Yoshiharu AMANO, @SICE2022 2022/9/7

1

JEMIMA Special Talk

“Automation”
from the Perspective ofan Energy Management System
Professor Yoshiharu Amano,
Waseda University
September 7th 16:45-17:45
yoshiha@waseda.jp
AMANO LAB: Energy and Systems Engineering for Smart SocietyYoshiharu AMANO, @SICE2022 2022/9/7

Self-introduction

Yoshiharu AMANO, Dr. Eng., Professor,
Faculty of Science and Engineering, Waseda University
Director: IONL-WISE(Industrial Open-Network Lab, WISE)
Director: RIPES-ACROSS( Research Institute for Power and Energy Systems,
Advanced Collaborative Research Organization for Smart Society)2022- Advisor, JEITA Control & Energy Management Committee-WG1, RENKEI control
2019-2021 Chair: JEMIMA FEMS(IEC 63376) committee, TC65/JWG14, 17 expert
2008.4 - Professor, Waseda University
2008.3 - 2008.12
Visiting Professor, École polytechnique fédérale de Lausanne, Switzerland
2002.4 - 2008.3
Associate Professor, Waseda University
2000.4 - 2002.3
Assistant Professor, RISE (Research Institute for Science and Engineering), Waseda University
AMANO LAB: Energy and Systems Engineering for Smart SocietyYoshiharu AMANO, @SICE2022 2022/9/7

C)JAXAC)JAXA

基幹理⼯学部

AMANO LAB: Energy and Systems Engineering for Smart Society

3

Yoshiharu AMANO, @SICE2022 2022/9/7

Engineering is a knowledge system that evolves technology

Learn from Society and develop technology for Society
Information

World
Energy, material

Human

Mind

Mind

Mind

Mind

information

AMANO LAB: Energy and Systems Engineering for Smart Society

SocietyYoshiharu AMANO, @SICE2022 2022/9/7

Organization
Organization

Waseda Univ.
Faculty
SchoolDept. Applied Mechanics andAerospace Engineering
理⼯総研
スマート社会技術
融合研究機構Amano Lab.

WISE

IONL

ACROSS

RIPES
Smart Society

Smart Production

Funding Agencies

Collaborators

JSPS, NEDO,
JST,…

Company,
Institute; FCG, IO-Link, FDT,JEMIMA, JEITA, …

AMANO LAB: Energy and Systems Engineering for Smart Society

IONL: Industrial Open-Network Lab.

Yoshiharu AMANO, @SICE2022 2022/9/7

Founded in 2003
Target︓Process Automation，Factory Automationn Field information: Hierarchy from field to manufacturing execution system (MES) (ISA 95 model, Level 0 to 3)
n Holding technical seminars (every month to three months)• IONLFCG section

IO-Link section

FDT section

AMANO LAB: Energy and Systems Engineering for Smart Society

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Yoshiharu AMANO, @SICE2022 2022/9/7

Contents

7

n Challenges to overcome
n Energy Management Systemn Automation is a measure of evolution of technologyn Summary

AMANO LAB: Energy and Systems Engineering for Smart SocietyChallenges to overcome in societyn Develop Sustainable Societyp Climate Change due to GHG;
• Human activity• Re-build energy system
p Optimization of system

configuration

AMANO LAB: Energy and Systems Engineering for Smart Society

Yoshiharu AMANO, @SICE2022 2022/9/7

8

Yoshiharu AMANO, @SICE2022 2022/9/7

Activity of life changed our environment
Great Oxidation Event; The event is inferred tohave been caused by cyanobacteria producing
the oxygenO2 build-up in the Earth's atmosphere

Red and green lines represent the range of the estimates
while time is measured in billions of years ago (Ga).

By Oxygenation-atm.svg: Heinrich D. Holland derivative work: Loudubewe (talk)- Oxygenation-atm.svg, CC BY-SA 3.0,
https://commons.wikimedia.org/w/index.php?curid=12776502

Biliion Years ago

AMANO LAB: Energy and Systems Engineering for Smart Society

NOWYoshiharu AMANO, @SICE2022 2022/9/7

we put more carbon dioxide into the atmosphere than natural processes can remove

https://www.climate.gov/news-features/understanding-climate/climate-change-atmospheric-carbon-dioxide
AMANO LAB: Energy and Systems Engineering for Smart Society

10Yoshiharu AMANO, @SICE2022 2022/9/7

Global human-made mass exceeds all living biomass
On average, for each person on the globe, anthropogenic
mass equal to more than his or her bodyweight is
produced every week.

