  according  to  non-discriminatory  and  transparent  rules.  In  order  to  ensure  the 
security  of  supply,  they  also guarantee  the  safe operation  and maintenance  of the  system.  They 

12 EEXSpot: Annual Report 2013. 

Annex1 - 12 

 
 
 
 
                                                
 
are members of the European Network of Transmission System Operators for Electricity, ENTSO-
E. The market areas of these companies are depicted in Figure Annex1-10. 

Figure Annex1-10: TSOs in Germany. 

Table Annex1-3 gives an overview and the links to the homepages of the relevant TSO companies. 

Table Annex1-3: Transmission System Operators (TSO's) in Europe and Germany. 

TSO Company 

European Transmission System 
Operators 
TransnetBW GmbH 

Link 

https://www.entsoe.eu 

http://www.transnetbw.de/de 

TenneT TSO GmbH 

http://www.tennet.eu/de/home.html 

Amprion GmbH 

http://www.amprion.net 

50Hertz Transmission GmbH 

http://www.50hertz.com/de 

Übertragungsnetzbetreiber (ÜNB) 

www.netztransparenz.de 

Management of regulation energy 

www.regelleistung.net 

Development of German grids 

www.netzentwicklungsplan.de 

Annex1 - 13 

 
 
 
 
 
In Germany the grids are divided in different voltage areas: 

  Maximum voltage (Höchstspannungsnetz)   
  High voltage (Hochspannungsnetz)   
  Medium voltage (Mittelspannung) 

220 k V and 380 kV 
110 kV (partly also 60 kV) 
10 / 20 kV and 15 / 30 kV 

The operation of grids and the transformation of voltage is charged separately to the costs  of the 
supplied  energy.  The  tariffs  of  the  four  German  TSO´s  are  published  on  their  homepages.  The 
tariffs are charged for the use of following services: 

  use of the infrastructure of the grid including power transforming and switching, 
  use of the services for frequency and voltage stabilization, 
  energy losses from transforming, 
 

reactive power. 

Table Annex1-4 shows a comparison of these tariffs of the German TSOs. The peak load that is 
measured as a mean value of intervals of ¼ of an hour can be measured and charged either for 
single months or for the whole year. Therefore the tariffs are different. 

Table Annex1-4: Costs for the use of the grids in 2015 incl. power transforming into high voltage for 
companies with use of the grid for more than 2.500 h/a. 

Measurement 
the peak load 

of 

Transnet-
BW 

Tennet 

Amprion 

50Hertz 

per year 

per month 

€/kW a 

€ct/kWh 

€/kW m 

€ct/kWh 

53.82 

0.03 

-- 

-- 

56.30 

0.13 

9.38 

0.13 

35.99 

0.176 

6.00 

0.176 

60.79 

0.14 

-- 

-- 

Additional fees are charged if the reactive power exceeds the limits that have been declared in the 
contract. 

Furthermore different fees and levies are charged to the end-consumer of energy due to different 
national laws: 

levies due to the “Kraft-Wärme-Kopplungsgesetz“ (KWKG), 
levies due to the “Erneuerbaren Energie Gesetz” (EEG), 

 
 
  extra expenses due to the requirements of § 19 Abs. 2 StromNEV, 
 
  value added taxes and energy taxes. 

licences of the grid operator to communities, 

Table Annex1-5: Fees due to different laws in Germany for energy-intensive industries in 2015 12F

13 

Levy or Fee 

EEG-Umlage 

Law 

EEG 

Costs 

Usually:  6.24  €ct/kWh.  but 
energy-intensive 

for 
industry:  0.05 

13 www.netztransparenz.de  

Annex1 - 14 

 
 
 
 
 
 
 
                                                
 
€ct/kWh 

KWK-Aufschlag (für 2015)  KWKG 

0.025 €ct/kWh 

Umlage StromNEV 

Offshore-Haftungs-
Umlage 

§  19  Strom 
NEV 

0.025 €ct/kWh 

§ 17f EnWG 

0.025 €ct/kWh 

Umlage 
Lasten 

abschaltbare 

§ 18 AbLaV 

0.006 €ct/kWh 

Konzessionsabgabe 

NAV 

0.11 – 0.13 €ct / kWh 

Tax on power generation 

StromStG 

up to 2.05 €ct/kWh, but reduced for 
producing 
energy-intensive 
industry 

/ 

The power generation in Germany has changed rapidly due to an increasing use of renewables for 
power  generation.  Additional  capacities  for  Wind  and  Solar  power  have  been  installed.  Figure 
Annex1-11  shows  the  installed  capacities  for  power  generation  with  the  use  of  different  primary 
energies in 201413F

14. 

Figure Annex1-11: Installed capacities for power generation with use of different primary energies in 

201414F

15. 

Figure  Annex1-12  shows  the  generation  of  power  in  2014  in  Germany  and  the  use  of  different 
primary energies.  

14 Burger, B.: Stromerzeugung aus Solar und Windenergie im Jahr 2014, Fraunhofer ISE. 
15 Burger, B.: Stromerzeugung aus Solar und Windenergie im Jahr 2014, Fraunhofer ISE. 

Annex1 - 15 

 
 
 
 
 
 
                                                
 
Figure Annex1-12: Power production by the use of different primary energy sources in Germany 
201415F16. 

In  2014  in  total  521,5  TWh  of  net  power  has  been  produced  including  156,6  TWh  produced  of 
renewable sources. The share of renewable energy was 30%. 

Figure Annex1-13 shows the relevant submarkets for electricity in Germany. The power exchange 
includes the trading of energy and the delivery services as well and needs an additional balancing 
of the grids. 

Figure Annex1-13: Submarkets of the electricity market in Germany, chronological representation 16F

17. 

The energy exchange markets for power typically include: 

 
 
 

the forward market, 
the day-ahead spot market, 
the intra-day spot market. 

16 Fraunhofer ISE, Freiburg 2014. 
17 An Electricity Market for Germany’s Energy Transition. Discussion Paper of the Federal Ministry for Economic Affairs and Energy, Oct. 2014. 

Annex1 - 16 

 
 
 
 
 
                                                
 
All such kinds of markets foresee the physical delivery of energy or future financial derivatives.  In 
this  report  we  will  focus  on  the  day-ahead  and  on  the  intra-day  markets,  i.e.  the  ones  that  can 
provide short-term flexibility to electricity consumption. 

In Germany the Phelix index has been defined (Physical Electricity Index) for a comparison of the 
prices of the products traded at the EEX. Within the forward market future derivatives and options 
are offered for the time slots of the “base load”, “peak load” or “off-peak” of the day, valid for daily, 
weekly,  monthly,  quarterly  and  yearly  delivery  periods.  These  products  were  designed  to  supply 
the bulk of the energy consumption but they do not provide flexibility.  

The  day-ahead  and  intra-day  spot  markets  provide  more  flexibility  for  short-term  contracts.  New 
markets and opportunities to reduce the energy costs are given today by the balancing market and 
the opportunities for a demand side management as well. 

The  different  price  components  of  power  that  have  to  be  paid  in  Germany  are  shown  in  Figure 
Annex1-14 for household customers. These cost elements are mainly relevant for: 

  costs for the procurement of energy, 
 
 
 

tariffs for the use of the network, grid and transformation of power, 
levies as a kind of cross-subsidization, mainly for renewable energy, 
taxes e.g. for the use of fuels and the power production itself. 

Figure Annex1-14: Evolution of electricity price components for household customers 17F

18. 

The power costs for industrial processes are significantly lower than that for household customers 
because taxes and levies are reduced for energy intensive industries. 

18 Bundesnetzagentur, Annual Report 2013. 

Annex1 - 17 

 
 
 
 
 
 
 
                                                
 
3  Power Exchange 

3.1  Day-ahead spot market 

The  day-ahead  spot  market  is  typically  the  most  important  one,  in  terms  of  volumes  of  energy 
traded and of significance of the clearing prices.  

In the following an analysis of the day-ahead spot markets in Italy and in Germany is provided. 

3.1.1  Italy 

The Italian day-ahead spot market is called Mercato del Giorno Prima  – MGP. The Italian Power 
Exchange,  including  the  day-ahead  spot  market,  is  managed  by  GME  -  Gestore  dei  Mercati 
Energetici (www.mercatoelettrico.org).  

3.1.1.1  Market description 

The  Italian  day-ahead  spot  market  is  not  a  mandatory  market,  i.e.  it  is  also  possible  to  trade 
electric energy outside the market (Over The Counter – OTC) through bilateral contracts. 

In  the  day-ahead  spot  market,  producers,  wholesalers  and  consumers  may  sell  /  buy  electric 
energy  for  each  hour  of  the  next  day,  by  submitting  supply  offers  /  demand  bids  in  the  relevant 
market session. 

The session relevant for the day D starts at 08:00 of D-9 and closes at 09:15 of the day D-1 (i.e. 
the day-ahead). 

Offers / bids consist of pairs of values, i.e. volume and unit price of electric energy (MWh, €/MWh). 
They express the willingness to sell / buy a volume of energy not higher than the one specified in 
the offer / bid at a price not lower / not higher than the one specified in the same offer / bid. 

Prices and volumes must not be negative and demand bids may also not specify any purchasing 
price; in this case, they express the market participant’s willingness to purchase electric energy at 
any price. The highest price considered by the market clearing algorithm is 3000 €/MWh, i.e. the 
Value of Energy Not Supplied defined by the Authority for Electric Energy, Gas and Hydric System 
19].  Demand  bids  that  do  not  specify  any  purchasing 
(AEEGSI),  the  Italian  regulatory  authority  [ 18F
price are therefore equivalent to bids at 3000 €/MWh. 

Offers / bids may be: 

  simple,  consisting  of  a  pair  of  values  indicating  the  volume  of  electric  energy  to  be  sold  or 

bought in the market by a market participant and the related price for a given hour; 

19 AEEGSI Order no. 111/06: “Condizioni per l’erogazione del pubblico servizio di dispacciamento dell’energia elettrica 
sul  territorio  nazionale  e  per  l’approvvigionamento  delle  relative  risorse  su  base  di  merito  economico,  ai  sensi  degli 
articoli 3 e 5 del decreto legislativo 16 marzo 1999, n. 79”. 

Annex1 - 18 

 
 
 
 
 
 
 
                                                
 
  multiple,  consisting  of  a  set  of  at  most  4  simple  offers  /  bids  that  allow  to  split  with  different 
prices an overall volume to be sold or bought in the market by the same market participant for 
the same hour, the same generating unit or the same withdrawal point; 

  pre-defined,  consisting  of  simple  or  multiple  offers  /  bids  which  are  daily  repeatedly 

automatically submitted to the market. 

Offers / bids refer to “offer points” and to individual hours: this means that, for each day and each 
“offer  point”,  a  maximum  of  24  independent  offers  /  bids  may  be  submitted.  Supply  offers  and 
demand  bids,  to  be  considered  valid,  must  be  consistent  with  the  injection  or  withdrawal 
capabilities of the “offer points” to which they refer and, above all, they must correspond to the real 
willingness to inject or withdraw the related volumes of electric energy. 

20] an “offer point” is a “dispatching point” as defined in the dispatching regulation [19] 
According to [ 19F
21] issued by the Italian Transmission System Operator 
issued by AEEGSI and in the Grid Code [ 20F
TERNA. As far as consumption units are concerned, a “dispatching point” is the set of withdrawal 
points corresponding to consumption units located in a single “zone” and for which a “dispatching 
user” signed a contract for transmission and distribution services. 

In fact, for each “offer point”, a “dispatching user” is identified. This user is responsible towards the 
Italian  TSO TERNA  both for the  implementation  of  injection  and  withdrawal  schedules  defined in 
the  market  and  for  the  execution  of  balancing  commands.  These  commands  may  be  sent  by 
TERNA  to  dispatching  users  in  real  time  in  order  to  maintain  the  security  of  the  system.  Non-
compliance with the final  schedules involves  the  payment  of  imbalance charges,  applied  to each 
offer point. 

As far as “zones” are concerned, the power system is divided into portions of the transmission grid 
(called “zones”) where, according to security requirements, there are physical limits to transmission 
of  electric  energy  to/from  the  corresponding  neighboring  zones.  These  transmission  limits  are 
determined  through  a  computational  model  that  is  based  on  the  balance  between  electricity 
generation and consumption in different scenarios, taking into account the impact of possible faults 
of network elements and aiming to satisfy the “N-1” security criterion. According to this criterion the 
power system must in any circumstance withstand the fault of one network element and remain in 
safe operation. 

The identification of the zones and the definition of transmission capacities between them is made 
by the Italian TSO TERNA. The zones may correspond to physical geographical areas, to virtual 
areas (i.e. not directly corresponding to physical areas) and to “limited production poles”, i.e. virtual 
zones composed of a set of generators whose overall maximum power production is higher than 
the available transmission capacity connecting them to the neighboring zones. 

The “geographical zones” are currently 6:  Northern Italy, Central-Northern Italy, Central-Southern 
Italy, Southern Italy, Sicily and Sardinia (see Figure Annex1-15), for each of which the transmission 
capacity to / from the neighboring zones / poles is defined by TERNA. 

20  GME:  “Testo  integrato  della  disciplina  del  mercato  elettrico  approvato  con  D.M.  del  19  dicembre  2003  come 
successivamente modificato e integrato”. 
21 TERNA: “Codice di Rete – Capitolo 4 – Regole per il dispacciamento”. 

Annex1 - 19 

 
 
                                                
 
Figure Annex1-15: Geographical zones of the Italian electricity market (source: GME). 

22,  Corsica,  Corsica  AC22F

The “virtual zones” are currently 8 and correspond to neighboring countries (France, Switzerland, 
23  and  Greece).  The  Italian  transmission  grid  is 
Austria,  Slovenia,  BSP21 F
interconnected with neighboring countries via 22 lines: 4 with France; 12 with Switzerland; 1 with 
Austria;  2  with  Slovenia;  1  direct-current  submarine  cable  with  Greece,  in  addition  to  the  SACOI 
direct-current  cable  linking  Sardinia  to  mainland  Italy  through  Corsica,  and  an  additional 
alternating-current cable between Sardinia and Corsica and the SAPEI direct-current link between 
Sardinia  and  mainland  Italy.  All  these  lines  and  cables  are  aggregated  into  “equivalent” 
interconnections  between  Italy  and  each  neighboring  country, for  each of  which the  transmission 
capacity to / from Italy is defined by TERNA. 

The  “limited  production  poles”  are  currently  4:  Foggia,  Brindisi,  Rossano  and  Priolo,  for  each  of 
which the transmission capacity to the neighboring geographical zones is defined by TERNA. 

22 It corresponds to the Slovenian electricity market, coupled with the Italian one with a “price coupling” algorithm. 
23 It corresponds to the Corsica island interconnected through an Alternating Current (AC) cable. 

Annex1 - 20 

 
 
                                                
 
To  manage  any  congestion  which  may  be  caused  by  injection  or  withdrawal  schedules  (whether 
defined in the market or deriving from bilateral contracts) and to clear the market, GME uses the 
map of geographical and virtual zones shown in Figure Annex1-16.  

Figure Annex1-16: Map of the geographical and virtual zones used by GME for congestion 
management and market clearing (source: GME). 

Before the 09:15 “gate closure” of the market session, GME receives by  the Italian Transmission 
System Operator TERNA and publishes at 08:45 the following information, that could be of interest 
for market participants: 

the maximum hourly transmission capacity between geographical zones, 
the maximum hourly transmission capacity with foreign market zones, 
the maximum hourly export capacity from limited production poles, 

 
 
 
  an estimation of hourly electric energy demand for each geographical zone. 

After  the  09:15  “gate  closure”,  GME  activates  the  market  clearing  process.  For  each  hour  of  the 
following day, the market algorithm accepts offers / bids in such a way as to maximize the value of 
transactions, while complying with the maximum transmission limits between zones. 

The clearing process may be summarized as follows: 

  All valid supply offers are ranked in increasing price order on an aggregate supply curve and all 
valid demand bids are ranked in decreasing price order on an aggregate demand curve. The 
intersection of the two curves gives (see Figure Annex1-17): 

o 
o 
o 
o 

the overall traded volume, 
the clearing price, 
the accepted offers / bids, 
the  injection  and  withdrawal  schedules  obtained  as  the  sum  of  the  accepted  offers  / 
bids pertaining to the same hour and to the same “offer point”. 

Annex1 - 21 

 
 
 
 
 
Figure Annex1-17: Schematization of the market clearing process (source: GME). 

 

 

If  the  flows  on  the  grid resulting  from  the  schedules  do  not  violate  any  transmission  capacity 
limit, the clearing price is the same in all the zones and equal to P*. Accepted offers / bids are 
those having a selling price not higher than P* and a purchasing price not lower than P*. 
If at least one transmission capacity limit is violated, the algorithm “splits” the market into two 
aggregates  of  geographical  and/or  virtual  zones  called  “market  zones”:  an  exporting  market 
zone, including all the zones upstream the constraint, and an importing market zone, including 
all  the  zones  downstream  the  constraint.  Then,  the  algorithm  repeats  the  above-mentioned 
clearing process and, for each market zone, it builds an aggregate supply curve (including all 
the supply offers submitted in the same zone, as well as the maximum importable volume) and 
an aggregate demand curve (including all the demand bids submitted in the same zone, as well 
as the maximum exportable volume). The result is a zonal clearing price (Pz), which is different 
in the two market zones. In particular, Pz is higher in the importing market zone and lower in 
the  exporting  one. If,  as  a  result  of this  solution,  additional  transmission  capacity  limits  within 
each market zone are violated, the “market splitting” process is repeated again within this zone 
until  a  result  which  is  compliant  with  all  transmission  capacity  limits  is  obtained.  The  “market 
splitting” mechanism is a non-discriminatory implicit auction for the assignment of transmission 
rights.  This  process  is  also  called  “congestion  management”  since  in  this  way  all  inter-zonal 
network congestions are removed. 