4 Gt

Animals
Plastics…… 4 Gt…… 8 Gt

8 Gt

Trees and shrubs …………………. 900 Gt
Buildings and infrastructure ….1,100 Gt

900 GtSource: Elhacham, E., Ben-Uri, L., Grozovski, J. et al. Global human-made mass exceeds all living
biomass. Nature 588, 442–444 (2020). https://doi.org/10.1038/s41586-020-3010-5
AMANO LAB: Energy and Systems Engineering for Smart Society

11,00 Gt

Yoshiharu AMANO, @SICE2022 2022/9/7

Sustainable Energy Systemn12Human productive activities on earth
involve the use of energy.

Manipulating resources (energy andmaterial) changes the world

Thermodynamics

AMANO LAB: Energy and Systems Engineering for Smart SocietyYoshiharu AMANO, @SICE2022 2022/9/7

Thermodynamic perspective

Boundary

SUROUNDINGS

Material outflow

Material inflow

EnergyTransfer𝑚̇ !" 𝑒!"System(Heat) 𝑄̇ !"KE, PE, State(T,P;X)
∆𝐸
∆𝑆
∆𝑀

(Work) 𝑊̇ !"𝑚̇ #$% 𝑒#$%Energy Transfer

Environment
Dead State(T0,P0;X0)
AMANO LAB: Energy and Systems Engineering for Smart Society(Heat) 𝑄̇ #$%(Work) 𝑊̇ #$%Yoshiharu AMANO, @SICE2022 2022/9/7

Thermodynamic perspective on production

Enterprise

Surroundings

Energy Loss

Production facility
From other Enterprises
Material transport

Material
Sub- distribution

mini. exergy
Energy transfer

Production process

Wastematerial
By-products

products/
materials

products

Energy transfer

Products
To other Enterprises

Energy facility
Energy transfer
Work / Heat

AMANO LAB: Energy and Systems Engineering for Smart SocietyYoshiharu AMANO, @SICE2022 2022/9/7

Production Process

15

Information flow along with material/energy flow in Industrial facility
From “linear” material flow to “closed loop” in order to recycling finite resource
Order(Information)

Resource(Material,
Energy)Production

By-Product(Information,
Material, Energy)
Useful/not useful

Product(Information,
Material, Energy)

Leaner process of production

AMANO LAB: Energy and Systems Engineering for Smart Society

Greenhouse Gas
emission

Cost
User

Yoshiharu AMANO, @SICE2022 2022/9/7

Change entire lifecycle of production system

16

Goal; Change lifecycle of products, from design to production,
consumption and disposal.
Sustainable
Circular material flow, including by-products

How can weachieve thegoal?

INDUSTRY

Production
transportationtransportation

Disposal

COMMERCE

Consumption
COMMUNITY transportation

CONSUMER

AMANO LAB: Energy and Systems Engineering for Smart SocietyYoshiharu AMANO, @SICE2022 2022/9/7

Change entire lifecycle of production system

17

Short period; EMSLong period; Planning

Responding quickly

How to design a (large)energy system?

How to operate a (complicated)energy system?

System optimisation for investment

System optimisation for operation

Regarding future: forecasting, but still uncertain

Regarding future: uncertain change

Setting an Ideal goal

Forecasting

AMANO LAB: Energy and Systems Engineering for Smart SocietyYoshiharu AMANO, @SICE2022 2022/9/7

Solution for DX, but…

18

Design large-scale complex systems while considering optimal operation
Su
Order/
pply
ProcurementchainProduction Planning/
ERP
management
SCM

Process design

MES / MOMManagement
SystemProduct design
Product planningResearch &Development

E

in
ng

e

n
eri

g

a
ch

Production
Distribution
Maintenancein

AMANO LAB: Energy and Systems Engineering for Smart Society

CustomerSustainable Society and EMS?

Yoshiharu AMANO, @SICE2022 2022/9/7

n Major changes in energy systems configuration are inevitablep From fossil fuel to renewable resourcen The system is large and very complicatedp Systematic approach

Set up an ideal energy system and actively commit to its realization.
Develop optimal operation mechanisms for energy facilities.
EMS can contribute to transition to sustainable energy system with optimal
operation.

AMANO LAB: Energy and Systems Engineering for Smart Society

19Energy Management System

Yoshiharu AMANO, @SICE2022 2022/9/7

n Example of EMS
p Development of optimal operation model for demand side energy

resources corresponding to demand responsen What is Energy Management System?
• General definition by EnMS, ISO 50000 series• Fundamental functions for EMS• How can we classify the system; Automation Levels

AMANO LAB: Energy and Systems Engineering for Smart Society

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Yoshiharu AMANO, @SICE2022 2022/9/7

Energy system design

21

Desing optimal system configuration to meet demand/supply change>>>

Most simple configuration

Supply change

Energy loss

Energy resource

Energy output

ON-OFF
Energy resource

Energy resource

Demand change

Energyconversion

Energyconversion

Energyconversion
ON-OFF

Energy output

Energystorage

AMANO LAB: Energy and Systems Engineering for Smart Society

Energy output

s article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TSG.2016.2581882, IE
Transactions on Smart Grid

Yoshiharu AMANO, @SICE2022 2022/9/74

Optimal operation with uncertainty: MPC
IEEE TRANSACTIONS ON SMART GRID

22

Energy demand uncertainty
Demand-side EMS (HEMS)!
PV!

Objective:
Minimization of expected operational cost under constraints of resident’s comfort.

Resident’s Reverse
demand! power ﬂow!

Timeline of HEMS!

Forecast!

HVAC
AC
Smart
meter!

PV!

AW
TB
SB!

HEMS
controller!

FC!

FC

Operational
plan!

Control!

!!

AC!

TOU pricing!

LRT!

O!

C!

Time!
0:00! 1:00! 2:00! 3:00! 4:00!

Distribution
network!

F!

24:00!25:00!

27:00!

Time!
GEMS
controller!

SVR!

Electricity consumption
Reverse power ﬂow!

LVR!

F! O! C!
Residential
House with PV!

Voltage!

Upper limit!
Lower limit!
Line length!

Supply-side EMS (GEMS)!

F!O! C!
F!O! C!
F!O! C!

!!

Timeline of GEMS!
Objective:
Minimization of expected voltage violation, the number of tap control, and PV curtailment.

Fig. 1. Schematic image of distributed EMS framework. The GEMS aims to reduce the expected voltage violation in the distribution network, the number of
AMANO
LAB: Energy
for SmartSocietytap controls, and the amount of curtailed PV output.
The HEMS
aimsand
toSystems
reduce Engineering
the expected
operatingcost for consumers while maintaining the resident’s
thermal comfort. Assume that the GEMS provides a TOU pricing program to the consumers, and the HEMS derives a day-ahead operational plan for residential
energy appliances in order to reduce the expected daily operating cost. The control results of HEMSs affect the control of the GEMS through the change inenergy consumption and reverse power flow from the residential PV.

Yoshiharu AMANO, @SICE2022 2022/9/7

EMS delivers optimal strategy to contlloers

23

Control segment
Control segment

EMS à
Optimal storategy

Control segment

When and Which facility?
ON-OFF/start-stop

Set-point value

Which operation mode? (objectives: CO2 emission min.?, Low cost op?)

Mixed-Interger Programing model (MIP model)
AMANO LAB: Energy and Systems Engineering for Smart Society

JSME 第26回動⼒・エネルギー技術シンポジウム
2022/7/14(⽊) OS4 省エネルギー・コジェネ・ヒートポンプ(2) 16:00-16:20

Yoshiharu AMANO, @SICE2022 2022/9/7

デマンドレスポンスに対する需要家側エネルギー資源の最適運⽤モデルの開発*1 Yusuke Shiga, Akira Yoshida, Yoshiharu Amano, Impact for CO2 Emission Reduction by Ice Storage Tank in District Cooling System
Using Variable Renewable Energy, Proceedings of ECOS 2022, Copenhagen, 2022.
*2 Akira Yoshida, Yoshiharu Amano, Multi-horizon Forecasting and Operational Planning Method of Energy Storage Under a Demand
Response, Proceedings of ECOS 2022, Copenhagen, 2022.

Development of optimal operation model for demand side energy
resources corresponding to demand response
Application: Water Distribution System and District Heating and Cooling System
ACROSS Dr. Akira YOSHIDA and Yoshiharu AMANO
AMANO LAB, Waseda Univ.
AMANO LAB: Energy and Systems Engineering for Smart SocietyYoshiharu AMANO, @SICE2022 2022/9/7

Objective

25

n Develop and evaluate an optimisation model for virtual power plant (VPP)

that can be easily applied to arbitrary plants to generate arbitrary power
profilesüüüü

Digitalization by a method for estimating the connection relationshipbetween entities based on diagrams and texts in reports by AI
Data-driven model identification; Estimation of physical models fromprocess variables based on conservation laws
Time-series forecasting with quantiles as exogenous variables
Application of annealing technology to solve large-scale problems thatcannot be solved by existing mixed-integer linear programming (MIP)
solvers; inspired by quantum computation