  The  zonal  price  Pz  is  applied  to  all  sales  and  purchases  of  energy  related  to  injection  offer 
points (i.e. generators), to mixed offer points (e.g. pumped storage hydro power plants) and to 
withdrawal offer points belonging to neighboring countries’ virtual zones. 

  On  the  contrary,  all  withdrawal  offer  points  belonging  to  national  geographical  zones  pay  the 
same  Uniform  National  Purchase  Price,  called  PUN  (Prezzo  Unico  Nazionale).  The  PUN  is 
equal to the average of zonal selling prices Pz, weighted on zonal electric energy demand. 

Annex1 - 22 

 
 
 
The overall market clearing algorithm above described can be summarized as reported in  Figure 
Annex1-18. 

Figure Annex1-18: Schematization of the market clearing algorithm (source: GME). 

The  electric  energy  traded  Over-The-Counter  through  bilateral  transactions  (that  must  be 
registered  onto  a  specific  platform  managed  by  GME,  i.e.  the  Piattaforma  Conti  Energia  –  PCE) 
participates  to  the  above-described  process,  since  it  contributes  both  to  use  a  share  of  the 
available  inter-zonal  transmission  capacity  and  to  determine  the  volumes  to  be  weighted  for  the 
Uniform National Purchase Price (PUN). 

The  schedules  registered  onto  the  PCE  are  therefore  submitted  to  the  day-ahead  market  in  the 
form  of  pairs  of offers  / bids  and  in such  a  way  contribute  to  determine the  results  of  the  market 
itself. To this aim, for bilateral contracts registered by operators enabled to participate to the day-
ahead  market,  a  price  must  be  specified  in  the  registration  and  such  price  characterizes  the 
corresponding pair of offers / bids submitted to the market. On the contrary, for bilateral contracts 
registered  by  operators  not  enabled  to  participate  to  the  day-ahead  market,  an  offer  at  a  price 
equal to zero and a bid without purchasing price specification is submitted to the market. 

The results of the day-ahead market are then communicated to each market participant at 10:45. 

Annex1 - 23 

 
 
 
 
 
 
 
3.1.1.2  Market Coupling 

In  the  first  half  of  2015  the  Italian  day-ahead  market  will  join  the  Europe-wide  Price  Coupling  of 
24 that puts together all the day-ahead markets of the different European 
Regions – PCR initiative23 F
countries  and  clears  them  with  a  single  algorithm  that  preserves  the  peculiarities  of  the  different 
national markets (e.g. types of offers, PUN in the Italian market, etc.). 

The  price coupling  algorithm manages congestion  between the  different countries with  a “market 
splitting”  where  each  zone  is  a  country,  or  a  subset  of  a  country  (like  in  Italy,  that  is  in  turn 
subdivided  into  zones).  In  such  a  way  cross-border  available  transmission  capacity  is  optimally 
used,  differently  from  the  previous  situation  where  transmission  capacity  was  allocated  in 
dedicated auctions, before knowing the electric energy prices that would have been subsequently 
defined  in  the  day-ahead  markets.  In  such  situation  it  could  happen  that  between  two  countries 
with  different  electric  energy  prices  not  all  the  available  transmission  capacity  is  used  or,  worse, 
the  energy  flow  goes  from  the  highest  price  country  to  the  lowest  price  one  (so-called  “adverse 
flow”), leading to a non-optimal use of the transmission capacity itself. 

At the beginning the only impact of the price coupling on the Italian day-ahead market procedures 
will be a change of the “gate closure” time, that will be moved from the current 09:15 to 12:00, like 
the other European markets. Moreover, the price limits (0 €/MWh ÷ 3000 €/MWh) will remain the 
current ones, even if negative prices are allowed in the other European markets. 

3.1.1.3  Analysis of recent market results 

The day-ahead market of the Italian Power Exchange in November 2014 accounted for 63.5% of 
the  overall  electric  energy  trades  (“Liquidity”:  see  Figure  Annex1-19).  The  remaining  36.5%  was 
traded  Over-The-Counter  with  bilateral  contracts.  From  Figure  Annex1-19  it  can  be  seen  that 
liquidity was higher in the first half of 2013, when it reached almost 80%. 

Figure Annex1-19: “Liquidity” of the day-ahead market of the Italian Power Exchange (source: GME). 

24 The Price Coupling of Regions (PCR) is an initiative by 7 European Power Exchanges (APX-ENDEX, Belpex, EPEX SPOT, 
GME,  Nord  Pool  Spot,  OMIE  and  OTE)  operating  in  Austria,  Belgium,  Czech  Republic,  Denmark,  Estonia,  Finland, 
France, Germany, Italy, Latvia, Lithuania, Luxembourg, the Netherlands, Norway, Portugal, Spain, Sweden, Switzerland 
and the United Kingdom. 

Annex1 - 24 

 
 
 
 
 
 
                                                
 
Since  we  are  specifically  interested  in  purchases  of  electric  energy  on  the  day-ahead  market,  to 
look for opportunities to take profit from demand flexibility, in the following the analysis will focus on 
the Uniform National Purchase Price – PUN in the one-year period between December 2013 and 
November 2014. 

The  following  Figure  Annex1-20  shows  hourly  PUN  values  in  the  considered  period.  PUN 
variability is clear: values range from 2.23 €/MWh to 149.43 €/MWh. 

Figure Annex1-20: Hourly PUN values from December 2013 to November 2014 (source: RSE 
elaboration on GME data). 

Annex1 - 25 

 
 
 
 
Figure Annex1-21: PUN monthly average (source: RSE elaboration on GME data). 

In Figure Annex1-21 the PUN monthly average is shown. The months from March to August show 
the  lowest  values,  also  due  to  the  high  renewable  generation  (especially  photovoltaic)  that 
characterizes such months and that is offered in the day-ahead market at a price equal to zero. 

In  Figure  Annex1-22  the  PUN  monthly  average  in  working  days  and  in  weekends  and  holidays 
(non-working  days)  is  shown.  Since  demand  in  non-working  days  is  lower,  also  electric  energy 
prices are lower. In the considered period, PUN reduction in non-working days ranged from about 
6% (2.7 €/MWh) to about 30% (14.2 €/MWh). 

Annex1 - 26 

 
 
 
 
Figure Annex1-22: PUN monthly average in working days and in weekends and holidays (source: RSE 
elaboration on GME data). 

In Figure Annex1-23 the average hourly PUN profile is shown. In the considered period the PUN 
on average reached the lowest minimum between 3 and 5 a.m. (when demand is low), followed by 
a  first  peak  from  8  to  9  a.m.  (demand  rises  steeply  and  photovoltaic  generation  is  still  low), 
followed by a second minimum from 1 to 2 p.m. (photovoltaic generation is high) and then followed 
by the highest peak from 7 p.m. to 8 p.m. (photovoltaic generation stops and demand is still high). 

In Figure Annex1-24 the PUN hourly average difference w.r.t. the overall annual average is shown. 
In the considered period, consuming electric energy between 11 p.m. and 7 a.m. and between 11 
a.m.  and  4  p.m.  would  have  allowed  to  pay  a  price  lower  than  the  annual  average  (saving  from 
about 1 €/MWh to about 15 €/MWh), while in the remaining hours the price paid would have been 
higher than the annual average (range from about +2 €/MWh to about +19 €/MWh). 

Annex1 - 27 

 
 
 
Figure Annex1-23: Average hourly PUN profile (source: RSE elaboration on GME data). 

Figure Annex1-24: PUN hourly average difference w.r.t. the overall annual average (source: RSE 
elaboration on GME data). 

Annex1 - 28 

 
 
 
In  terms  of  average  percentage  difference  (Figure  Annex1-25),  in  the  considered  period, 
consuming  electric  energy  between  11  p.m.  and  7  a.m.  and  between  11  a.m.  and  4  p.m.  would 
have allowed to pay a price lower than the annual average (saving from about 2% to about 28%), 
while  in  the  remaining  hours  the  price  paid  would  have  been  higher  than  the  annual  average 
(range from about +3% to about +36%). 

Figure Annex1-25: PUN hourly average percentage difference w.r.t. the overall annual average 
(source: RSE elaboration on GME data). 

It is also interesting to look at the differences of the average hourly PUN profiles in working days 
and  in  weekends  and  holidays  (non-working  days):  see  Figure  Annex1-26.  In  the  considered 
period,  from  8  p.m.  to  5  a.m.  PUN  was  on  average  similar  both  in  working  and  in  non-working 
days. In the remaining hours working days PUN was significantly higher. 

In Figure 2 the PUN hourly average difference w.r.t. the overall annual average of the same type of 
day  in working days and in weekends and holidays (non-working days) is shown. It can be seen 
that in the considered period the differences were lower in non-working days than in working ones 
in the first half of the day, while in the second half they were higher. 

Figure  Annex1-28  shows  that  in  working  days  the  difference  ranged  about  from  -30%  to  +30%, 
while in non-working days the difference ranged about from -30% to +50%. 

Annex1 - 29 

 
 
 
 
Figure Annex1-26: Average hourly PUN profiles in working days and in weekends and holidays 
(source: RSE elaboration on GME data). 

Figure Annex1-27: PUN hourly average difference w.r.t. the overall annual average of the same type 
of day in working days and in weekends and holidays (source: RSE elaboration on GME data). 

Annex1 - 30 

 
 
 
Figure Annex1-28: PUN hourly average percentage difference w.r.t. the overall annual average of the 
same type of day in working days and in weekends and holidays (source: RSE elaboration on GME 
data). 

In  Figure  Annex1-29,  the  average  hourly  PUN  profiles  in  December  2013  and  in  January  and 
February  2014  are  shown.  The  profiles  in  the  three  months  are  similar  (except  for  the  more 
pronounced  evening  peak  from  5  p.m.  to  6  p.m.  in  December),  but  with  a  progressive  price 
reduction. 

The  profiles  of  working  days,  shown  in  Figure  Annex1-30,  are  not  so  different  from  the  overall 
ones,  while  profiles  of  non-working  days  (Figure  Annex1-31)  show  significantly  lower  prices, 
especially  in  February,  when  PUN,  in  the  last  hours  of  the  night  and  in  the  first  hours  of  the 
afternoon, on average is around 25 €/MWh. 

Annex1 - 31 

 
 
 
Figure Annex1-29: Average hourly PUN profile in December 2013 and in January and February 2014 
(source: RSE elaboration on GME data). 

Figure Annex1-30: Average working days hourly PUN profile in December 2013 and in January and 
February 2014 (source: RSE elaboration on GME data). 

Annex1 - 32 

 
 
 
Figure Annex1-31: Average non-working days hourly PUN profile in December 2013 and in January 
and February 2014 (source: RSE elaboration on GME data). 

In  Figure  Annex1-32,  the average hourly  PUN  profiles  in  March,  April  and May  2014  are  shown. 
The profiles in the three months are similar, except for the higher night prices in May and for the 
more  pronounced  and  anticipated  by  one  hour  evening  peak  in  March,  when  the  sunset  occurs 
earlier. 

The  profiles  of  working  days,  shown  in  Figure  Annex1-33,  are  not  so  different  from  the  overall 
ones, while profiles of non-working days (Figure Annex1-34) show a much lower morning peak and 
significantly lower prices in the first hours of the afternoon, with PUN in March and in May below 20 
€/MWh. On the contrary, the evening peak reaches values similar to working days. 

Annex1 - 33 

 
 
 
Figure Annex1-32: Average hourly PUN profile in March, April and May 2014 (source: RSE elaboration 
on GME data). 

Figure Annex1-33: Average working days hourly PUN profile in March, April and May 2014 (source: 
RSE elaboration on GME data). 

Annex1 - 34 

 
 
 
Figure Annex1-34: Average non-working days hourly PUN profile in March, April and May 2014 
(source: RSE elaboration on GME data). 

In Figure Annex1-35, the average hourly PUN profiles in June, July and August 2014 are shown. 
The profiles in the three months are similar and quite “flat”: prices are almost always between 40 
and 60 €/MWh. 

The  profiles  of  working  days,  shown  in  Figure  Annex1-36,  are  not  so  different  from  the  overall 
ones, while profiles of non-working days (Figure Annex1-37) show a quite low morning peak and 
significantly  lower  prices  in  the  first  hours  of  the  afternoon,  with  PUN  in  June  approaching  20 
€/MWh. On the contrary, the evening peak reaches values similar to working days. 

Annex1 - 35 

 
 
 
Figure Annex1-35: Average hourly PUN profile in June, July and August 2014 (source: RSE 
elaboration on GME data). 

Figure Annex1-36: Average working days hourly PUN profile in June, July and August 2014 (source: 
RSE elaboration on GME data). 

Annex1 - 36 

 
 
 
Figure Annex1-37: Average non-working days hourly PUN profile in June, July and August 2014 
(source: RSE elaboration on GME data). 

In Figure Annex1-38, the average hourly PUN profiles in September, October and November 2014 
are shown. In the first half of the day the profiles of September and of October are similar, while 
the  profile  of  November  shows  significantly  lower  prices.  In  the  second  half  of  the  day,  profiles 
differentiate for the evening peak, that occurs earlier according to the sunset in the three months 
and is much higher in October. 

The  profiles  of  working  days,  shown  in  Figure  Annex1-39,  are  not  so  different  from  the  overall 
ones, while profiles of non-working days (Figure Annex1-40) show a quite low morning peak (with 
much lower prices in November) and lower prices in the first hours of the afternoon. The evening 
peak reaches values about 10 €/MWh lower than working days. 

Annex1 - 37 

 
 
 
 
Figure Annex1-38: Average hourly PUN profile in September, October and November 2014 (source: 
RSE elaboration on GME data). 

Figure Annex1-39: Average working days hourly PUN profile in September, October and November 
2014 (source: RSE elaboration on GME data). 

Annex1 - 38 

 
 
 
Figure Annex1-40: Average non-working days hourly PUN profile in September, October and 
November 2014 (source: RSE elaboration on GME data). 

Annex1 - 39 

 
 
 
 
3.1.2  Germany 

3.1.2.1  Market description 

In Germany the trading of energy is managed by the European Energy Exchange (EEX). It is the 
leading  energy  exchange  in  Europe.  It  develops,  operates  and  connects  secure,  liquid  and 
transparent  markets  for  energy  and  related  products.  On  the  EEX  markets,  power,  natural  gas, 
CO2 emission allowances, coal and guarantees of origin are traded. Clearing as well as financial 
and physical settlement of all trading transactions are provided by European Commodity Clearing 
(ECC).  ECC  is  a  subsidiary  of  EEX  and  also  provides  clearing  services  for  other  European 
exchanges. EEX Group has its headquarters in Leipzig with offices in London, Paris and Brussels. 

The EEX Market for the trade of power has three different segments: 

1)  the Spot Market (trade volume in 2013: 346 TWh) including the day-ahead market and the 

intra-day market; 

2)  the Derivatives Market (trade volume in 2013: 1264 TWh); 
3)  the Trade Registration. 

The  power  Spot  Market  for  Germany,  France,  Austria  and  Switzerland  is  operated  by  EPEX 
SPOT,  a  joint  venture  between  EEX  and  the  French  power  exchange  Powernext.  Further 
information  on  EPEX  SPOT  is  available  at  www.epexspot.com.  On  this  market,  power  products 
with delivery on the same day (Intra-day) or on the following day (Day-ahead) are traded. The spot 
market permits the short-term optimization of procurement and sale. 

Within  the  Derivatives  Market  trading  transactions  are  only  fulfilled  financially  or  physically  at  a 
specific  time  agreed  in advance.  The  derivatives  market  facilitates  medium  to  long-term  portfolio 
optimisation. Hedging against risks of price changes is possible for up to six years in advance. 

The Trades have to be registered. Trades concluded bilaterally can be registered on the exchange 
for clearing. The clearing house provides protection against payment and delivery default. 

For the trade of power the following spot markets are supported by the EEXSPOT: 

 
 
 

Day-ahead auction (CH), gate closure at 11 a.m. CET, 
Day-ahead auction (DE/AT, FR), gate closure at 12 a.m. CET, 
Intra-day auction (DE, AT, FR, CH), continuous 24/7. 

The  Day-ahead  auctions  are  the  historical  core  of  the  short-term  power  market.  They  are 
organized  by  EPEX  SPOT  for  the  German/Austrian,  French  and  Swiss  market  areas.  Trading 
takes  place  one  day  before  the  delivery  of  power.  Organized  as  an  auction,  market  participants 
send  their  orders  until  noon.  EPEX  SPOT  then  closes  the  market  and  aggregates  all  bids  into 
supply and demand curves. Price and volume are determined at the intersection of the curves for 
each hour of the following day. Results are published at 12:55. 

The following characteristics have to be mentioned for the day-ahead-market (see Table Annex1-
6): 

 
 
 
 
 

blind auction procedures, on seven days a week, all year round, 
24 hours of the respective next day can be traded, hour and block contracts, 
integrated into market coupling (depending on the market area), 
trading via the EPEX trading system (ETS), 
establishment  of  the  Phelix  index  (Physical  Electricity  Index)  –  the  reference  price  on 
the European wholesale market. 

Annex1 - 40 

 
 
 
 
Table Annex1-6: Day ahead auctions by EPEXSpot for Germany / Austria. 

3.1.2.2  Analysis of recent market results 

25.  Figure 
EPEXSpot  has  published  the  results  of  its  operation  within  the  Annual  Report  2013  24F
Annex1-41  shows  the  results  of  the  trading  volumes  within  the  market  areas  of  EEXSPOT  in 
Germany, France and Switzerland. 

Figure Annex1-41: Trading volumes of EEXSpot on all three day-ahead markets (in TWh). 

The figure shows that the trade volume increased to 245 TWh in 2013 and to 263 TWh in 2014. 

In Figure Annex1-42 the yearly average base price of the day-ahead markets is shown (in €/MWh). 
In Germany it fell below 40 €/MWh since 2013. 

25 EPEXSpot Annual Report 2013: http://static.epexspot.com/document/29102/EPEX%20SPOT%20Annual%20Report%202013.pdf  

Annex1 - 41 

 
 
 
 
 
 
 
 
 
                                                
 
Figure Annex1-42: Yearly average base price of the Day-Ahead market (in €/MWh). 