AMANO LAB: Energy and Systems Engineering for Smart SocietyYoshiharu AMANO, @SICE2022 2022/9/7

Application for a Virtual Power Plant (VPP)

Aggregator

26• Limited profitability and opportunity: 10 EUR/kW for DR at most.
• No control system modifications due to limited budget.
• Plant operators have supply responsibilities.
• Retrofitting the Information system to the existing control system would besuitable.
Given specification

Processvariable

Estimate inclusion
relations and connections

IssuesAutomatic recognition of “text”
meaning on schematic diagrams

Solutions

Regularexpression

Gray box
modelling forenergy and mass
IO balance

Correlation
analysis

Timeseries
forecasting

Criticism ofpredictions
against the mean

Propose operational
strategy under
uncertainty

Soft constraints
requirecomputing power

Soft-constrained
quadratic
unconstrained
binary optimization

Quantum-inspired
annealing which is
hardware solver

Quantile
forecasting

AMANO LAB: Energy and Systems Engineering for Smart Society

OptimalschedulingYoshiharu AMANO, @SICE2022 2022/9/7

A Virtual Power Plant for Demand Response
Correlation
analysis

Estimate relationsof entities

On/offWater demand

Hours

Decreased amountof stored energy

Water distribution*2

Pump operating time

OptimalschedulingTimeseries
forecasting

Forecast quantile

Electricity

Estimate inclusion
relations and connections
Increased amountof stored energy

Processvariable

27

Hours

Optimal on/off
status of pumps

Water demand

Estimate stored energy and rates
Model the system fromlimited information

Describe different processes as
a common abstracted model

Electricity
consumption

Energyconverter

Storage

Quantify uncertainty
of forecast byquantiles

DemandAMANO LAB: Energy and Systems Engineering for Smart Society

Decision support
under uncertainty

Yoshiharu AMANO, @SICE2022 2022/9/7

DR Overview: data, electricity, cash flow
AWS

Waseda28

Need to predict
Need to predict
behavior as
Business-as-Usual

Fujitsu

VPC

Aggregation
coordinator

Resource
V aggregator
P
N

Existing Plant
IT system

DemandDCS/PLCInformation
systemClosed network
EthernetEdge
device

TDSO

Field

DRAS*

Balancing market
After GateClose

JEPX

UtilitydemandElectricity
consumption
Energy storage and machinery*DRAS: Demand Response Application Server

AMANO LAB: Energy and Systems Engineering for Smart SocietyYoshiharu AMANO, @SICE2022 2022/9/7

Apply to Water Distribution System (WDS) as an example
Correlation
analysisPump operating time

Hours

Estimate stored energy and their speeds

n To be simplified … WDS∈

Pumps, Reservoirs

Responsible
for supply

On/offWater demand
Water demand

OptimalschedulingTimeseries
forecastingElectricity

Estimate inclusion
relations

Decreased amountof stored energy

Estimate inclusion
relations and connections
Increased amountof stored energy

Water distribution

Processvariable

29

Forecast quantile

Hours

Propose on/off

Water reservoir on the hill

Energy storage
Pump up

Pump upControllable
Controllable
AMANO LAB: Energy and Systems Engineering for Smart Society

Upper reservoir
Uncontrollable

Lower reservoir

Yoshiharu AMANO, @SICE2022 2022/9/7

System structure and data flow for demonstration on actual WDS
Electricity market
Demand-side VPP systemn There are three players

30

p Operator
p Resource aggregator (RA) of VPP
p Aggregation coordinator (AC) of VPP

n numerical experiments.
Input on/off
schedule
Database
Observed
Process variable𝑬̇ ∗!∈ 2&,…,3
𝑽̇ 2,∗!∈ 2&,…,3𝑯∗!∈ 2&,…,3
5,∗𝑽̇!∈ 2&,…,3𝜹∗!∈ 2&,…,3

Weekday,
Temperature etc.