Since  we  are  specifically  interested  in  purchases  of  electric  energy  on  the  day-ahead  market,  to 
look for opportunities to take profit from demand flexibility, in the following the analysis will focus on 
the price in the one-year period between December 2013 and November 2014. 

The  following  Figure  Annex1-43  shows  hourly  price  values  in  the  considered  period.  Price 
variability is clear: values range from -65.03 €/MWh to 116.53 €/MWh. Negative prices occurred in 
60 hours. 

Annex1 - 42 

 
 
 
 
 
 
Figure Annex1-43: Hourly price values from December 2013 to November 2014 (source: RSE 
elaboration on EPEX Spot data). 

Figure Annex1-44: Price monthly average (source: RSE elaboration on EPEX Spot data). 

Annex1 - 43 

 
 
 
In Figure Annex1-44 the price monthly average is shown. Just like in Italy, the months from March 
to  August  show  the  lowest  values,  also  due  to  the  high  renewable  generation  (especially 
photovoltaic) that characterizes such months. 

In  Figure  Annex1-45  the  price  monthly  average  in  working  days  and  in  weekends  and  holidays 
(non-working  days)  is  shown.  Since  demand  in  non-working  days  is  lower,  also  electric  energy 
prices are lower. In the considered period, price reduction in non-working days ranged from about 
18% (6.6 €/MWh) to about 41% (13.3 €/MWh). The largest reduction is 14.67 €/MWh in December. 

Figure Annex1-45: Price monthly average in working days and in weekends and holidays (source: 
RSE elaboration on EPEX Spot data). 

In Figure Annex1-46 the average hourly price profile is shown. In the considered period the price 
on average reached the lowest minimum between 3 and 5 a.m. (when demand is low), followed by 
a  first  peak  from  8  to  9  a.m.  (demand  rises  steeply  and  photovoltaic  generation  is  still  low), 
followed by a second minimum from 2 to 3 p.m. (photovoltaic generation is high) and then followed 
by the highest peak from 7 p.m. to 8 p.m. (photovoltaic generation stops and demand is still high). 

In Figure Annex1-47 the price hourly average difference w.r.t. the overall annual average is shown. 
In the considered period, consuming electric energy between 11 p.m. and 7 a.m. and between 1 
p.m. and 4 p.m. would have allowed to pay a price lower than the annual average (saving from less 
than 1 €/MWh to about 12 €/MWh), while in the remaining hours the price paid would have been 
higher than the annual average (range from about +1 €/MWh to about +12 €/MWh). 

Annex1 - 44 

 
 
 
 
Figure Annex1-46: Average hourly price profile (source: RSE elaboration on EPEX Spot data). 

Figure Annex1-47: price hourly average difference w.r.t. the overall annual average (source: RSE 
elaboration on EPEX Spot data). 

Annex1 - 45 

 
 
 
In  terms  of  average  percentage  difference  (Figure  Annex1-25),  in  the  considered  period, 
consuming electric energy between 11 p.m. and 7 a.m. and between 1 p.m. and 4 p.m. would have 
allowed to pay a price lower than the annual average (saving from about 2% to about 36%), while 
in the remaining hours the price paid would have been higher than the annual average (range from 
about +3% to about +36%). 

Figure Annex1-48: price hourly average percentage difference w.r.t. the overall annual average 
(source: RSE elaboration on EPEX Spot data). 

It is also interesting to look at the differences of the average hourly price profiles in working days 
and  in  weekends  and  holidays  (non-working  days):  see  Figure  Annex1-49.  In  the  considered 
period, working days price was always higher, but with a limited difference during the night. 

In  Figure  Annex1-50  the  price  hourly  average  difference  w.r.t.  the  overall  annual  average  of  the 
same type of day in working days and in weekends and holidays (non-working days) is shown. It 
can be seen that in the considered period the differences were lower in non-working days than in 
working ones in the first half of the day, while in the second half they were in most cases higher. 

Figure  Annex1-51  shows  that  in  working  days  the  difference  ranged  about  from  -40%  to  +33%, 
while in non-working days the difference ranged about from -27% to +47%. 

Annex1 - 46 

 
 
 
 
Figure Annex1-49: Average hourly price profiles in working days and in weekends and holidays 
(source: RSE elaboration on EPEX Spot data). 

Figure Annex1-50: price hourly average difference w.r.t. the overall annual average of the same type 
of day in working days and in weekends and holidays (source: RSE elaboration on EPEX Spot data). 

Annex1 - 47 

 
 
 
Figure Annex1-51: price hourly average percentage difference w.r.t. the overall annual average of the 
same type of day in working days and in weekends and holidays (source: RSE elaboration on EPEX 
Spot data). 

In  Figure  Annex1-52,  the  average  hourly  price  profiles  in  December  2013  and  in  January  and 
February  2014  are  shown.  The  profiles  in  the  three  months  are  similar  (except  for  the  more 
pronounced evening peak from 5 p.m. to 6 p.m. in December). Prices during daytime in February 
are lower, probably due to the impact of photovoltaic generation, higher than in December and in 
January. 

The profiles of working days, shown in Figure Annex1-53, are characterized by higher morning and 
evening  peaks,  while  profiles  of  non-working  days  (Figure  Annex1-54)  show  significantly  lower 
prices, especially in February during daytime. 

Annex1 - 48 

 
 
 
Figure Annex1-52: Average hourly price profile in December 2013 and in January and February 2014 
(source: RSE elaboration on EPEX Spot data). 

Figure Annex1-53: Average working days hourly price profile in December 2013 and in January and 
February 2014 (source: RSE elaboration on EPEX Spot data). 

Annex1 - 49 

 
 
 
Figure Annex1-54: Average non-working days hourly price profile in December 2013 and in January 
and February 2014 (source: RSE elaboration on EPEX Spot data). 

In  Figure  Annex1-55,  the average hourly  price  profiles  in March,  April  and May  2014  are  shown. 
The profiles in the three months are similar, except for the very high evening peak in March. 

The  profiles  of  working  days,  shown  in  Figure  Annex1-56,  are  not  so  different  from  the  overall 
ones, while profiles of non-working days (Figure Annex1-57) show a much lower morning peak and 
significantly lower prices in the first hours of the afternoon, with price below 20 €/MWh and near 10 
€/MWh in May. 

Annex1 - 50 

 
 
 
Figure Annex1-55: Average hourly price profile in March, April and May 2014 (source: RSE 
elaboration on EPEX Spot data). 

Figure Annex1-56: Average working days hourly price profile in March, April and May 2014 (source: 
RSE elaboration on EPEX Spot data). 

Annex1 - 51 

 
 
 
Figure Annex1-57: Average non-working days hourly price profile in March, April and May 2014 
(source: RSE elaboration on EPEX Spot data). 

In Figure Annex1-58, the average hourly price profiles in June, July and August 2014 are shown. 
The profiles in the three months are similar and relatively “flat”: prices are almost always between 
20 and 40 €/MWh. 

The  profiles  of  working  days,  shown  in  Figure  Annex1-59,  are  not  so  different  from  the  overall 
ones, while profiles of non-working days (Figure Annex1-60) show a quite low morning peak and 
significantly lower prices in the first hours of the afternoon, with price in August below 10 €/MWh. 
On the contrary, the evening peak reaches higher values. 

Annex1 - 52 

 
 
 
Figure Annex1-58: Average hourly price profile in June, July and August 2014 (source: RSE 
elaboration on EPEX Spot data). 

Figure Annex1-59: Average working days hourly price profile in June, July and August 2014 (source: 
RSE elaboration on EPEX Spot data). 

Annex1 - 53 

 
 
 
Figure Annex1-60: Average non-working days hourly price profile in June, July and August 2014 
(source: RSE elaboration on EPEX Spot data). 

In Figure Annex1-61, the average hourly price profiles in September, October and November 2014 
are shown. In the first half of the day the profiles are similar, while in the second half of the day, 
profiles differentiate for the evening peak, that occurs earlier according to the sunset in the three 
months and is higher in November. 

The  profiles  of  working  days,  shown  in  Figure  Annex1-62,  are  not  so  different  from  the  overall 
ones, while profiles of non-working days (Figure Annex1-63) show a low morning peak (with much 
lower prices in November). The evening peak reaches values about 10 €/MWh lower than working 
days. 

Annex1 - 54 

 
 
 
 
Figure Annex1-61: Average hourly price profile in September, October and November 2014 (source: 
RSE elaboration on EPEX Spot data). 

Figure Annex1-62: Average working days hourly price profile in September, October and November 
2014 (source: RSE elaboration on EPEX Spot data). 

Annex1 - 55 

 
 
 
Figure Annex1-63: Average non-working days hourly price profile in September, October and 
November 2014 (source: RSE elaboration on EPEX Spot data). 

Annex1 - 56 

 
 
 
 
 
3.2  Intra-day spot market 

The  intra-day  spot market,  whose  sessions  take  place  after  the closure of  the  day-ahead market 
session,  has  the  purpose  of  adjusting  the  schedule  output  of  the  day-ahead  market,  for  several 
reasons. 

For  example,  the  day-ahead  market  schedule  could  violate  some  technical  constraints  of 
generation  /  consumption  units  (e.g.  ramp  rates,  start-up  /  shut-down  times,  etc.),  making  it  not 
feasible.  Moreover,  since  the  intra-day  market  sessions  take  place  closer  to  real-time,  market 
players could benefit from new information that has become available after the closure of the day-
ahead  market  session  (e.g.  failures  of  plants,  new  forecasts  of  renewable  generation  or  of 
consumption,  etc.)  and  that  would  make  necessary  to  adjust  the  day-ahead  market  schedule,  in 
order to avoid imbalances. 

For  these  reasons,  the  intra-day  market  typically  deals  with  volumes  much  lower  than  the  day-
ahead ones. 

In the following an analysis of the intra-day spot markets in Italy and in Germany is provided. 

3.2.1  Italy 

The  Italian  intra-day  spot  market  is  called  Mercato  Infragiornaliero  –  MI.  The  Italian  Power 
Exchange,  including  the  intra-day  spot  market,  is  managed  by  GME  -  Gestore  dei  Mercati 
Energetici (www.mercatoelettrico.org).  

3.2.1.1  Market description 

The  intra-day  market  (MI)  allows  participants  to  change  the  schedules  defined  in  the  day-ahead 
market through additional demand bids or supply offers. In this way, a generator can sell additional 
energy or buy-back part of the energy already sold in the day-ahead market, while a load can buy 
additional energy or re-sell part of the energy already bought in the day-ahead market. 

The MI consists of four sessions: MI1, MI2, MI3 and MI4. The sessions are organized in the form of 
implicit  auctions  of  electric  energy,  like  the  day-ahead  one,  with  different  closure  times  and  in 
sequence.  

The session of MI1 takes place after the results of the day-ahead market have been published: it 
opens  at  10:45  of  the  day  before  the  day  of  delivery  and  closes  at  12:30  of  the  same  day.  The 
results  of  MI1  are  notified  to  participants  and  published  by  13:00  of  the  day  before  the  day  of 
delivery. 

The session of MI2, like MI1, opens at 10:45 of the day before the day of delivery, but closes at 
14:40 of the same day. The results of MI2 are notified to participants and published by 15:10 of the 
day before the day of delivery. 

The session of MI3 opens at 16:00 of the day before the day of delivery and closes at 07:30 of the 
day of delivery. Offers / bids refer to the hours from noon to midnight of the day of delivery.  The 
results of MI3 are notified to participants and published by 8:00 of the day of delivery. 

The session of MI4, like  MI3, opens at 16:00 of the day before the day of delivery and closes at 
11:45 of the day of delivery. Offers / bids refer to the hours from 4 p.m. to midnight of the day of 
delivery.  The  results  of  MI4  are  notified  to  participants  and  published  by  12:15  of  the  day  of 
delivery. 

Annex1 - 57 

 
 
 
 
 
 
The sessions of the intra-day market are based on price-setting rules that are consistent with those 
of the day-ahead market, including zonal prices and congestion management. Nevertheless, unlike 
in the day-ahead market, the PUN is not calculated and all purchases and sales are valued at the 
zonal price. 

Nevertheless,  in  the  intra-day,  to  replicate  the  effect  of  the  application  of  the  PUN  to  withdrawal 
points  belonging  to  geographical  zones,  GME  applies  a  non-arbitrage  fee  to  all  accepted  bids  / 
offers pertaining to such points. 

In  particular,  for  each  purchase  transaction  concluded  in  the  MI  and  pertaining  to  a  withdrawal 
point  belonging  to a geographical  zone,  if  the  PUN  set  in the  previous  session  of  the  day-ahead 
market was higher (lower) than the related zonal price, the market participant will pay (receive) a 
non-arbitrage  fee  equal  to  the  difference  between  the  PUN  and  the  zonal  price,  applied  to  each 
MWh of the purchase transaction. 

Vice  versa,  for  each  sale  transaction  concluded  in  the  MI  and  pertaining  to  a  withdrawal  point 
belonging to a geographical zone, if the PUN in the previous session of the day-ahead market was 
lower (higher) than the related zonal price, the market participant will pay (receive) a non-arbitrage 
fee equal to the difference between the zonal price and the PUN, applied to each MWh of the sale 
transaction. 

In the  intra-day  market  it  is  also possible  to submit  the  so-called  “balanced  offers”,  composed  of 
sale offers at a price equal to zero and purchase bids without price indication, submitted even by 
different market participants, but referring to the same hour and to “offer points” belonging to the 
same geographical or virtual zone, and such that the sold quantity is equal to the purchased one. 
“Balanced offers” are typically used by each large generation company to redispatch the amount of 
energy sold in the day-ahead market among its generators located in a specific zone, in order to 
better optimize its portfolio schedule. 

After the  closure  of  each session  of  the  intra-day  market, GME  (as done  after the  closure of  the 
day-ahead market) notifies the Italian TSO TERNA of the results that are relevant for dispatching: 
flows  and  updated  injection  and  withdrawal  schedules.  These  results  are  required  by  TERNA  to 
determine  information  about  residual  transmission  capacities  between  zones  for  subsequent 
market sessions. 

3.2.1.2  Analysis of recent market results 

In Figure Annex1-64 the volumes traded in each month of the last year in the four sessions of the 
intra-day market are shown, while in Figure Annex1-65 the average hourly volumes traded in 2014 
(January÷November)  in  the  day-ahead  market  (MGP)  and  in  the  four  sessions  of  the  intra-day 
market are reported. 

The volumes traded in the intra-day market are about one tenth of the volumes traded in the day-
ahead market. 

Annex1 - 58 

 
 
 
Figure Annex1-64: Volumes traded in each month of the last year in the four sessions of the intra-
day market (source: GME). 

Table Annex1-7: Average hourly volumes traded in 2014 (January÷November) in the day-ahead 
market (MGP) and in the four sessions of the intra-day market (source: GME), 

MGP 

MI1 

MI2 

MI3 

MI4 

32165 MWh 

1449 MWh 

778 MWh 

421 MWh 

696 MWh 

In Figure Annex1-65 the monthly average of the hourly prices in the four sessions of the intra-day 
market is shown. 

It can be noticed that the average prices of MI1 and of MI2 (that refer to all the 24 hours of the day 
of  delivery)  are  quite  similar  to  the  average  prices  of  the  day-ahead  market,  reported  in  Figure 
Annex1-21, while the average prices of MI3 and of MI4 are higher, since they refer to set of hours 
(including the evening peak) characterized by a higher average price also in the day-ahead market. 

As far as the hourly price profiles are concerned, they are quite similar to the day-ahead ones. 

Annex1 - 59 

 
 
 
Figure Annex1-65: Monthly average of the hourly prices in the four sessions of the intra-day market 
(source: GME). 

Annex1 - 60 

 
 
 
 
 
3.2.2  Germany 

3.2.2.1  Market description 

The  intra-day  market  is  the  second  pillar  of  the  short-term  power  market.  Trading  happens 
continuously  until  45  minutes  before  delivery.  Trading  participants  send  orders  which  can  be 
matched  and  executed  at  any  moment.  All  four  EPEX  SPOT’s  intra-day  markets  are  connected 
through cross-border trading.  

The  intra-day  market  for  power  is  available  for  the  German,  Austrian,  French  and  Swiss  market 
areas with the following characteristics: 

(Austria and Switzerland: 75 minutes), 

  continuous trading and pricing (24/7),  
  contracts tradable up to 45 minutes before the beginning of delivery,  
 
  hour and block contracts available for trading, 
 
  cross-border trading between Germany, Austria, France and Switzerland. 

flexible portfolio balancing through 15 minute contracts on the German and Swiss markets, 

The intraday market has the following advantages: 

 
 

 

 

buying or selling of volumes which could not be traded in the auction, 
possibilities of very short-term portfolio optimisation, e.g. in the event of a power plant 
outage or changed weather conditions, 
lower balancing costs as a result of highly developed optimisation options and a more 
efficient utilisation of capacities, 
permits  arbitrage  between  neighbouring  countries  and  creates  opportunities  for  cross-
border trading. 

The characteristics of the Intraday Auctions are summarized in Table Annex1-8. 

Table Annex1-8: Intraday auctions by EPEXSpot for Germany. 

3.2.2.2  Analysis of recent market results 

The market results of the intraday market can be analysed for different periods of the year  on the 
EPEXSpot  website  (http://www.epexspot.com/en/market-data/intradaycontinuous/intraday-table/). 
An example of the days 01-02.12.2014 is shown in Figure Annex1-66.  

Annex1 - 61 

 
 
 
 
 
 
 
 
Figure Annex1-66: Results of the EPEXSpot intra-day power market (01-02.12.2014) 

Annex1 - 62 

 
 
 
 
3.3  Opportunities for the participation of large industrial loads with flexible 

demand to the Power Exchange energy markets in Italy and in Germany 

26  electric  energy  purchases  through  the  Power  Exchange  can  be 
As  above  reported,  short-term25F
carried  out  in  the  day-ahead  spot  market,  that  is  typically  the  most  important  one,  in  terms  of 
volumes of energy traded and of significance of the clearing prices, or in the intra-day spot market, 
where adjustments to the day-ahead market schedule can be done, in order to tackle with changes 
in  the  situation  that  occurred  after  the  closure  of  the  day-ahead  market  session  and  to  avoid 
imbalances. For these reasons, the intra-day market typically deals with volumes much lower than 
the day-ahead one. 