Initial value𝑬̇ ∗3, 𝑽̇ ∗3, 𝑯3, 𝜹3Predictor
Time-series!̇𝑬!∈ #,…,&!
̇𝑽2!∈ #,…,&! !∈ #,…,&
𝜹DR signal
𝐸̇ '∈ () *+,-./01

OperatorTradeelectricity
DR event

exogenous
variable:Temp., Weather

Operating terminal

Optimizer
Operational
Strategy! !∈ #,…,&
𝜹

1min. PV

VPP system

Web app

TabletUserinterface

Forecast system

Recommendation
systemDocker engine: Docker

Proposed
system

OS:Ubuntu 20.04 LTS
Hardware: x64

AMANO LAB: Energy and Systems Engineering for Smart Society

DRAS
server30min. Power profile

LaptopLAN

RA/AC
serverOptimization solver（Fujitsu DA）

Yoshiharu AMANO, @SICE2022 2022/9/7

Tag name tells us…

31

Fragmented content
Smart
Meter*

Water
Pump Storage demand

1

1

2

2

𝑅

1
1

2

1

2

4

Regular expressions ^(.*?)hoge(.*?)$

5

2

6

3
2

4

WDS

𝐸

𝑅−1

𝑃

2

2

6

𝑃-3

𝐷−1
𝐸

𝑃-1
𝑃-2

5

𝑃-4

𝑃-4
𝑃-3

*https://www.tepco.co.jp/pg/technology/smartmeterpj.html
https://www.tepco.co.jp/en/pg/development/domestic/smartmeter-e.html

1

1
3

𝐷

1

2

1
…

…
𝑃

2

1

1

…

…
𝐸

1

Connection

relation of inclusion

𝑅

𝐷

AMANO LAB: Energy and Systems Engineering for Smart Society

𝑅−1
𝐷−1

𝑃-1
𝑃-2
𝑃

𝑅

𝐷

Yoshiharu AMANO, @SICE2022 2022/9/7

Identify I/O relationship32

n K-means clustering to classify operating points.
p Up to 8 clusters in this studyn Connect origin and blue clusters

which means that the water level rises 0.07 m when thepump operates for 15 minutes.

which means that the water level falls 0.06 m when the
4 m3/15min of water demand is occurred.

AMANO LAB: Energy and Systems Engineering for Smart SocietyYoshiharu AMANO, @SICE2022 2022/9/7

Dataflow of PV forecasting33

Training phase

Input 𝑿
Observed
Process variable𝑬̇ ∗!∈ (&,…,)
𝑽̇ (,∗!∈ (&,…,)
𝑯∗!∈ (&,…,)𝑽̇ *,∗!∈ (&,…,)
𝜹∗!∈ (&,…,)
Weekday,
Temperature etc.

Temporal Fusion Transformer

Forecasting phase

Output 𝒀
Time-series𝑬̇ ∗!∈ #,…,&𝑽̇ (∗!∈ #,…,&

ON/OFF𝜹∗!∈ #,…,&

B.Lim, Temporal Fusion Transformer for interpretable multi-horizon time series forecasting, Int. J. forecasting, 2021
AMANO LAB: Energy and Systems Engineering for Smart SocietyYoshiharu AMANO, @SICE2022 2022/9/7

Quantile forecasting for multivariable multi-horizon timeseriesn Estimate connectionn Multi-horizon forecastingn

Describing inclusions
around a reservoir
from the tag name

AMANO LAB: Energy and Systems Engineering for Smart Society

34

Yoshiharu AMANO, @SICE2022 2022/9/7

Operational planning problem by MIPnn 𝛿 ∗ = argmin 𝜌# Σ$%&' 𝛿$%&' 𝐸&̇𝜹p+𝜌+ 𝛴,-.,!∈ +,…,& 𝛾. 𝛿,-.! − 𝛿,-.,!(#

+

+

p

+𝛴!/ 𝜌0,/3

𝐸̇ ,!/ − 𝛴,-.

𝛿,-.! 𝐸̇ .

+p

+𝛴,!/ 𝜌1,/

3

𝐸̇ -!/ − 𝛴-.

4,-!/ − ℎ,-!
𝐻p+𝜌2,/ 𝛴,-!

p+𝜌3 Σ,! Σ-. 𝛿,-.!

p

𝛿,-.! 𝐸̇ .
+++𝜌4 Σ,-. Σ! 𝛿,-.! −

35

s.t.
p

Σ,-. 𝛿,-.! 𝐸̇ . ≤ 𝐸̇ ,p

𝐻,- ≤ ℎ,-! ≤ 𝐻,-*(
6,-! 𝜂,-.
ℎ,-! = 𝐻,-,!8) + 𝛿,-.! 𝜂,-.
Δ𝑡 − 𝐷Δ𝑡
if 𝑡 = 1
p 1*(
6,-! 𝜂,-.
ℎ,-! = ℎ,-,!(# + 𝛿,-.! 𝜂,-.
Δ𝑡 − 𝐷Δ𝑡 otherwisep𝐸̇ 9:;-< − 1.1𝐸̇ 9=> ≤ 𝛴,-. 𝛿,-.9 𝐸̇ . ≤ 𝐸̇ 9:;-< − 0.9𝐸̇ 9=>p

min ℎG ,-!/ , 𝐻,- ≤ ℎ,-! ∀𝑡 except for 𝜏

n Feature of this formulationp Decision variables are binary
p Objective terms work as constraints that can beviolatep The weights of each term need to be adjusted ina heuristic way

5!" 6#!$" +#.!
AMANO LAB: Energy and Systems Engineering for Smart SocietyYoshiharu AMANO, @SICE2022 2022/9/7

Operational planning problem by MIP
Electricity
consumptionn 𝛿 ∗ = argmin 𝜌# Σ$%&' 𝛿$%&' 𝐸&̇𝜹pp+𝜌+ 𝛴,-.,!∈ +,…,& 𝛾. 𝛿,-.! − 𝛿,-.,!(#+𝛴!/ 𝜌0,/3

𝐸̇ ,!/ − 𝛴,-.

𝛿,-.! 𝐸̇ .

++𝛴,!/ 𝜌1,/

3

𝐸̇ -!/ − 𝛴-.

4,-!/ − ℎ,-!
𝐻p+𝜌2,/ 𝛴,-!

p+𝜌3 Σ,! Σ-. 𝛿,-.!

p

𝛿,-.! 𝐸̇ .
+Suppress hunting

Deviation from thenforecasted
electricity
consumption perwater storage group

Deviation from forecasted
water level 𝐻++𝜌4 Σ,-. Σ! 𝛿,-.! −nDeviation+from the forecasted
electricity
consumption
per pump station
+

p

36

s.t.
p

Σ,-. 𝛿,-.! 𝐸̇ . ≤ 𝐸̇ ,Contracted powerp

𝐻,- ≤ ℎ,-! ≤ 𝐻,-

Operable range of water level*(
6,-! 𝜂,-.
ℎ,-! = 𝐻,-,!8) + 𝛿,-.! 𝜂,-.
Δ𝑡 − 𝐷Δ𝑡
if 𝑡 = 1
balancing
of
water
level
p 1
*
(
6,-! 𝜂,-.
ℎ,-! = ℎ,-,!(# + 𝛿,-.! 𝜂,-.
Δ𝑡 − 𝐷Δ𝑡 otherwisep

DR should be ±10%𝐸̇ 9:;-< − 1.1𝐸̇ 9=> ≤ 𝛴,-. 𝛿,-.9 𝐸̇ . ≤ 𝐸̇ 9:;-< − 0.9𝐸̇ 9=>p

min ℎG ,-!/ , 𝐻,- ≤ ℎ,-! ∀𝑡 except for 𝜏Constraint of water level

Feature of this formulationp Decision variable is only binary
p Objective terms work as constraints that can beviolatep The weights of each term need to be adjusted inan heuristic way

5!" 6#!$" +#operating pumps multiplied per water storage group#.!

Deviation of pump operating time fromAMANO LAB:per
Energyand Systems
Engineering
averagewater
storagegroupfor Smart Society

1 MW downtrend DR result optimized by Gurobin Incentive-based DR specification

Yoshiharu AMANO, @SICE2022 2022/9/7

37

n Difficult to solve; problem size is too

p 1 MW curtailment

large

p Three hours

p 0.25 hr time stepp ±10% success criteria

p #107 / 129 controllable pumps
p #20 smart meters, #40 reservoirs
p #binary decision variables 10,272
p #quadratic objective terms 1,444,742

Controllable

DR duration
DR duration
AMANO LAB: Energy and Systems Engineering for Smart Society

Uncontrollable

Yoshiharu AMANO, @SICE2022 2022/9/7

Breakdown of profiles: A reservoir
BAU 700kW→DR 0KW

Load balancing

Stopped pumpby DR request
AMANO LAB: Energy and Systems Engineering for Smart Society38Annealing solver can solve with 60 seconds.
n

Yoshiharu AMANO, @SICE2022 2022/9/7

39

Operational planning by MIP solvern Quadratic Unconstrained Binary
Optimization(QUBO) solved in 1 minutes.
n With 86% MIP-gap, Gurobi took 8 hoursn up to 100,000 variables
Convert to QUBOGeneral MIP solver，Gurobi
Fujitsu
3rd-gen Digital Annealer(DA)https://www.fujitsu.com/jp/documents/digitalanne
aler/researcharticles/DA_WP_JP_20210922.pdf