Within  this  context,  a  detailed  analysis  of  hourly  price  profiles  in  the  period  between  December 
2013  and  November  2014  has  been  carried  out  for  the  day-ahead  spot  markets  of  the  Italian 
Power Exchange (IPEX) and of the German one (EPEX Spot). Several charts have been included 
in the report, such as price hourly values, monthly average, hourly average (annual and for each 
month), for all the days of the year and for working-days and non-working days only. 

The charts reported in Figure 2 and in Figure 3, showing the Italian and German day-ahead spot 
market price hourly average difference w.r.t. the overall annual average of the same type of day in 
working days and in weekends and holidays, are the most significant ones. They clearly show how 
cheaper or more expensive can be to consume energy in each hour of each different type of day. 

Price differences w.r.t. the average can be significant, ranging from about -18 ÷ +23 €/MWh in Italy 
and  from  about  -15  ÷  +12  €/MWh  in  Germany,  showing  that  there  is  a  great  opportunity  to  take 
profit from demand flexibility in purchasing energy from the day-ahead spot market. 

As for the intra-day spot market, the Italian one is characterized by price profiles similar to the day-
ahead profiles, while in the German one, that is a continuous auction, price profiles can be different 
from the day-ahead ones. 

26 We focus on short-term (day-ahead / hours-ahead) since it is the typical time horizon over which demand flexibility can more 
effectively be exploited. For this reason, we did not consider longer-term forward and future contracts in the analysis. 

Annex1 - 63 

 
 
 
 
 
                                                
 
4  Contracts with a supplier 

In  the  following,  a  summary  of  the  typical  contractual  arrangements  for  energy  purchases  in  the 
steel industry is reported, with a focus on the degree of flexibility.  

4.1  Italy 

In the Italian steel industry energy is typically bought through a supplier, at an agreed price defined 
according  to  specific  consumption  profiles.  The  price  is  usually  the  day-ahead  spot  market  price 
(PUN). 

In such a case, the foreseen consumption profile has to be communicated to the supplier two days 
before  the  day  of  delivery,  to  allow  the  supplier  itself  to  buy  the  energy  on  the  day-ahead  spot 
market,  one  day  before  the  delivery.  Of  course  this  reduces  the  flexibility  with  respect  to  direct 
purchases on the Power Exchange. 

In case  of  deviations  of consumption from  the  program  agreed with the  supplier  or  deriving from 
Power Exchange purchases, imbalance energy must be settled at specific imbalance prices. In the 
Italian regulation, prices are penalizing or profitable according to the concordance or discordance 
of  the  imbalance  sign  with  the  sign  of  the  overall  imbalance  of  the  zone  where  the  consumer  is 
located: see section 5.1 for details. 

Apart from the cost of energy purchases and of possible imbalances, grid usage charges related to 
peak  power  consumption  are  very  important,  both  in  case  of  contracts  with  a  supplier  and  of 
purchases on the PowerExchange. In Italy, the fee applied to the monthly peak power consumption 
measured in any quarter of an hour of each month is currently equal to 1.528 €/kW-month.  

4.2  Germany 

In  the  German  steel  industry  a  main  part  of  the  yearly  electricity  supply  is  purchased  via  the 
traditional  forward  market.  Many  companies  directly  buy  their  estimated  yearly  electricity  supply 
with  a  risk  surcharge  (for  deviations  from  the  estimated  consumption).  Still,  most  of  the  bigger 
companies  participate  at  the  day-ahead  market  as  well  for  a  part  of  their  more  dynamic  needs. 
Conclusively, a precise estimation of the daily electricity demands is mandatory to avoid imbalance 
fees, as specified in section 5.2. 

The energy delivery contract consists of two parts: 

 
 

the grid usage, to be paid to the responsible TSO; 
the charge for the electricity delivered itself. 

Normally,  the  yearly  paid  fee  for  grid  usage  depends  on  the  electricity  peak  consumption  of  a 
company, resembling a sort of maximum-surveillance. in Germany the monthly fee ranges from 6 
to 9.38 €/kW-month, or an annual fee can be paid, ranging from 35.99 to 60.79 €/kW-year. 

This means that today a flat electricity consumption without big peaks is often desired. This is often 
accompanied by reducing the peak loads in form of intervention within the production cycle. 

Annex1 - 64 

 
 
 
 
 
 
 
 
 
5  Economic regulation of imbalances in Italy and in Germany 

5.1  Italy 

According  to  the  Italian  regulation  [19],  imbalance  fees  in  Italy  are  defined  on  the  basis  of  the 
comparison of the sign of the imbalance of the “dispatching point” (i.e. the difference between the 
actual injection / withdrawal of electric energy and the schedule programmed on the markets of the 
Power  Exchange or through bilateral  contracts) with the  sign  of the  aggregated  imbalance  of the 
“macrozone” including it. 

The considered “macrozones” are two: the North geographical zone and the rest of Italy [ 26F

27]. 

The  aggregated  imbalance  of  a  macrozone  is  the  algebraic  sum,  with  the  sign  changed,  of  the 
volumes  of  electric  energy  traded  by  the  Italian  TSO  TERNA  for  balancing  purposes  in  the 
Dispatching  Services  Market,  both  in  the  programming  phase  and  in  real  time,  in  a  specific 
“relevant time period” and in the specific macrozone. 

The  volumes  corresponding  to  sales  /  purchases  by  TERNA  are  accounted  for  with  a  positive  / 
negative  sign.  The  “relevant  time  period”  for  consumption  units  and  for  generation  units  not 
enabled  to provide  dispatching services is  an  hour,  while for generation  units  enabled  to provide 
dispatching services it is a quarter of an hour [21]. 

As far as “dispatching points” regarding consumption units (as well as generation units not enabled 
to provide dispatching services) are concerned, the prices that are used to value the imbalanced 
energy are the following: 

 

in each hour when the aggregated imbalance of the macrozone where the “dispatching point” is 
located is positive, the imbalance price is the minimum between: 

o 

o 

the average price of purchase bids accepted in the Dispatching Services Market for real 
time  balancing,  weighted  for  the  respective  quantities,  in  that  hour  and  in  that 
macrozone; 
the  zonal  sell  price  of  the day-ahead market for that  hour  and for  the  zone  where the 
“dispatching point” is located; 

 

in each hour when the aggregated imbalance of the macrozone where the “dispatching point” is 
located is negative, the imbalance price is the maximum between: 

o 

o 

the  average  price  of  sale  offers  accepted  in  the  Dispatching  Services  Market  for  real 
time  balancing,  weighted  for  the  respective  quantities,  in  that  hour  and  in  that 
macrozone; 
the  zonal  sell  price  of  the  day-ahead market for that  hour  and for  the  zone  where the 
“dispatching point” is located. 

Since  typically  the  average  price  of  purchase  bids  /  sale  offers  accepted  in  the  Dispatching 
Services Market for real time balancing is lower / higher than the zonal sell price of the day-ahead 
market,  if  the  imbalance  of  the  “dispatching  point”  has  the  same  the  sign  of  the  aggregated 
28,  the  “dispatching  point”  is  penalized,  since  it  has  to  sell  /  buy  the 
imbalance  of  the  macrozone27 F
imbalanced energy at a price lower / higher than the one of the day-ahead market. Conversely, if 

27 AEEGSI Order no. 525/2014/R/eel: “Modifiche e integrazioni alla disciplina degli sbilanciamenti effettivi di energia 
elettrica”. 
28 I.e. the “dispatching point” contributes to the overall imbalance. 

Annex1 - 65 

 
 
 
                                                
 
the imbalance of the “dispatching point” has the opposite sign of the aggregated imbalance of the 
29,  the  “dispatching  point”  makes  a  profit,  since  it  has  to  sell  /  buy  the  imbalanced 
macrozone28F
energy at a price higher / lower than the one of the day-ahead market. 

5.2  Germany 

In  Germany  imbalance  energy  is  settled,  for  each  quarter  of  an  hour,  at  the  price  obtained  by 
dividing  the  control  energy  costs  sustained  by  the  TSO  in  the  balancing  market  in  that  specific 
quarter  of  an  hour,  by  the  balance  of  the  deployed  amount  of  control  energy  in  that  same  time 
interval. 

29 I.e. the “dispatching point” counteract the overall imbalance. 

Annex1 - 66 

 
 
 
 
                                                
 
6  Comparison  of  the  two  options  (Power  Exchange  vs.  contracts  with  a 
supplier)  in  terms  of  possibilities  to  take  profit  from  demand  side 
management 

Purchases in the Day-Ahead Market and in the Intra-Day Market of the Power Exchange are the 
most  flexible  option,  since  they  allow  to  take  full  profit  from  hourly  price  differences  and  allow  to 
change the consumption profile nearer to real-time. 

Thus,  demand  side  management  would  be  more  effective  in  case  of  purchases  directly  on  the 
Power Exchange. 

On the other hand, without forward contracts that fix the price for the bulk of energy consumption, a 
full  exposure  to  the  volatility  of  Power  Exchange  prices  can  be  risky.  Moreover,  the  direct 
participation to the Power Exchange requires an internal structure that only larger companies could 
afford. 

Annex1 - 67 

 
Annex 2 

D 1.2 Description of the grid characteristics (events, time 
response, etc.) and interaction with large consumers 
(Responsible RSE) (Mar 2015) 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Summary 

1 

Current and expected criticalities in the power system ................................................ 2 

2 

3 

4 

5 

6 

1.1 

Italy .......................................................................................................................... 2 

1.2  Germany .................................................................................................................. 7 

Technical regulation of ancillary services ................................................................... 9 

2.1 

Italy .......................................................................................................................... 9 

2.2  Germany ................................................................................................................ 11 

Dispatching services market / balancing market ....................................................... 12 

3.1 

Italy ........................................................................................................................ 12 

3.1.1  Market description .......................................................................................... 12 

3.1.2 

Analysis of recent market results .................................................................... 14 

3.2  Germany ................................................................................................................ 26 

3.2.1  Market description .......................................................................................... 26 

3.2.2 

Analysis of recent market results .................................................................... 31 

Non market-based services, remunerated on the basis of a tariff ................................ 33 

4.1 

Italy ........................................................................................................................ 33 

Other current business opportunities for large industrial loads in providing ancillary services 34 

5.1 

Italy ........................................................................................................................ 34 

5.2  Germany ................................................................................................................ 35 

Synthesis ............................................................................................................ 36 

6.1  Opportunities for large industrial loads in the current regulatory/market/tariff context36 

6.2  Proposals  for  improvements  of  the  current  context  to  make  it  more  favorable  for  large 
industrial loads ....................................................................................................... 36 

Annex2 - 1 

 
 
 
 
 
 
 
1  Current and expected criticalities in the power system 

1.1  Italy 

The  large  development  of  intermitted  renewable  sources,  together  with  the  reduction  of  power 
consumption  due  to  the  global  economic  crisis  have  led  the  Italian  power  system  to  a  general 
condition  of  overcapacity.  As  shown  in  Figure  Annex2-1,  starting from  2009  the  Italian  system  is 
undergoing  a  situation  of  large  overcapacity,  since  the  reserve  margin  is  much  higher  than  the 
typical values considered sufficient, around 10% – 15% of the peak load. 

Figure Annex2-1: Reserve margin at peak load conditions in the Italian power system (in green the 
values typically considered sufficient). 

This was due first to large investments in new combined cycle gas turbines (34 GW of additional 
generation capacity) and more recently by the huge development of renewable sources, especially 
photovoltaic  and  wind  (Figure  Annex2-2),  that,  due  to  incentives,  gradually  replaced  thermal 
generation. The load factor of conventional power plants has then been decreasing dramatically in 
the last years, so that producers can hardly find a suitable coverage for their fixed costs.  

Annex2 - 2 

 
 
 
 
 
 
 
Figure Annex2-2: Development of renewable sources in the Italian power system (hydro excluded). 

The  main  effect  of  intermittent  renewables,  mainly  wind  and  solar  PV,  on  the  electricity  market 
operation is related to the residual load profile that shall be covered by conventional power plants. 
The  increase  of  renewable  generation,  mainly  from  PV  source,  introduces  two  effects:  the 
30 during the central hours of the day and an increase of the ramp slope 
reduction of residual load29F
in the evening.  

Figure Annex2-3 shows the evolution of the residual load resulting from the day ahead market in 
March and November of years 2011, 2012 and 2013. It can be noted that the effect of PV during 
the day increased in the considered years, with a remarkable reduction of residual load. The effect 
is more critical during the bank holidays in summer, when the total load is very low. Figure Annex2-
4 shows the residual load profile on 18th August 2014: the minimum level reached in the hour 14 is 
about 5000 MW, when the total load was more than 20000 MW.  

30 I.e. the load to be supplied by dispatchable conventional generation. 

Annex2 - 3 

 
 
 
  
                                                
 
Figure Annex2-3: Evolution of residual load resulting from the day ahead market in the working days 
of March and November (comparison of years 2011 – 2013). 

Figure Annex2-4: Residual load resulting from the day ahead market on 18th August 2014. 

Annex2 - 4 

 
 
 
This  effect  impacts  not  only  on  the  dispatchability  of  thermal  power  plants  reducing  their  load 
factors,  but  also  on  day  ahead  prices  resulting  from  the  market.  This  further  reduces  the 
profitability  of  conventional  plants  that  suffer  a  low  production  and  prices  too  low  to  cover  their 
costs.  

Figure Annex2-5 shows the ratio between the average hourly day ahead price in 2007 and in 2014 
and its minimum value (reach at hour 4 or 5), in order to get a “normalized” profile. The dramatic 
change of the price profile, especially during the central hours of the day, is quite clear. 

Figure Annex2-5: Change of day ahead average price profiles between 2007 and 2014. 

The  large  development  of  intermittent  renewable  sources  has  an  impact  also  on  the  ancillary 
services  market  and  on  the  real  time  balancing  market.  The  volatility  of  the  RES  requires  an 
increase  of  system  flexibility  during  the  scheduling  phase  and  the  uncertainty  related  to  power 
production forecasts forces the system operator to procure additional spinning reserve margins to 
guarantee  a  safe  operation  in  real  time.  Thus  controllable  power  plants  with  a  high  level  of 
flexibility,  demand  side  management  and  new  devices  such  as  energy  storage  systems  able  to 
quickly react to the changes of residual load are more and more required for future operation of the 
system.  

Another  element  of criticality  is  related  to  the geographical  location  of  renewable sources;  in fact 
large part of RES production is located in the southern part of Italy, while the load is mainly in the 
North.  This  results  in  increasing  energy  flows  from  South  to  North  that  can  be  limited  by 
transmission network constraints, causing congestion. 

Annex2 - 5 

 
  
 
 
As  for  the  future,  the  new  targets  of  the  2030  EU  climate  and  energy  policy  entail  a  further 
development of renewable sources, while demand is expected to slowly grow: this means that the 
above mentioned criticalities are here to stay. 

To face all the criticalities that emerge in the power systems, in the next years several actions are 
planned by  the  policy  makers  and  the regulatory  authority.  Among them the most  relevant  in the 
framework of the integration of renewable sources are: 

  Market reform: 

o  Complete the integration of the Italian market in the Internal Electricity Market with 

the market coupling mechanism. 

o  Carry  out  a revision  of the  ancillary  services markets  in  order  to  allow  all  potential 
suppliers  of  regulating  reserve to  participate to  the  market,  thus  including  demand 
side response, energy storage, downward reserve from RES. 
Introduce a more cost reflective mechanism for the settlement of imbalances. 

o 
o  Harmonize the market rules with the other EU countries for preventing inefficiencies. 

  Capacity market: 

o 

Introduce an efficient and effective capacity market, compliant to the EU guidelines, 
in order to guarantee long term adequacy for the system. 

o  Develop a specific branch of the capacity market with the aim of promoting flexibility 

in power generation.  

  Network expansion: 

o  Foster the network development to avoid congestion and RES curtailment. 
o  Promote a better coordination between the TSO and DSOs for a more coordinated 

development and operation of networks. 

Annex2 - 6 

 
 
 
1.2  Germany 

32]. Renewable energy will 
The power system in Germany is undergoing a period of transition [ 30F
take  on  a  greater  role  in  the  power  supply  as  the  use  of  nuclear  energy  in  Germany  will  end  in 
2022 and the European markets for electricity will continue to grow together. 

31,

31F

The  power  system  must  maintain  a  balance  between  power  generation  and  consumption, 
especially  in  view  of  the  fact  that  the  shares  of  wind  and  solar  energy  in  the  power  supply  mix 
increase. Examples for this development are shown in Figure Annex2-6. 

Figure Annex2-6: Examples of situations with high and low residual load [32]. 

To  achieve  the  balance  the  market  has  to  fulfil  two  tasks:  firstly,  it  must  ensure  that  sufficient 
capacity is available (i.e. reserve function) and secondly, that this capacity is used at the right time 
and  to  the  extent  necessary  (i.e.  dispatch  function).  The  policy  is  therefore  focusing  on  how  to 
develop a future market design and a regulatory framework for the electricity sector that ensures 
that the power supply is secure, cost-efficient and environmentally friendly. 

The main challenges of the German power system may be summarized as follows: 

  Transition of the markets 

31 Federal Ministry for Economic Affairs and Energy (BMWi), Public Relations: Electricity Market for Germany’s Energy 
Transition (Grünbuch). Berlin, October 2014. 
32 CONNECT Energy Economics GmbH: Leitstudie Strommarkt. Berlin, July 2014. 

Annex2 - 7 

 
  
 
 
 
 
                                                
 
The  electricity  market  is  now  liberalised.  The  European  markets  are  largely  coupled  and 
continue  to  grow.  Overcapacities  and  low  CO2  prices  are  driving  down  the  prices  for 
electricity.  The  nuclear  power  will  phase-out  until  2022  and  the  base-load  will  decrease. 
The  expansion  of  renewables  needs  new  flexible  peak  load  technologies  and  a  demand 
side management.  