8 hours

ON/OFFAMANO LAB: Energy and Systems Engineering for Smart SocietyYoshiharu AMANO, @SICE2022 2022/9/7

SUMMARYn Developed and evaluated an optimization model for virtual power plant (VPP)
p A method for estimating the connection relationship between entities by text mining method
p Input-output relationship modeling of elements based on process variables (PV)p Time-series forecasting with quantiles as exogenous variables
p Annealing technology for quantizing and solving problems that cannot be solved by existing

mixed-integer linear programming solvers
p Methodology for Discretizing Continuous Variables to Formulate Binary Optimization Problems

Analytics tools are ready for modeling the energy management problem.
Large-scale combinatorial optimization can be solved with a special hardware!

AMANO LAB: Energy and Systems Engineering for Smart Society

40

Yoshiharu AMANO, @SICE2022 2022/9/7

Management system model can be formulated as optimization problems
Optimization approach is promising fordesigning large, complicated system andoperating the system

Variety of modeling/formulating theories are available; from MIP to QUBO

Any question? Ask to the expert: Dr. Akira Yoshida by e-mail; a.yoshida@aoni.waseda.jp

AMANO LAB: Energy and Systems Engineering for Smart Society

41

Energy Management System

Yoshiharu AMANO, @SICE2022 2022/9/7

n Example of Energy Management System
p Large-scale hierarchical optimisation of resource aggregation for Demand
Response / VPPn What is Energy Management System(EMS)?
p Management and Control
p Definition of EMS in ISO, IEC
p Fundamental functions for EMS
p How can we classify the system; Automation Levels

AMANO LAB: Energy and Systems Engineering for Smart Society

42

Yoshiharu AMANO, @SICE2022 2022/9/7

Management and Control

43

n Control; automatic regulation of dynamic behavior of systems
n Management; automatization of decision-making activity with prediction /forecasting on uncertain system; system of systems
Management target;
Hybrid system = Logic(Decisionmaking) + Dynamics

Multi-objective
Optimisation
with prediction /forecasting on
uncertain system

Control target;
Dynamics ofDeterministic
systemAMANO LAB: Energy and Systems Engineering for Smart Society

MINLP:
Mixed Integer
Linear/Nonlinear
Programming
SCP: Sequential
Convex
Programming
SQP: Sequential
Quadratic
ProgrammingYoshiharu AMANO, @SICE2022 2022/9/7

Management and Control
n44

Objectives ; manage the system to achieve goals

p Represent Plant; Identification of the target system’s behavior modelp Configure system including feedback loop;
p Management system is composed of functions of sensing/analysis/optimization andinstruction• Located on LEVEL-3 of ISA-95 model

Learning from Past
State Prediction

ISA 95 model

Future forecasting

Control

LEVEL 4ERP

LEVEL 3

MOM

Past
State estimation

Now

FutureDecision-Making

LEVEL 2 Engineering/Control
LEVEL 1

Field Control

LEVEL 0Sensors/Actuators

Management
AMANO LAB: Energy and Systems Engineering for Smart SocietyYoshiharu AMANO, @SICE2022 2022/9/7

ISA95, Enterprise-Control System Integration

Level 4Loop/cycle
speed
Slow

ERP

ERP: Enterprise Resource Planning
SCM: Supply Chain Management
MOM: Manufacturing Operations Management
MES: Manufacturing Execution System
DCS: Distributed Control System
PLC: Programming Logic Controller

MOM/MESLevel 3

Energy and Systems Engineering for Smart Society
It makes sense to establish data
connectivity between each level
through a common database hub.
DCS・PLC

Level 2

Fast
Level 0, 1

SCM

45

Field Devices
Sensor/Actuator

It is reasonable to share the datathat spans the segments withineach level as data afterconnecting with the Gateway.

AMANO LAB: Energy and Systems Engineering for Smart SocietyYoshiharu AMANO, @SICE2022 2022/9/7

MOM/MES46

ISA95, Enterprise-Control System Integration

Level 4Loop/cycle
speed
Slow

ERP

Areas that are substituting human activities andbehaviors that connect the field and the head office
Energy and Systems Engineering for Smart Society
MOM/MESLevel 3

DCS・PLC

Level 2

Fast
Level 0, 1

SCM

Areas that are replacing human activities and behaviors
in supply chains, etc.