  Flexibility options 

In order to adjust the balance of the grids, new flexible mechanisms have to be developed. 
A flexible production of conventional and renewable energy as well as a flexible demand of 
consumers  in  industry  and  household  is  necessary.  Furthermore  the  possibilities  to  store 
energy have to be developed. 

  Grid expansion 

Powerful  grids  are  needed  that  couple  the  regions  with  wind,  solar  or  hydropower 
production  to  the  consuming  markets.  The  electricity  is  more  and  more  produced  in  a 
decentralized way and the lay-out of the grids and voltage transformation has to be adapted 
to the future needs. 

  Market price signals 

New  mechanisms  to  adjust  production  and  consumption  of  power  have  to  be  developed. 
Within the spot market a cost-effective synchronization of supply and demand takes place. 
Since 2011 a trade in 15-minute units takes place. Negative price signals are possible if the 
costs for a shut-down of power plants are too high. Additional incentives are necessary to 
stimulate  the  combined  heat  and  power  production  and  a  competitive  production  of 
renewable energies. 

The policies are intensifying the European co-operation and the cross-border trading of electricity, 
too.  This  will  strengthen  the  security  of  power  supply  in  Europe.  Furthermore  the  environmental 
goals  for  climate  protection  and  reduction  of  CO2  will  influence  the  power  market  in  an  essential 
way.  

Annex2 - 8 

 
 
 
 
 
 
2  Technical regulation of ancillary services 

2.1  Italy 

In Italy the regulation that control the dispatching activity and the related ancillary services market 
33] of the national Regulatory Authority. The technical regulation for the 
is the deliberation 111/06 [ 32F
34]  for  transmission,  dispatching, 
operation  of  the  system  is  instead  provided  by  the  Grid  Code  [ 33F
development and security of grid issued by the TSO TERNA. This document provides all technical 
regulations for: 

i) 
ii) 
iii) 
iv) 

v) 

access to the grid; 
development, management and maintenance of the grid; 
performance of dispatching services; 
supply  measurement  and  settlement  services  for  financial  charges  connected  to  the 
aforementioned services; 
security of national electricity system. 

Concerning the provision of ancillary services, in Italy the generating units qualified to participate to 
the ancillary services market must have the following features: 

  being a generating unit with a rated power greater than 10 MVA; 
  not being a non-dispatchable renewable energy source; 
  not being a generating units under test period; 
  being able to increase/reduce at least 10 MW of power generation within 15 minutes from 

 

the start of the service; 
for reservoir hydro power plants: being able to produce at the maximum power for at least 
4 hours. 

The consumption units at the moment are not qualified to provide any form of ancillary service.  

Further technical requirements are defined depending on the specific service provided: 

  Primary  control  reserve  (PCR):  all  the  qualified  generating  units  shall  provide  the  PCR, 
responding  to  frequency  variations  with  variations  of  the  generated  power  within  upward 
and downward bands equal to 1.5% of nominal power; half of the variation of the generated 
power requested to the unit must be supplied within 15 seconds and the full variation within 
30  seconds;  then,  the  new  generation  level  must  be kept for  at  least  15 minutes (in case 
there are no new frequency variations). 

  Secondary control reserve (SCR): to provide SCR generating units shall be equipped with a 
control system able to automatically respond with variations of the generated power to the 
set point signal transmitted by the TSO. Each unit shall provide SCR within bands equal to 
± 15% of the nominal power (for hydro plants) or equal to the maximum between ± 10 MW 
and  ±  6%  of  nominal  power  for  thermal  power  plants.  The  power  corresponding  to  the 
whole band must be supplied within 200 seconds and kept for at least 2 hours. 

33 Authority for Electric Energy, Gas and Hydro Services, Deliberation n° 111/06, 13 June 2006 and successive updates, 
http://www.autorita.energia.it/it/docs/06/111-06.htm. 
34 TERNA, Grid Code, http://www.terna.it/default/home_en/electric_system/grid_code.aspx  

Annex2 - 9 

 
 
 
                                                
 
  Tertiary control reserve (TCR): all the qualified generating units that are able to supply the 
power  variation  requested  by  the  TSO  within  15  minutes  with  a  ramp  rate  of  at  least  50 
MW/min can provide the “ready reserve” TCR service. All the generating units that are able 
to  supply  the  power  variation  requested  by  the  TSO  within  2  hours  without  any  time 
limitation are qualified for the “substitution reserve” provision. 

Further technical details and limitations for the provision of ancillary services can be found in the 
grid code of the Italian TSO TERNA [34].  

Annex2 - 10 

 
  
 
 
2.2  Germany 

The legal and regulatory framework for providing and transmitting electric power and sustaining a 
balanced  load-frequency  in  Germany  is  complex  and  interwoven  by  different  national  and 
international  regulations.  On  European  level  the  European  Network  of  Transmission  System 
Operators for Electricity (ENTSO-E) elaborated a common valid grid code which will come in force. 
Until  now  a  collection  of  principles  for  system  operation  for  the  European  TSO’s  is  comprised 
within an Operation Handbook [ 34F

35]. 

The  German  national  legal  framework  is  mainly  devoted  to  reserve  and  balancing  energy  topics 
36]  and  the  German  Renewable  Energy 
and  is  included  in  the  Energy  Industry  Act  (EnWG)  [ 35F
37].  Additional  regulatory  decrees  can  be  introduced  and  changed  by  the 
Sources  Act  (EEG)  [ 36F
German Network Agency which also provides different market rules for control reserve segments. 
The German Network Agency (Bundesnetzagentur) acts as an additional regulator by determining 
market rules and access conditions for each control-reserve quality after consulting the TSOs and 
bidders. For the allocation of control energy actual data concerning the control tariffs can be found 
and downloaded on a specific website [ 37F

38]. 

Further requirements focusing on technical issues are included in the latest version of the German 
TSOs’ grid code which has been set to work in 2007 [38F

39]. 

In this document the German TSOs define the rules for the access to the national grid, while also 
considering and satisfying the boundary conditions of the European electricity transmission grid.  

Main topics are: 

- 

- 

- 

- 

- 

Connection  conditions,  technical  requirements  and  approaches  for  the  connection  and 
parallel  operation  of  generating  plants  within  the  high  voltage  network,  grid  frequency 
stability (PCR/SCR/TCR), active/reactive power generation, grid protection in case of faults, 
accounting. 

Grid usage (transmission losses, bottlenecks, costs of EEG). 

TSO  system  services  in  addition  to  transmission  of  energy,  regarding  quality  of  supply 
(frequency and grid stability, grid restoration after failures, transmission management). 

Network development describing possible means for capacity and architecture of new grids 
to  ensure  low  risk  of  failures  and  a  high  guarantee  of  supply  (e.g.  implementation  of  grid 
redundancies). 

Operational  planning  and  system  management  to  define  coordinative  and  organization 
tasks  of  TSOs  (definition  of  daily  load  schedules,  maintenance  schedule  of  energy 
generation  plants,  technical  organization  of  grid  management  for  maintaining  frequency 
stability, count and charging of transmitted and feed electricity). 

This  framework  ensures  an  open  and  transparent  access  to  the  grid  as  a  prerequisite for  a free 
market. Furthermore, it targets to guarantee grid stability even in times of highly fluctuating feed-in 

35 ENTSO-E: “UCTE Operation Handbook (OH)”, v2.5 draft, 24.06.2004 
36 Federal Ministry of Justice: „Energiewirtschaftsgesetz - EnWG”, 07.07.05 
37 Federal Ministry of Justice: „Renewable Energy Sources Act – EEG 2014”, 21.07.14 
38 www.regelleistung.net/ip/action/abrufwert 
39  Verband  der  Netzbetreiber  (VDN)  e.V.  beim  VDEW:  “TransmissionCode  2007  –  Network  and  System  Rules  of  the 
German Transmission System Operators”, August 2007. 

Annex2 - 11 

 
 
                                                
 
of  electricity.  The  TSOs  have  thereby  “developed  a  common  understanding  concerning  the 
implementation of their responsibility for the system” [ 39F

40]. 

3  Dispatching services market / balancing market 

3.1  Italy 

The  Ancillary  Services  Market  (Mercato  per  il  Servizio  di  Dispacciamento  -  MSD)  is  the  market 
where TERNA  -  as Transmission  System  Operator  -  procures  the  resources  needed  to  manage, 
operate, monitor and control the power system (relief of intra-zonal congestions, creation of power 
reserve, real-time balancing). In the MSD, TERNA enters into purchase and sale contracts with a 
view  to  obtaining  resources  for  dispatching  services  and  acts  as  central  counterpart  to  the 
transactions. 

Bids/offers must refer to offer points (i.e. generation units) qualified to provide ancillary services in 
the MSD and must be submitted by the respective dispatching users directly (without agents acting 
on  their  behalf).  All  accepted  bids/offers  are  remunerated  at  the  offered  price  (pay-as-bid 
methodology).  

The  MSD  consists  of  a  scheduling  stage  (ex-ante  MSD)  and  a  real  time  stage,  the  Balancing 
Market (MB).  

In  the  ex-ante  MSD,  TERNA  accepts  energy  demand  bids  and  supply  offers  in  order  to  relieve 
41  congestions  and  create  reserve  margins.  In  particular,  the  ex-ante  MSD 
residual  intra-zonal40F
consists of four scheduling sub-stages: MSD1, MSD2, MSD3 and MSD4. In this phase the Italian 
TSO  accepts  tertiary  reserve  bids  and  offers  to  define  an  optimal  scheduling,  relieving  network 
congestions  and  creating  suitable  secondary  and  tertiary  reserve  margins  for  the  real  time 
operation. 

The  Balancing  Market  (MB)  takes  place  in  different  sessions,  during  which  TERNA  selects 
bids/offers for the groups of hours of the same day on which the related MB session takes place. 
At  present,  the  MB  consists  of  5 sessions.  In  the  MB,  TERNA  accepts  energy  demand bids and 
supply  offers  in  order  to  provide  secondary  regulation  and  to  balance  energy  injections  and 
withdrawals into/from the grid in real time. 

3.1.1  Market description 

Unlike  other  markets  in  Europe,  the  main  ancillary  services  in  Italy  are  traded  on  the  MSD  spot 
market (that takes place in different stages) and are remunerated only on the basis of the energy, 
without any remuneration for the capacity made available. In the following a brief description of the 
dispatching services trading is provided41F

42. 

40  Verband  der  Netzbetreiber  (VDN)  e.V.  beim  VDEW:  “TransmissionCode  2007  –  Network  and  System  Rules  of  the 
German Transmission System Operators”, August 2007, p.3 
41 Inter-zonal congestion have already been managed by the previous sessions of the day ahead and of the intra-day 
markets:  see  deliverable  D1.1  “Description  of  the  general  and  local  electricity  markets  and  tariffs  in  Italy  and  in 
Germany”. 
42 Primary control reserve (PCR) is not offered in a competitive market: the supplied regulating energy is remunerated 
on the basis of a tariff defined by the Italian Regulatory Authority, provided that the unit is equipped with an adequate 
metering system. 

Annex2 - 12 

 
 
 
 
                                                
 
Secondary control reserve: SCR is automatically activated through the regulation signal sent by the 
TSO  to  each  unit.  The  amount  of  energy  is  selected  by  a  pro-rata  criterion  and  remunerated 
according to the price offered by the producer in the MSD (pay-as-bid mechanism). 

Tertiary control reserve: TCR is activated by the TSO by means of specific dispatching orders sent 
to the generating  units. The  amount  of  energy  is  selected  on the  basis of  a common  merit  order 
and  remunerated  according  to  the  price  offered  by  the  producer  in  the  MSD  (pay-as-bid 
mechanism). 

Table  Annex2-1  summarizes  the  main  characteristics  of  the  products  traded  in  the  ancillary 
services market for balancing purpose. 

Table Annex2-1: Main characteristics of the products traded in the ancillary services market for 
balancing purpose. 

Service 

Product 

Time slice 

SCR 

TCR 

2  products  (upward  and  downward) 
for each hour 
2  products  (upward  and  downward) 
for  each  hour.  Bids  between 
minimum  and  maximum  power  can 
be  divided  into  up  to  4  steps  with 
different prices. 
1 minimum power bid (increase from 
zero  to  minimum  power)  for  each 
hour 
from 
1  shut-down  bid  (reduction 
minimum  power  to  zero)  for  each 
hour 
1  start-up  bid  for  each  hour  (only 
CCGT and OCGT)  
1  change  of  configuration 
CCGT with two gas turbines) 

(only 

Selection of 
bid 
Energy price 
merit order 

Remuneration 

Energy 
pay as bid 

1 hour 

1 hour 

Energy price 
merit order 

Energy 
pay as bid 

1 hour 

1 hour 

- 

- 

Energy price 
merit order 

Energy 
pay as bid 

Energy price 
merit order 

Energy 
pay as bid 

Merit order 

Merit order 

Token 
compensation 
Token 
compensation 

According to the central-dispatching design of the Italian market, energy imbalances are computed 
and  settled  for  each  generating  and  consumer  unit,  with  different  prices  depending  on  the  unit 
classification. The imbalance settlement for an aggregate portfolio is not allowed. 

For  the  qualified  generating  units,  energy  imbalances  are  priced  according  to  a  dual  price 
mechanism, depending on the sign of the control area imbalance. On the other hand, imbalances 
of consumers and of all the generating units not qualified for providing ancillary services are priced 
with  a  single  price  mechanism,  depending  on  the  sign  of  the  control  area  imbalance.  Table 
Annex2-2  and  Table  Annex2-3  shows  respectively  the  dual  price  mechanism  applied  to qualified 
units  and  the  single  price  mechanism  for  the  non-qualified  units.  The  energy  imbalances  are 
settled  with  reference  to  different  time  periods:  for  qualified  units  the  relevant  time  frame  is  15 
minutes, for non-qualified units it is 1 hour. 

The imbalance prices, both for qualified and non-qualified units, are computed on zonal basis for 
two macro-areas: North (equivalent to the Northern zone of the day ahead market) and South (that 
includes all the other market zones). 

For  non-dispatchable  renewable  energy  sources  the  current  regulation  fixes  an  imbalance 
threshold for each energy sources (wind, PV, RoR hydro), within which the cost of imbalances is 

Annex2 - 13 

 
 
 
 
 
aggregated and equally divided among all generating units of a specific source. The net imbalance 
of a unit exceeding the threshold is then priced according to the non-qualified unit regulation. 

Table Annex2-2: Dual price mechanism for imbalances of qualified units. 

Sign of 
control 
area 
imbalance 

Sign of 
unit 
imbalance 

+ 

+ 

- 

- 

+ 

- 

+ 

- 

Price for qualified unit 

Minimum 

between: 

-  day ahead price  

-  min price of downward bids 

accepted 

Day ahead price 

Day ahead price 

-  day ahead price  

-  max price of upward bids accepted 

Maximum 

between: 

Table Annex2-3: Single pricing mechanism for imbalances of non-qualified units. 

Sign of 
control 
area 
imbalance 

+ 

- 

Price for non-qualified unit 

Minimum between: 

-  day ahead price  

-  average price of downward bids accepted 

Maximum 

between: 

-  day ahead price  

-  average price of upward bids accepted 

3.1.2  Analysis of recent market results 

Figure Annex2-7 shows the trend between years 2008 and 2013 of the annual volume of ancillary 
services  provided  in  Italy,  detailed  for  type  of  service  (SCR/TCR)  and  type  of  regulation 
(upward/downward). Such volumes include both the scheduling phase (ex-ante MSD) and the real 
time operation (MB). 

During  the  considered  period  volumes  of  control  reserves  significantly  changed,  because  of 
several  reasons:  improvements  in  scheduling  and  operation  of  the  system  and  several  market 
reforms  have  contributed  to  enhance  the  real  time  control  and  balancing  of  the  system,  leading 
also to a better economic performance.  

Annex2 - 14 

 
 
  
 
 
 
Figure  Annex2-8  shows  the  monthly  amount  of  balancing  energy  provided  only  in  real  time 
43 
operation  for  each  type  of  services  in  the  year  between  December  2013  and  November  2014 42F
and Figure Annex2-9 shows the corresponding prices. It can be noted that in real time the energy 
used for downward services is almost twice than the upward energy. This is a consequence of the 
large  development  of  intermittent  RES  that  introduce  more  uncertainties  in  the  balance  of  the 
system; in order to face uncertainties the TSO in the scheduling phase procures additional reserve 
that is then balanced by downward energy in real time, if not used.  

Figure Annex2-7: Annual volume of balancing reserve provided in years 2008-2013. 

43 The same period considered for the analysis of the energy markets reported in deliverable D1.1. 

Annex2 - 15 

 
 
 
 
                                                
 
Figure Annex2-8: Monthly volume of balancing energy provided for each type of services. 

Figure Annex2-9: Monthly weighted average of balancing energy prices. 

Annex2 - 16 

 
 
 
 
Typical prices of upward reserves are higher than the day ahead energy price, while the downward 
reserve  price  is  lower.  This  means  that  operating  on  the  balancing  market  can  be  much  more 
remunerative than operating only on the day ahead market. 

Prices of control reserves reflect also the value of the two distinct services: SCR and TCR. Since 
SCR  is  a  faster  service  and  not  all  the  market  participants  are  able  to  provide  it,  it  is  the  most 
valuable  product,  with  higher  prices  for  increasing  generation  and  lower  prices  for  reducing 
generation43F

44. 

Figure Annex2-10 and Figure Annex2-11 respectively show the hourly balancing energy provided 
and the corresponding prices. It can be noted that the volumes of balancing energy depend also on 
the residual load profiles and in particular on the direction of the ramp.  

Figure Annex2-10: Hourly average of balancing energy provided for each type of service. 

44 Currently in Italy negative prices are not allowed. The price for downward service is the price at which the producer 
buys back its own energy: this results in a net expenditure for the producer.  

Annex2 - 17 

 
 
 
                                                
 
Figure Annex2-11: Hourly weighted average of balancing energy prices. 

In  order  to  better  understand  the  market  framework  where  the  Italian  industrial  partners  (ORI 
Martin and AST) involved in the project can potentially participate, in the following a more detailed 
analysis of the historical results of the Balancing Market in the two market zones where their steel 
plants  are  located  is  presented.  The  two  market  zones  are  NORTH  and  CENTER-NORTH 
(CNORTH)  and  the  analysis  is  focused  on  the  TCR  used  for  real  time  balancing  in  the  period 
December 2013 – November 2014. 

Figure Annex2-12 reports the hourly average amount of TCR provided in the NORTH market zone 
for  upward  and  downward  regulation.  Even  in  this  case  the  hourly  profile  of  services  provision 
depends  on  the  residual  load  profile  and  follows  the  general  trend  already  shown  for  the  whole 
Italian market (Figure Annex2-10).  

Figure  Annex2-13  shows  the  same  volumes  provided  in  the  CNORTH  market  zone;  in  this  case 
the  average  volumes  are  significantly  lower  than  in  the  NORTH  zone.  This  is  due  to  the  lack  of 
resources that can provide TCR. 

Annex2 - 18 

 
 
 
 
Figure Annex2-12: Hourly average balancing energy for TCR provided in the NORTH zone. 

Figure Annex2-13: Hourly average balancing energy for TCR provided in the CNORTH zone. 

Figure  Annex2-14  and  Figure  Annex2-15  show  the  hourly  average  price  for  TCR  in  both  market 
zones. It can be noted that prices for TCR in the NORTH zone are quite constant along the day, 
while  in  the  CNORTH  zone  there  is  a  price  spike  for  upward  service  in  correspondence  to  the 
Annex2 - 19 

 
 
 
 
evening load ramp. This is coherent with the reduced volumes traded and indicates a shortage of 
TCR for upward service in that market zone. 

Figure Annex2-14: Hourly average price of TCR in the NORTH zone. 

Annex2 - 20 

 
 
 
 
Figure Annex2-15: Hourly average price of TCR in the CNORTH zone. 

Figure  Annex2-16  and  Figure  Annex2-17  show  the  monthly  average  of  daily  values  of  TCR 
procured respectively in the NORTH and in the CNORTH zone, while Figure Annex2-18 shows the 
corresponding  prices.  The price  spike that  occur  in April  in the  CNORTH  zone  can  be  explained 
with the lack of TCR occurred in that month (see Figure Annex2-17). 

Figure Annex2-16: Monthly average of daily balancing energy for TCR provided in the NORTH zone. 

Annex2 - 21 

 
 
 
Figure Annex2-17: Monthly average of daily balancing energy for TCR provided in the CNORTH zone. 

Figure Annex2-18: Monthly weighted average of TCR prices in the NORTH and CNORTH zones. 

Annex2 - 22 

 
 
 
 
Another interesting aspect is the difference between TCR procured during working days and non-
working days. Figure Annex2-19 through Figure Annex2-22 clearly show that the average volumes 
procured  during  working  days  are  significantly  higher  than  in  non-working  days,  in  both  market 
zones  and  both  for  upward  and  downward  regulation.  This  is  mainly  due  to  the  typical  load 
reduction during weekend.  

On  the  contrary,  while  in  the  day  ahead  market  the  energy  price  in  non-working  days  is  lower 
because of a reduction of traded volumes, in the ancillary services market the prices of TCR are 
almost  constant  in  working  and  non-working  days,  as  shown  in  Figure  Annex2-23  and  Figure 
Annex2-24.  Indeed  there  is  a  slight  increase  of  the  upward  price,  especially  in  the  CNORTH 
market zone, proof of the scarce availability of regulating resources.  

Figure Annex2-19: Monthly average of daily downward balancing energy for TCR provided in the 
NORTH zone in working and non-working days. 

Annex2 - 23 

 
 
 
 
Figure Annex2-20: Monthly average of daily upward balancing energy for TCR provided in the NORTH 
zone in working and non-working days. 

Figure Annex2-21: Monthly average of daily downward balancing energy for TCR provided in the 
CNORTH zone in working and non-working days. 

Annex2 - 24 

 
 
 
 
Figure Annex2-22: Monthly average of daily upward balancing energy for TCR provided in the 
CNORTH zone in working and non-working days. 

Figure Annex2-23: Monthly weighted average of TCR prices in the NORTH zone for upward and 
downward regulation in working and non-working days. 

Annex2 - 25 

 
 
 
 
Figure Annex2-24 - Monthly weighted average of TCR prices in the CNORTH zone for upward and 
downward services in working and non-working days. 

3.2  Germany 

In  Germany  larger  electricity  customers  cover  their  energy  demands  via  the  traditional  energy 
market.  Therefore,  contracts  with  a  power  company  are  concluded.  These  contracts  mostly 
comprise a fixed base price combined with a demand rate. When exceeding a threshold, defined 
as a maximum averaged quarter of an hour achievement, the customer is financially fined. 

Additionally, there is the possibility for larger companies to cover a part of their electricity demand 
via  the  European  Electricity  Exchange  (EEX).  To  stabilize  the  electricity  grid  larger  electricity 
customers  can  participate  solitarily  or  as  a  part  of  a  pool  of  customers  at  the  electricity  control 
market, the German Control Reserve Market. 

3.2.1  Market description 

This  control  market  assists  the  so  called  Netzregelverbund  (NRV)  as  a  supplementary  tool  to 
guarantee the grid-stability in the context of flexible electricity generation and demand. The NRV is 
an  incorporation  of  all  German  and  several  international  transmission  grid  operators  (TSOs)  for 
electricity. In this network all control zones are optimized with respect to economic advantages and 
grid-stability,  by  exchanging  required  information.  In  case  of  imbalances,  control  energy  is 
activated. Major impacts on the actual grid situation are the stochastic load behavior, regenerative 
electricity supply, failures of conventional power stations as well as the electricity wholesale trade.  

There  are  three  types  of  control  power  which  can  be  differentiated  by  their  activation  speed  and 
principle of activation. Primary control reserve (PCR), secondary control reserve (SCR), as well as 

Annex2 - 26 

 
 
 
 
 
 
tertiary control reserve (TCR). They can be defined as positive (shutdown of aggregates) as well 
as negative (cut-in of aggregates). 

The PCR is quantified by ENTSO-E which directly cooperates with the NRV within the International 
Grid  Control  Cooperation  (IGCC).  It  stabilizes  the  grid  as  fast  as  possible  and  will  not  be 
considered further in this analysis. 

The SCR is an automatically activated control reserve and only activated where imbalances occur, 
slower  than  the  PCR.  SCR  minimizes  deviations  of  network  frequency  as  well  as  unscheduled 
energy flows. SCR has a limited duration, therefore for longer lasting imbalances in the system it 
must be replaced by the slower TCR. The requirements for the TCR are lower than for the other 
two. 

The overall situation for German industrial consumers is depicted in Figure Annex2-25. 

Figure Annex2-25: General overview of the German electricity market for industrial consumers. 

Since 1st December 2006, the minute reserve (TCR) required by the four TSOs has been procured 
45]. A joint 
via a joint cooperation. For this purpose, a TSOs' common Internet platform is available [ 44 F
tender for the procurement of primary and secondary control reserve was introduced one year later 
on 1st December 2007 and is also processed via this internet-platform. 

Control reserve and control energy have to be procured within a common (cross-control area) and 
anonymized  and  strictly  regulated  tendering  process.  Supplementary  information  on  IGCC  is 
provided there as well by detailing energy exchanges between the various IGCC partners. A study 
commissioned by the German TSOs dealing in more detail with “The description of load-frequency 

45 www.regelleistung.net 

Annex2 - 27 

 
 
 
 
                                                
 
control concept and market for control reserves” enables a sufficiently deep and complete insight 
into this topic [ 45F

46].  

The main characteristics of control reserve products in Germany are listed in Table Annex2-4. 

46  Consentec:  “The  description  of 
commissioned 
content/uploads/2014/08/Consentec_50Hertz_Regelleistungsmarkt_en__20140227.pdf  

load-frequency  control  concept  and  market  for  control  reserves”,  study 
http://www.consentec.de/wp-

27.02.14, 

German 

TSO’s, 

the 

by 

Annex2 - 28 

 
 
 
                                                
 
Table Annex2-4: Main characteristics of control reserve products in Germany [46] 

In general, bidders are chosen on the basis of the merit order (based on costs for procured control 
reserve)  of  capacity  prices.  Energy  prices  bids  are  solely  considered  where  bids  have  identical 
capacity prices. All chosen bidders are paid according to their individual capacity-price bid (pay-as-
bid). For a more detailed insight into the control power market and the control power concepts see 
47], where the legal and regulatory framework, the network access model, the realization of power-
[46F
frequency control, pre-qualification of suppliers and provision and use of control reserve as well as 
the  determination  and  settlement  of  control  energy  is  analyzed.  PCR  and  SCR  are  procured  in 
weekly tenders, whereas TCR is provided in daily tenders. 

For  SCR  and  TCR  it  is  essential  that  two  different  biddings  for  provided  reserves  as  well  as  an 
energy  price  bid  for  deployed  reserves  and  capacities  which  become  valid  in  case  of  a  possible 
activation have to be made. 

A uniform balancing energy price, the so called “reBAP”, has been introduced as a single uniform 
price  for  the  whole  of  Germany,  changing  every  15-minute  interval  [46].  The  imbalance  price  is 
symmetric,  i.e.  it  is  used  both  for  offtake  of  balancing  energy  out  of  the  system  by  a  player 
having  a  shortage  of  energy  (positive  balancing  energy:  e.g.  a  customer  with  a  higher 
consumption than the scheduled amount) and for feed-in of balancing energy into the system 
by  a  player  having  a  surplus  of  energy  (negative  balancing  energy:  e.g.  a  customer  with  a 
lower consumption than the scheduled amount). 

The reBap is determined by dividing the energy costs arising in each specific quarter of an hour by 
the balance of the deployed amount of control energy within the same time interval. Since control 
energy costs can be either positive or negative, the reBAP can also be positive or negative. A 
positive reBAP means that the player pays the TSO for the balancing energy offtaken from the 

47 www.regelleistung.net 

Annex2 - 29 

 
 
 
 
                                                
 
system (i.e. in case of shortage), and that it is paid by the TSO for the balancing energy fed 
into the system (i.e. in case of surplus). If the reBAP is negative, the payment flows are in the 
opposite  directions.  Table  Annex2-5  shows  the  payment  flows  for  balancing  energy  in  the 
different possible cases. 

Table Annex2-5: Payment flows for balancing energy in the different possible cases. 

Type of imbalance 

shortage 

shortage 

surplus 
surplus 

Flow of balancing 
energy 
offtake from the 
system 
offtake from the 
system 
feed-in to the system 
feed-in to the system 

Sign of reBAP 

Effect for the player 

positive 

negative 

positive 
negative 

pays 

gets paid 

gets paid 
pays 

As far as loads are concerned, Table Annex2-6 shows the main characteristics of SCR (Seconds 
Reserve Load  –  SRL),  TCR  (Minutes Reserve  Load  –  MRL),  as  well  as  of  the  service  regarding 
interruptible loads (AbLaV) [ 47F

48]. 

Table Annex2-6: Main characteristics of control reserve products in Germany for loads [ 48F

49] 

48 https://www.transnetbw.com/en/energy-market/ancillary-services/interruptible-loads  
49 FOREnergy: Flexibilisierung der Energienachfrage von industrieellen Verbrauchern, Jan. 2015 

Annex2 - 30 

 
 
 
 
 
 
 
 
                                                
 
3.2.2  Analysis of recent market results 

The necessary provision of control reserve is calculated every quarter of an hour and is based on 
historical data and estimations concerning relevant factors. It can be seen from Figure Annex2-26 
that the tendered volume has undergone a significant change within the years. 

Figure Annex2-26: Quarterly averaged values of control reserve tendered (data base 
www.regelleistung.net) [46]. 

This  is  mainly  based  on  changing  behavior  of  the  energy  consumers  as  well  as  on  structural 
changes of load-frequency control in Germany as e.g. the introduction of the NRV. 

A  nice  historical  overview  condensing  the  overall  development  of  tendered  SCR  and  TCR  is 
depicted in Figure Annex2-27 for SCR and Figure 7 for TCR.  

Annex2 - 31 

 
 
 
Figure Annex2-27: Evolution of averaged capacity price of accepted bids for SCR (data base 
www.regelleistung.net) [46]. 

Figure Annex2-28: Development of averaged capacity price of accepted bids for TCR (data base 
www.regelleistung.net) [46]. 

These prices result from the overall provided control reserve. 
It is not feasible to apply these figures to single use cases directly, since the capacity price strongly 
varies  according  to  offered  boundary  conditions  of  the  industrial  plant.  These  requirements 
comprise: 
Time response for providing balancing power (positive/negative) 
Amount of balancing power offered in total 
Possible duration of activation time 
Allowable amount of possible activations (per week/month/year) 
Robustness during possible activation (rejection rate, acceptance rate) 

Annex2 - 32 

 
 
 
4  Non market-based services, remunerated on the basis of a tariff  

4.1  Italy 

In Italy the primary regulation service (PCR) for frequency regulation is a mandatory service to be 
provided  by  all  generators  with  rated  power  greater  than  10  MVA  and  compliant  to  specific 
requirements defined in the grid code. The primary regulation service consists in the provision of 
an upward and downward regulation capacity band, equal to 1.5% of the rated power, subject to 
the  automatic  control  during  real  time  operation,  according  to  the  frequency  measured  at  the 
connection point of the plant with the grid.  

In the  past  the  primary  control  energy  was  settled  as a deviation from  the  scheduled  profile,  like 
imbalances: this was penalizing for generating units that provided the primary control service. For 
this  reason  from  2014  a  mechanism  for  the  remuneration  of  this  service  has  been  introduced, 
avoiding the accounting of primary control energy as imbalances. The mechanism is optional and 
generating units that want to be remunerated must install a specific metering system. By means of 
such systems the TSO TERNA can measure the amount of primary control energy provided by the 
unit  every  15  minutes.  This  amount  is  subtracted  to  the  net  power  imbalances  and  remunerated 
with a price defined as:  

 

 

for  upward  regulation:  hourly  day  ahead  market  zonal  price  plus  50%  of  the  difference 
between the annual average price of bids accepted for  upward secondary regulation and 
the annual average zonal price of the day ahead market (both average values refer to the 
previous year); 
for downward regulation: hourly day ahead market zonal price minus 50% of the difference 
between the annual average zonal price of the day ahead market and the annual average 
price  of  bids  accepted  for  downward  secondary  regulation  (both  average  values  refer  to 
the previous year). 

Annex2 - 33 

 
 
 
 
5  Other current business opportunities for large industrial loads in 

providing ancillary services 

5.1  Italy 

Large  industrial  loads  in  Italy  can  participate  to  the  provision  of  a  service  for  switching  off  their 
consumption,  if  required  by  the  TSO  for  security  reasons.  According  to  the  grid  code,  the 
interruptible loads are classified in: 

Instantaneous switch-off (within 200 ms after the control signal) 

 
  Emergency switch-off (within 5 s after the control signal) 

50]  that 
The  current  regulation  in  force  is  order  no.  301/14  of  the  Italian  regulatory  Authority  [ 49F
regulates  the  period  2015-2017.  The  amount  of  interruptible  loads  to  be  supplied  in  the  3-year-
period  is  3300  MW,  that  the  TSO  TERNA  shall  buy  by  means  of  a  Dutch  auction  mechanism, 
starting from a price of 105000 €/MW/year for Instantaneous switch-off and 60000 €/MW/year for 
Emergency switch-off.  

The standard products traded in this auction include: 

  3-year-period  product  (¾  of  the  total  amount  to  be  provided  should  be  covered  by  this 

product) 

  Annual product, that accounts for the residual capacity after the 3-year-period allocation  
  Monthly  product,  that  accounts  for  the  residual  capacity  after  the  previous  long  term 

allocations 

In addition  to the  capacity  compensation,  during  the  annual  operation,  interruptible loads receive 
an additional compensation of 3000 €/MW for each interruption ordered by the TSO. 

The requirements for a consumer for participating to the auction include the minimum size of the 
load equal to 1 MW, the capability to switch-off the consumption according to the required timing, 
the presence in the site of an automatic device for processing the control signal issued by the TSO. 

The whole amount required for the period 2015-2017 has been provided through the Instantaneous 
switch-off  service  with  3-year-period  product,  with  a  marginal  price  of  88899  €/MW/year.  Almost 
300 consumers participated to the auction for the period 2015-2017 and several of them belong to 
the steel industry. 

50  Italian  regulatory  Authority  for  Electric  Energy,  Gas  and  Hydro  Services:  “Disciplina  delle  procedure  per 
l’approvvigionamento a termine delle risorse elettriche interrompibili e proroga semestrale dei contratti vigenti”, order 
n° 301/14, June 19th 2014.  

Annex2 - 34 

 
 
 
 
 
                                                
 
5.2  Germany 

Larger customers  and pools  of  smaller  customers  can  participate  at  the electricity  control  market 
by making an application to their transmission system operator with respect to the governmentally 
introduced decree for switching-off loads [48]. 

The  decree  for  switching-off  loads  [50F51]  within  industrial  branches  possessing  high  electricity 
demands  is  a  governmental  decree  introduced  to  stabilize  the  electric  transmission  grids  and 
thereby enhancing the security of supply. It will  be valid until the end of 2015 with a potential for 
prolongation. The participation can be comparably performed via the official homepage responsible 
for collecting the bids for balancing energy [51F52]. 

A main drawback of the main energy-intensive processes within the steel industry (e.g. hot rolling, 
cold  rolling,  sinter  blower,  hot-dip  galvanizing)  is  the  discontinuous  process  behaviour  and  strict 
boundary  conditions.  Under  these  restrictions  it  is  yet  nearly  impossible  for  the  German  steel 
producing industry to participate in this field. 

51 German federal ministry of justice: “Verordnung über Vereinbarung zu abschaltbaren Lasten (AbLaV)”, 28.12.12 
52 https://www.regelleistung.net/ip/action/static/ausschreibungAbLa 

Annex2 - 35 

 
 
 
 
                                                
 
6  Synthesis 

6.1  Opportunities 

for 

large 

industrial 

loads 

in 

the 

current 

regulatory/market/tariff context 

In Italy, according to the current Grid Code by the TSO TERNA, loads of any size are not enabled 
to provide ancillary services. Nevertheless, the Italian Regulatory Authority intends to remove soon 
this  limitation,  as  stated  in  recent  consultation  documents,  in  line  with  the  provisions  of  the 
European directive 2012/27/EU on energy efficiency. Considering the current prices of the Italian 
ancillary services market, this opportunity could be particularly profitable. 

The  only  opportunity  today  available  for  large  industrial  loads  is  the  provision  of  the  interruptible 
load  service,  by  means  of  multi-year  contracts  that  remunerate  the  capacity  made  available  for 
switching  off  the  consumption.  Such  contracts  are  established  with  the  TSO  through  an  auction 
and  are  bound  to  large  industrial  loads  that  can  guarantee  at  least  1  MW  of  switch-off  capacity. 
Currently, among the industrial loads that participate at the auction there are also consumers from 
the steel industry. 

On  the  contrary  in  Germany  the  ancillary  services  market  is  not  precluded  to  any  entity  of  the 
power  system,  thus  loads  and  consumers  can  participate  to  the  provision  of  regulating  reserve, 
even in a pooled way. Because of the strict requirements for the provision of PCR and SCR, TCR 
seems  to  be  the  most  attractive  service  that  can  be  provided  by  large  industrial  loads.  At  the 
moment 1 MW balancing capacity is a rough number at which it gets interesting to participate at 
the balancing market or at least for companies offering demand response services.  

Besides  the  ancillary  services  market,  as  well  as  in  Italy,  large  consumers  can  participate  at  the 
auctions  for  the  provision  of  interruptible  load.  However,  as  above  mentioned,  the  discontinuous 
process  behaviour  and  strict  boundary  conditions  of  the  steel  production  process  make  nearly 
impossible for the German steel producing industry to participate in this field. 

6.2  Proposals  for  improvements  of  the  current  context  to  make  it  more 

favorable for large industrial loads 

The main improvement that should be put in place in the Italian context is the removal of the barrier 
for  consumers  to  participate  to  the  ancillary  services  market,  since  many  industrial  loads  and 
aggregations of consumers are today ready to contribute to the balancing market. 

For  example,  all  the  industrial  loads  enabled  to  provide  interruptible  load  services  are  equipped 
with automatic devices for a fast switch-off and this makes them also compatible with a balancing 
reserve service both for the scheduling phase and in real time. 

Allowing  loads  and  consumers  to  participate  to  the  ancillary  services  market  would  increase  the 
balancing energy available to the system, increasing also the competition in the market. Since the 
Italian approach for the system operation is based on central dispatch and thus each point of the 
network  is  responsible  for  its  own  injection/withdrawal  and  cannot  benefit  from  a  portfolio 
aggregation, the most suitable service that can be provided by loads is the tertiary reserve, while 
the PCR and SCR are faster, automatic and more flexible services provided by generating units.  

In the context of demand side response, the new interaction between TSO and DSOs should be 
regulated,  since  DSOs  will  be  able  in  the  next  future  to  provide  a  contribution  to  the  balancing 
service thanks to the controllable loads located in their network.  

Annex2 - 36 

 
 
 
 
With the participation of large industrial loads to the ancillary services market, the service provided 
by interruptible loads can be reduced or even removed, since the flexibility provided by loads would 
be properly remunerated by the market. 

Finally, in order to allow market participants to express the value of the flexibility offered, negative 
prices should be introduced, as already happens in many EU countries. 

The  Italian  framework  for  system  dispatching  has  been  designed  when  the  generation  mix  was 
mainly based on controllable conventional power plants and the power consumption was inelastic. 
Today the general conditions have changed, following the large development of new technologies 
such  as  intermittent  renewable  sources,  distributed  generation,  demand  side  management  and 
storage systems. For this reason the Italian Regulatory Authority has launched a process with the 
objective of reforming the electricity market in line with this new context, in order to exploit every 
potential resource able to provide control reserve and other ancillary services to support a secure 
and effective system operation. 

On  the  other  hand,  the  German  market  framework  seems  more  open  and  favorable  for  large 
industrial loads, since they can participate to the provision of control reserve.  

The  balancing  mechanism  based  on  the  balance  responsible  parties  makes  more  flexible  the 
participation of loads to the ancillary services market. Further improvements in this context could 
be a more effective dissemination of information about the potential participation of industrial loads 
to the ancillary services market, making clear: 

  What are the main contents of a separate electricity contract concerning balancing energy? 
  Will there be extra grid usage fees? 
  How is it affecting the off-balance financing of the energy supplier? 
  How will the usage of extra electricity be integrated (valid activation) ? 

Finally,  in  order  to  allow  steel  industries  to  participate  to  the  interruptible  load  service,  tradable 
products  with  less  binding  constraints  could  be  introduced,  to  account  for  the  discontinuous 
process behaviour that characterizes them. 

More in general, energy supply contracts should change putting flexibility more into the focus, due 
to  the  not  constant  energy  consumption  of  the  production  process  and  taking  into  account  the 
opportunity to exploit the flexibility to provide ancillary services to the network. 

Annex2 - 37 

 
Annex 3 

Statistical Imbalance Data Analysis  
(Responsible SSSA) 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Summary 

1 

2 

3 

4 

5 

An example of the dataset build with data from TERNA ............................................... 2 

Markov chain results in imbalance sign analysis .......................................................... 2 

Classification performance through the Balanced Classification Rate .............................. 4 

Performance functions ............................................................................................. 5 

Multi-step-ahead prediction results on selling and purchase price ................................. 5 

Annex3 - 1 

 
 
 
 
 
In  this  section  some  detail  arguments  and  results,  concerning  with  statistical  data 
analysis lead from SSSA in Task 2.3, are listed. 

1  An example of the dataset build with data from TERNA 

A  dataset  has  been  created  for  each  macro  zone  (NORTH  and  SOUTH)  with  data 
collected  from  TERNA  and  through  data  pre-processing  and  aggregation.  The  macro 
zone dataset has been represented by a matrix, where each row corresponds to one 
hour of a day, in the reference period, while columns correspond to the different types 
of collected information. 

An example of the dataset organization is shown in Figure Annex3-1. 

Figure Annex3-1: Example of dataset matrix for a macro zone 

The  dataset  has  been  divided  into  two  parts:  input  data  and  target  data.  The  target 
variables correspond to the variables to be predicted: 

  Hourly sign of imbalance of the National Transmission Network 
  Hourly average selling price at the Dispatching Services Market 
  Hourly average purchase price at the Dispatching Services Market 

2  Markov chain results in imbalance sign analysis 

In the context of network imbalance sign analysis, the network imbalance sign is the 
target variable and its possible values are ‘+1’ for positive imbalance and ‘-1’ for 
negative imbalance. Markov chain can be used to calculate the probability to remain in 
the same state or to move into the other one at the time t+K, knowing only the state 
at the time t. 

Considering K=10, we have obtained the results shown Table Annex3-1 and Table 
Annex3-2. In particular, Table Annex3-1 (a) and Table Annex3-2 (a) show the results 
obtained by considering a positive starting state, while Table Annex3-1 (b) and Table 

Annex3 - 2 

 
 
Annex3-2 (b) represent the probability related to a negative starting state in the 
NORTH and SOUTH macro zone. 

+ 

K=1 
K=2 
K=3 
K=4 
K=5 
K=6 
K=7 
K=8 
K=9 
K=10 

State 
Positive 

State 
Negative 

0.13 
0.22 
0.28 
0.32 
0.35 
0.36 
0.38 
0.38 
0.39 
0.39 

0.87 
0.78 
0.72 
0.68 
0.65 
0.64 
0.62 
0.62 
0.61 
0.61 

(a) 

- 

K=1 
K=2 
K=3 
K=4 
K=5 
K=6 
K=7 
K=8 
K=9 
K=10 

State 
Positive 

State 
Negative 

0.80 
0.67 
0.58 
0.52 
0.48 
0.46 
0.44 
0.43 
0.42 
0.41 

0.20 
0.33 
0.42 
0.48 
0.52 
0.54 
0.56 
0.57 
0.58 
0.59 

(b) 

Table Annex3-1: Probabilities related to the positive (a) and negative state (b) in the 
NORTH macro zone 

Table Annex3-1, related to the NORTH macro zone, demonstrate that, if the 
imbalance sign at the time t is positive, it is more probable that the sign remains 
positive rather than to move to the negative state until the time t + 10, while, if  the 
sign of the imbalance at the time t is negative, it is more probable to remain negative 
until the instant  t + 4, after which it is more probable to have a change of state.  

+ 

K=1 
K=2 
K=3 
K=4 
K=5 
K=6 
K=7 
K=8 
K=9 
K=10 

State 
Positive 

State 
Negative 

0.20 
0.32 
0.40 
0.46 
0.50 
0.52 
0.53 
0.54 
0.55 
0.56 

0.80 
0.68 
0.60 
0.54 
0.50 
0.48 
0.73 
0.46 
0.45 
0.44 

(a) 

- 

K=1 
K=2 
K=3 
K=4 
K=5 
K=6 
K=7 
K=8 
K=9 
K=10 

State 
Positive 

State 
Negative 

0.85 
0.75 
0.69 
0.65 
0.62 
0.60 
0.59 
0.58 
0.57 
0.57 

0.15 
0.25 
0.31 
0.35 
0.38 
0.40 
0.41 
0.42 
0.43 
0.43 

(b) 

Table Annex3-2: Probabilities related to the positive (a) and negative (b) state in the 
SOUTH macro zone 

Table Annex3-2, related to the SOUTH macro zone, demonstrates that, if the 
imbalance sign at the time t is positive, it is more probable that the sign remains 
positive rather than to move to the negative state until the time t + 4,  while, if  the 
sign of the imbalance at the time t is negative, it is more probable to remain negative 

Annex3 - 3 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
until the instant t + 10. It is interesting to note the opposite behaviour of NORTH and 
SOUTH. 

3  Classification performance through the Balanced Classification Rate 

The performance of a classifier has been computed by means of a performance 
indicator, the Balanced Classification Rate (BCR), also known as Area Under Curve 
(AUC)52F

53. 

The BCR is defined by the following equation: 

𝐵𝐶𝑅 =  

1

2

[

𝑇𝑃

𝑇𝑃+𝐹𝑁

+

𝑇𝑁

𝑇𝑁+𝐹𝑃

]  

where  the  ratios  TP/(TP+FN)  and  TN/(TN+FP)  are  also  called  as  sensitivity  and 
specificity, respectively.  

The  performance  of  the  classifier  is  expressed  in  terms  of  four  counts,  well-
represented in the confusion matrix, shown in Table Annex3-3.  

Classified as positive 

Classified as negative 

Actual positive 

Actual negative 

TP 

FP 

FN 

TN 

Table Annex3-3: Confusion matrix for binary classification 

The meaning of the four counts in the confusion matrix is the following: 

  True Positive (TP): percentage of correctly classified positive samples. 
  False Negative (FN): percentage of positive samples incorrectly classified as 

belonging to the negative class; 

  False Positive (FP): percentage of negative samples incorrectly classified as 

belonging to the positive class; 

  True Negative (TN): percentage of negative samples correctly classified. 

The BCR is an appropriate measure for imbalanced data53F
the balance between the two classes. For these reasons, in this work the BCR has 
been used to evaluate the classifier performance. 

54, since it takes into account 

53 Sokolova, M. and Lapalme, G. 2009. A systematic analysis of performance measures for classification 
tasks. Inf. Process. Manage. 45, 4 (Jul. 2009), 427-437 
54 Vannucci, M. Colla, V. Novel classification method for sensitive problems and uneven datasets based on 
neural networks and fuzzy logic. Applied Soft computing Journal, 11(2), 2011, pp. 2383-2390. 

Annex3 - 4 

 
 
 
 
 
 
 
                                                
 
4  Performance functions 

For a variable vector X, made of n scalar observations, the Root Mean Square Error is 
defined as follows: 

𝑅𝑀𝑆𝐸 =   √

𝑛
𝑖=1

∑ (𝑋𝑖 −   𝑋𝑖̂ )2
𝑛

where    𝑋𝑖  is  the  measured  value  at  time  i,  𝑋𝑖̂   is  the  value  predicted  by  the  model  at 
time i and n is the number of observations. 

The Normalized Square Root Mean Square Error is defined as follows: 

𝑁𝑆𝑅𝑀𝑆𝐸 =  

𝑅𝑀𝑆𝐸
𝜎

where σ is the standard deviation of X.  

The  Mean  Absolute  Error  is  defined  as  the  average  difference,  in  absolute  value, 
between  the  predicted  and  measured  sample.  It  is  not  relevant  in  this  analysis, 
because it is the amount of physical error in a measurement.   

The Mean Relative Error is defined as the average ratio between the absolute error 
and the  measured value. It is more interesting than the absolute error because it 
gives an indication of how good a measurement is relative to the measured value.  

5  Multi-step-ahead prediction results on selling and purchase price 

SSSA experimented the multi-step-ahead prediction algorithm, described in Task 2.3, 
to make predictions on the selling and purchase prices. 

The multi-step-ahead prediction algorithm has been applied by using the different 
statistical techniques NARX and NAR neural networks, respectively to implement the 
model with and without exogenous inputs. Initially, the results achieved with NAR are 
explained. 

Graphic visualizations with NAR 

A graphic visualization of measured and predicted price values has been produced by 
the algorithm for each of the next ten hours, for both selling and purchase prices and 
for each macro zone. The measured values are represented in blue color, while the 
predicted ones in red color. 

Another form of visualization of measured and predicted price values is the one where 
the  x-coordinate  represents  measured  values,  while  the  y-coordinate  represents 
predicted values. Each blue point represents a couple made of a measured value and 
the corresponding predicted value. Therefore, the closer to the bisector line (in green 
color) a point is, the better the prediction accuracy is.  

Only  graphics  related  to  the  first  and  the  second  hour  and  to  the  North  macro  zone 
are  shown  in  the  following  Figure  Annex3-2,  Figure  Annex3-3,  Figure  Annex3-4  and 
Figure Annex3-5. 

Annex3 - 5 

 
 
 
The  figures  show  that,  for  both  selling  and  purchase  prices,  the  predictions  of  the 
price  are  quite  good  and  satisfactory  for  the  first  hour  and  start  to  decrease  for  the 
second  hour.  Furthermore,  starting  from  the  third  hour,  the  results  are  less 
meaningful  and  the  predicted  values  tend,  always  more,  towards  the  mean  price 
value.  

The plots generated for South macro zone, not here represented, show similar trends. 

Annex3 - 6 

 
Figure Annex3-2: North Selling Price – Measured values (blue) and Predicted (red) for the first and second hour 

Figure Annex3-3: North Selling Price - Measured values towards predicted values for the first and second hour 

Annex3 - 7 

 
  
 
 
 
 
Figure Annex3-4:  North Purchase Price – Measured values (blue) and Predicted (red) for the first and second hour 

 Figure Annex3-5: North Purchase Price - Measured values towards predicted values for the first and second hour 

Annex3 - 8 

 
 
 
Multi-step-ahead prediction performance results with NAR  

NAR performance results for prediction of the selling price for the next hours are 
shown in Table Annex3-4, for North and South macro zone. 

Hour  NSRMSE 
    0.6731 
1 
    0.8389 
2 
    0.9254 
3 
    0.9716 
4 
    1.0022 
5 
    1.0083 
6 
    1.0088 
7 
    1.0072 
8 
    1.0054 
9 
    1.0048 
10 

NORTH 
Relative Error 
    0.1535 
    0.2022 
    0.2239 
    0.2378 
    0.2450 
    0.2485 
    0.2520 
    0.2551 
    0.2574 
    0.2585 

Absolute Error  NSRMSE 
    0.6680 
    0.8441 
    0.9365 
    0.9774 
    0.9954 
    1.0044 
    1.0047 
    1.0053 
    1.0058 
    1.0056 

    0.0303 
    0.0400 
    0.0444 
    0.0472 
    0.0486 
    0.0492 
    0.0496 
    0.0499 
    0.0501 
    0.0502 

SOUTH 
Relative Error 
    0.1902 
    0.2651 
    0.3038 
    0.3218 
    0.3301 
    0.3324 
    0.3337 
    0.3342 
    0.3342 
    0.3343 

Absolute Error 
    0.0348 
    0.0471 
    0.0533 
    0.0561 
    0.0574 
    0.0578 
    0.0579 
    0.0580 
    0.0580 
    0.0579 

Table Annex3-4: Selling Price – Multi step ahead prediction results with NAR 

NAR performance results for prediction of the purchase price for the next hours 
are shown in Table Annex3-5, for North and South macro zone. 

Hour  NSRMSE 
    0.7767 
1 
    0.9202 
2 
    0.9685 
3 
    0.9908 
4 
    1.0074 
5 
    1.0215 
6 
    1.0325 
7 
    1.0458 
8 
    1.0550 
9 
    1.0608 
10 

NORTH 
Relative Error 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

Absolute Error  NSRMSE 
    0.7388 
    0.8927 
    0.9952 
    1.0621 
    1.1002 
    1.1279 
    1.1446 
    1.1516 
    1.1571 
    1.1585 

    0.0828 
    0.1029 
    0.1099 
    0.1161 
    0.1188 
    0.1206 
    0.1224 
    0.1239 
    0.1250 
    0.1260 

SOUTH 
Relative Error 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

Absolute Error 
    0.0726 
    0.0931 
    0.1050 
    0.1131 
    0.1171 
    0.1200 
    0.1217 
    0.1227 
    0.1233 
    0.1233 

Table Annex3-5: Purchase Price - Multi-step ahead prediction results with NAR 

Concerning with the purchase price, the relative error is unavailable because zero 
is a possible value of the price.  

The NSRMSE is the most interesting index to evaluate the performance of the 
prediction, because it takes into account the standard deviation of the measured 
values. When the value of the NSRMSE is more than unitary value, it means that 
the deviation of the prediction error is greater than the standard deviation of the 
target value itself, therefore the accuracy of the model would be better if the 
mean value was output. Moreover, in some cases, while the relative error could 
be considered acceptable, the NSRMSE is greater than one, to confirm that the 
predicted value tends towards the mean value. 

Multi-step-ahead prediction performance results with NARX  

Annex3 - 9 

 
 
 
 
To make predictions on prices, SSSA has applied the multi-ahead prediction 
algorithm also by using NARX, with only wind prevision time-series data as 
exogenous input. Since only wind prevision data for future hours are priori 
known, only those data have been taken into account as external inputs. 

NARX performance results for prediction of the selling price for the next ten 
hours are shown in Table Annex3-6, for North and South macro zone. 

Hour  NSRMSE 
0.6432 
1 
0.8028 
2 
0.8710 
3 
0.9110 
4 
0.9452 
5 
0.9701 
6 
0.9846 
7 
0.9930 
8 
0.9972 
9 
0.9996 
10 

NORTH 
Relative Error 
0.1711 
0.2301 
0.2531 
0.2634 
0.2689 
0.2715 
0.2734 
0.2749 
0.2758 
0.2758 

Absolute Error  NSRMSE 
0.6640 
0.8479 
0.9338 
0.9626 
0.9725 
0.9871 
1.0032 
1.0215 
1.0376 
1.0521 

0.0316 
0.0422 
0.0465 
0.0485 
0.0499 
0.0506 
0.0511 
0.0515 
0.0516 
0.0517 

SOUTH 
Relative Error 
0.2089 
0.2943 
0.3348 
0.3481 
0.3569 
0.3706 
0.3905 
0.4112 
0.4289 
0.4422 

Absolute Error 
0.0365 
0.0503 
0.0566 
0.0586 
0.0598 
0.0616 
0.0639 
0.0663 
0.0685 
0.0701 

Table Annex3-6: Selling Price – Multi step ahead prediction results with NARX 

NARX performance results for prediction of the purchase price for the ten next 
hours are shown in Table Annex3-7, for North and South macro zone. 

Hour  NSRMSE 
0.7622 
1 
0.9406 
2 
1.0251 
3 
1.0894 
4 
1.1307 
5 
1.1599 
6 
1.1738 
7 
1.1763 
8 
1.1732 
9 
1.1657 
10 

NORTH 
Relative Error 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

Absolute Error  NSRMSE 
0.7085 
0.8484 
0.9313 
0.9753 
0.9911 
1.0018 
1.0074 
1.0071 
1.0104 
1.0127 

0.0831 
0.1084 
0.1196 
0.1291 
0.1346 
0.1384 
0.1405 
0.1410 
0.1406 
0.1397 

SOUTH 
Relative Error 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

Absolute Error 
0.0699 
0.0899 
0.1002 
0.1067 
0.1091 
0.1108 
0.1112 
0.1111 
0.1116 
0.1117 

Table Annex3-7: Purchase Price – Multi step ahead prediction results with NARX 

Comparing NARX multi step ahead prediction results with those reached with 
NAR, they turned out to be comparable, meaning that most of informative 
contents is already included inside the historical target. 

Annex3 - 10 

 
 
 
 
Annex 4 

 Mathematical formulation of Production Re-scheduling model  
(Responsible SSSA) 

 
 
 
 
 
 
 
 
 
 
There  is  a  certain  time 𝑡𝑚,𝑚′  needed  to transport  products  from  machine  m  to  machine  m’.  In 
order  to  avoid  a  too  large  decrease  of  the  temperature  of  the  molten  steel,  the  waiting  time 
𝑚𝑎𝑥. Specific 
spent by a heat p after stage st is restricted by a maximum allowed delay time 𝑡𝑝,𝑠𝑡
rules  about  the  sequences  of  heats  are  applied  in  the  continuous-casting  stage.  Heats  are 
divided  in  groups  and  all  the  heats  of  the  same  group  need  to  be  assigned  to  the  same 
continuous  cast  and  are  casted  subsequently  without  waiting times.  For  all  stages,  except  for 
𝑠𝑒𝑡𝑢𝑝 has  to  be  performed  in  advance  in  order  to  carry  out  the 
CC,  a  machine  specific  setup  𝑡𝑚
processing of the next heat. On the other hand, for the continuous casting, a setup is needed 
only for the first heat of the sequence or group of heats. The processing duration 𝜃𝑝,𝑚 depends 
both on the product  p  and on  the machine  m  where it is processed. Among input  parameters 
there is the power consumption ℎ𝑝,𝑚, which is specific of products and machines. 

To model the assignment of the heat p to the machine m, a binary variable 𝑋𝑚,𝑝 is used. This 
variable must equal one if p is assigned to m and zero otherwise. Starting and finishing times 
𝑓 . They represent starting and 
of the processes are stored in the variables 𝑡𝑚,𝑝
finishing times of product p, when processed on machine m and of product p at stage st 
𝑓 = 0 if p is not processed on m). A binary variable, necessary for this 
respectively (𝑡𝑚,𝑝
formulation, represents precedence relations between two heats p,p’ on a given stage. The 
variable 𝑉𝑠𝑡,𝑝,𝑝′ equals one if p is processed before p’ at stage st and equals zero otherwise. 
Heats might not be processed immediately from stage to stage, and thus there might be 
waiting times. These waiting times are contained in the variables 𝑤𝑠𝑡,𝑝. 

𝑠 = 𝑡𝑚,𝑝

𝑓 , 𝑡𝑝,𝑠𝑡

, 𝑡𝑝,𝑠𝑡

, 𝑡𝑚,𝑝

𝑠

𝑠

The following equality and inequalities serve to model the constraints of the problem and 
mathematically define the variables. To start, observe that exactly one machine must process a 
heat in a given stage st, thus 

where M(st) is the set of machines at stage st. The finishing time of a process equals the 
starting time, plus the process duration 𝜃𝑝,𝑚. The starting (finishing) time of heat p in a given 
stage is the starting (finishing) time of p on the machine where it is processed. Hence the 
following equations define starting and finishing times 

Where the inequalities are made to force 𝑡𝑚,𝑝
enough number). 
The next equations define the waiting time between stages as the difference between the 
starting time of stage st + 1 and the finishing time of stage st. 

𝑓 = 0 if p is not processed on m (B is a large 

𝑠 = 𝑡𝑚,𝑝

Waiting times between stages have restrictions: an heat should wait at least the time needed 
to transfer it from the machine of one stage to the machine of the next stage; on the other 
hand, waiting times are upper bounded, since heats must not cool off below a certain level. 

The precedence relations are defined by the following equations, stating that if p is processed 
before p’ on the same machine, then 𝑡𝑚,𝑝′

𝑓  plus the setup time of machine m 

𝑠 > 𝑡𝑚,𝑝

Annex4 - 2 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
At the CC stage there are groups of heats are processed continuously. Therefore the machine 
setup time is necessary only between the last heat of a group and the first heat of the next 
group. 

Constraints (11) force the heats of the same group to be assigned to the same caster 

Heats are ordered in the groups according to their casting sequence. The following constraints 
ensure that heats in the same groups are continuously cast 

The following equations serve for the precedence relation between two heats to be well 
defined: either p is processed before p’ or p’ is processed before p 

The sequence of heats processed on the CC stage, must be the same at all stages, this is 
enforced by the following equations 

Moreover, in the scheduling model task start times are represented by continuous variables.  

Task  start  times  variables  are  related  to  the  energy  part  of  the  model,  leading  to  the 
computation of the total electricity consumption within a given time interval.  

These variables are used to find the contribution of a task to the electricity consumption within 
a given time interval by considering four possible cases: 

  Case 1: a task is processed entirely within the time slot. 
  Case 2: a task starts before and finishes within the time slot. 
  Case 3: a task starts within and finishes after the time slot. 
  Case 4: a task over-spans the full time slot. 

Annex4 - 3 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure Annex4 

Figure Annex4 1: Task contribution to electricity consumption of a time slot 

Figure  Annex4 1  describes the four possible cases considered to  compute a task consumption 
of energy in a time slot: in the light blue boxes the process duration within the time slot s-th is 
indicated, by putting into relation the starting or final time of a heat  p on machine m with the 
lower,  𝜏𝑠−1 ,  and  upper  bound,  𝜏𝑠 ,  of  the  time  slot.  For  example,  when  the  task  is  entirely 
processed within the time slot, as in case 1, the complete process duration of the heat on the 
machine 𝜃𝑝,𝑚  is accounted for. 

The overall electricity consumption 𝑞𝑠  within the s-th time interval can be computed by 
summing the contributions of all tasks in the interval. The total electricity consumption 𝑞𝑠 is 
needed for the evaluation and optimization of KPI. 

In order to be able to define the consumptions in the four cases a number of technical 
constraints are formulated. 

𝑠

𝑓
, 𝑌𝑝,𝑠𝑡,𝑠

𝑠

First the variables 𝑌𝑝,𝑠𝑡,𝑠
time slot s, are defined. The variable 𝑌𝑝,𝑠𝑡,𝑠
if and only 𝜏𝑠−1 ≤ 𝑡𝑝,𝑠𝑡
used: the auxiliary variables 𝐴𝑝,𝑠𝑡,𝑠
auxiliary variables Ω𝑝,𝑠𝑡,𝑠
𝑓
𝑠
, 𝑌𝑝,𝑠𝑡,𝑠
𝑌𝑝,𝑠𝑡,𝑠
variables 𝐴𝑝,𝑠𝑡,𝑠

  and A𝑝,𝑠𝑡,𝑠

𝑓
 (Ω𝑝,𝑠𝑡,𝑠

𝑓
 (𝐴𝑝,𝑠𝑡,𝑠

𝑠

, modelling whether a heat p on stage st starts (or finishes) during 
𝑓 = 1 

= 1 if and only 𝜏𝑠−1 ≤ 𝑡𝑝,𝑠𝑡
𝑓 ≤ 𝜏𝑠. To model these logical conjunctions, auxiliary binary variables are 
) are true if and only if 𝜏𝑠−1 ≤ 𝑡𝑝,𝑠𝑡

𝑠 ≤ 𝜏𝑠 and, similarly, 𝑌𝑝,𝑠𝑡,𝑠

𝑓 ); the 

 (𝜏𝑠−1 ≤ 𝑡𝑝,𝑠𝑡

𝑠

𝑠

 must then be true if and only if both 𝐴𝑝,𝑠𝑡,𝑠
𝑠

𝑓

𝑠

 are defined by the following equations: 

) are true if and only if 𝑡𝑝,𝑠𝑡

𝑠 ≤ 𝜏𝑠 (𝑡𝑝,𝑠𝑡
𝑠

  and Ω𝑝,𝑠𝑡,𝑠

𝑓 ≤ 𝜏𝑠). The variables 
𝑓
  and Ω𝑝,𝑠𝑡,𝑠
 (𝐴𝑝,𝑠𝑡,𝑠

𝑓

) are true. The 

Recall that B is a large constant. The variables Ω𝑝,𝑠𝑡,𝑠
equations 

𝑠

  and Ω𝑝,𝑠𝑡,𝑠

𝑓

 are defined by the following 

Annex4 - 4 

 
 
 
 
  
 
With the previous equations, the variables 𝑌𝑝,𝑠𝑡,𝑠

𝑠

 can be defined. 

The variables 𝑌𝑝,𝑠𝑡,𝑠

𝑓

 are defined similarly 

In order to model on which machine m the product p is processed at stage st during slot s, 
other variables are introduced: 𝑦𝑝,𝑚,𝑠𝑡,𝑠
𝑠
𝑌𝑝,𝑠𝑡,𝑠

𝑓𝑎𝑢𝑥 ) : they are true if and only if both 𝑋𝑚,𝑝 and 

 )are true. The inequalities used to define them are the following: 

𝑠𝑎𝑢𝑥 (𝑦𝑝,𝑚,𝑠𝑡,𝑠

𝑓
,(𝑌𝑝,𝑠𝑡,𝑠

Similarly 

Four variables are used to capture the energy consumption in the four cases described in 
Figure 30. For the first case a binary variable 𝑎𝑝,𝑚,𝑠𝑡,𝑠 is used. It is true if and only if p is 
processed on m at stage st starting and finishing within time slot s. This definition is enforced 
as follows 

Real variables are used for the second case. The value 𝑏𝑝,𝑚,𝑠𝑡,𝑠 is the amount of time taken in 
time slot s at stage st by processing p on the machine m. In other words 𝑏𝑝,𝑚,𝑠𝑡,𝑠 = 𝑡𝑚,𝑝
and only if it starts before time slot s and finishes within time slot s, 𝑏𝑝,𝑚,𝑠𝑡,𝑠 = 0 otherwise. 

𝑓 −   𝜏𝑠−1 if 

Annex4 - 5 

 
 
 
 
 
 
 
 
 
 
 
  
 
Symmetrically, real variables are used for the third case. The value 𝑐𝑝,𝑚,𝑠𝑡,𝑠 is the amount of 
time taken in time slot s at stage st by processing p on the machine m. In other words 
𝑐 =   𝜏𝑠 − 𝑡𝑚,𝑝
0 otherwise. 

 if and only if it starts within time slot s and finishes after time slot s, 𝑐𝑝,𝑚,𝑠𝑡,𝑠 =

𝑠

Finally in the fourth case the variable 𝑑𝑝,𝑚,𝑠𝑡,𝑠  equals 𝜏𝑠 − 𝜏𝑠−1  (namely the entire time slot), if 
the task of processing p on machine m is at stage st, starts before time slot s and finishes after 
time slot s, 𝑑𝑝,𝑚,𝑠𝑡,𝑠 = 0   otherwise. This is enforced by the following equations 

The specific electricity consumptionℎ𝑝,𝑚 of process p on machine m is known for every process 
and for every machine. This consumption is assumed to be constant over time, introducing an 
approximation that creates an acceptable error. 
Using the variables a, b, c, d is possible to calculate the electricity consumption 𝑞𝑠 of each time 
slot s as follows  

where the sum is divided by the number of minutes in a the time slots, which in our case is 60. 

Having defined the electricity consumption 𝑞𝑠 it is possible to compute the objective function, 
which is a combination of the function 𝑀4 defined before and a function that penalizes the 
scheduling delay, namely the sum of all starting times. Therefore the objective function f is as 
follows 

Annex4 - 6 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Where |SL| is the number of times slot and 

The parameter 𝜙 is the fee for exceeding the maximum peak as described in the previous 
section. Moreover k is a parameter that tunes the tradeoff between the total scheduling time 
and the quality of the solution in terms of the KPI. 

This expression of the objective function f is nonlinear as it contains modules and a maximum 
calculation. In order to obtain a linear expression of the objective function, further equations 
are needed. To linearize modules |𝑞̂𝑠 − 𝑞𝑠|, real variables 𝜇𝑠 are introduced, so that  𝜇𝑠 = |𝑞̂𝑠 − 𝑞𝑠|.  
Therefore 

The max over the cases function can be linearized by introducing the variable M representing 
the max of the electricity consumption over all the time slots. 

If this maximum is bigger than the maximum load ML, then the real variable Γ = 𝑀 ∗ 𝜙, 
otherwise, Γ = 0. This is enforced by the following equations 

where 𝛿 is an auxiliary binary variable (and the previous equation forces 𝛿 = 1, if M > ML). The 
objective function becomes thus 

In order to account for the possibility of machines of the same type having different 
consumption ℎ𝑝,𝑚 another term is added to the objective function, namely the total 
consumption. Therefore the final objective function is 

The number of variables and constraints in the formulation is sensibly high, and therefore the 
problem cannot be solved in an acceptable running time if the input is too big. An iterative 
algorithm is designed to overcome this problem, when the set of products too large. 

The set of products is divided into smaller subsets, which are iteratively processed: the reduced 
products set is given as input to the MILP described above. The solution is stored and the 
booked consumptions vector is updated to account for the consumptions 𝑞𝑠
iterations. Hence 𝑞̂𝑠 becomes 𝑞̂𝑠 − 𝑞𝑠
machine m is used as a lower bound for the start times on m in the next iteration by adding 
the following constraints  

1. Moreover the maximum assigned start time L(m) of each 

1 of the first 

Annex4 - 7 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
     
 
 
 
 
 
 
 
A new solution is found and the process is repeated until all orders are assigned (or until there 
is no more time available for the scheduling). 

Annex4 - 8 

 
 
 
 
 
 
 
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Steel  Industry  is  an  important  energy  consumer  facing 
problems  of  electrical  grid  instability  also  due  to  the 

increasing  of 

renewable  energy 

sources.  Power 

fluctuations  have  important  influences  on  production 
costs (fares) and continuity (grid disconnections) pushing 

towards  a  closer  cooperation  with  the  grid  operators 
asking  for  the  improvement  of  power  engagement 

forecast and monitoring. 

The  analysis  of  European  electricity  markets  and 
specifically  Italy  and  Germany  ones  highlights  how  the 

steel  manufacturers  can  interact  and  take  advantage 

from  them.  The  focus  of  the  analysis  is  from  the  day-
ahead  market  to  the  short  term  one  being  the  most 

interesting  to  implement  reaction  policy  and  tools.  The 

analysis  shows  that  there  are  opportunities  in  all  the 
markets.  

The continuous enhancement of process monitoring and 

control,  from  an  energetic  point  of  view,  makes  more 
easy  the  forecasting  of  the  plant  electricity  demand  in 

order to participate profitably in the energy markets. 

DYNERGYSteel  developed  dynamic  approaches 

for 

electricity  demand  monitoring  and  timely  reactions  to 

external grid and internal process situations to avoid non 
flexible  equipment  disconnection  and/or  financial  fines 

when deviating from energy contingent.  

This approach has been tested on different use cases in 
the four industrial partners plants highlighting in general 
good potential. 

Studies and reports