Field Devices
Sensor/Actuator

Areas where sufficient technology
exists for automation

https://blog.lnsresearch.com/whats-the-difference-between-mom-mes
AMANO LAB: Energy and Systems Engineering for Smart Society

Definition of EMS from ISO, IEC

Yoshiharu AMANO, @SICE2022 2022/9/7

n management system

set of interrelated or interacting elements of an organization to establish
policies and objectives and processes to achieve those objectives
From ISO 50001:2018 Energy management systems — Requirements with guidance for use

n energy management system

control system designed to monitor the environment and the use of energy ina facility and to adjust the parameters of local control loops to conserve
energy while maintaining a suitable environment
From ISO 16818:2008(en)n energy management

process for monitoring, analysis, reporting and improvement of energy
efficiency
From ISO/IEC TS 22237-7:2018(en)AMANO LAB: Energy and Systems Engineering for Smart Society

47

Yoshiharu AMANO, @SICE2022 2022/9/7

Fundamental functions for EMS

48

n Idea for IEC 63376 FEMS

Modified from ISO/IEC TS 22237-7:2018 “process for monitoring, analysis, reporting andimprovement of energy efficiency”
Level 4
policies and objectives

Optimization results

Analysis

Monitoring
the “loop”
suggests

continuous
activity

Level 3

MOM/MES

Instruction

Optimization

actual and reference data

Operation strategy

Levels 0~2
AMANO LAB: Energy and Systems Engineering for Smart SocietyYoshiharu AMANO, @SICE2022 2022/9/7

Design of Management System
ISA 95 model
Level 4

Instruction
Data

Data

Data

Data

Monitoring

Data

Perception

DATA
LOOP

Feedback

Integration
Coordination
Decision-making

Data

Prediction/Forecasting
Change detection
Causal inference
Energy recovery
potential

Optimisation

Strategies

Decision

Level 3

Analysis

Information

policy/order(From/to Level 3,4) Data-Hub/Gateway(From/to Lower Level) Data-Hub/Gateway
Level 2
Level 0, 1

Controller
Segment A
Devices

Controller
Segment B
Devices

AMANO LAB: Energy and Systems Engineering for Smart Society

Controller
Segment C
DevicesYoshiharu AMANO, @SICE2022 2022/9/7

Automation level in EMS

Human
Artificial system

Boundary

AMANO LAB: Energy and Systems Engineering for Smart SocietyYoshiharu AMANO, @SICE2022 2022/9/7

Evolution of Technology

51

Automation and technology???

AMANO LAB: Energy and Systems Engineering for Smart SocietyYoshiharu AMANO, @SICE2022 2022/9/7

Evolution of Technology

52

automatic | adjective 1 (of a device
or process) working by itself with little or no direct humancontrol
automation; late 19th century (with reference
to a theory that living organisms

act in a purely mechanical way ):irregular formation from automatic + -ation.
The current sense dates from the 1940s.

Oxford Dictionary of English
Copyright © 2010, 2021 by Oxford University Press. All rights reserved.

AMANO LAB: Energy and Systems Engineering for Smart SocietyYoshiharu AMANO, @SICE2022 2022/9/7

Evolution of Technology

53n What is Technology?
p Engineering is a knowledge system that evolves technologyl Technology is a knowledge system that realize
accountable functionl The evolution of technology manifests itself as theextension/expansion of boundary of automation

AMANO LAB: Energy and Systems Engineering for Smart SocietyYoshiharu AMANO, @SICE2022 2022/9/7

Evolution of Technology

54n Mechanism of human recognition of the world
p How can we perceive the external world as it is, without biases?

based onAMANO LAB: Energy and Systems Engineering for Smart SocietyYoshiharu AMANO, @SICE2022 2022/9/7

Evolution of Technology

55

p Human activity inside and outside

How can we perceive the external world?
• How can we know the external world as it is, without biases?
•

actionHuman
判断
Decisionmaking

World

System 2︓ Conscious/logical思考
Thought

知覚
perception

sensing
推論
Inference

AMANO LAB: Energy and Systems Engineering for Smart Society

System 1︓ Unconscious/intuitive

Yoshiharu AMANO, @SICE2022 2022/9/7

Evolution of Technology

56

p Social activity and the social system• Function model

language

Cooperative
actionWorldsensingsystemsystemsselfothersthought

thought

perception

perception

Inference

Inference

AMANO LAB: Energy and Systems Engineering for Smart SocietyYoshiharu AMANO, @SICE2022 2022/9/7

Management system

57

n Management system, above the control system, shouldhave capability of recognition of external world (set of
facilities relevant to energy) and decision-making
functions based on policy.
ERP

Feed-backpolicyEMS

Feed-backstrategy

DCS/PLC

Feed-backSet-point
mode

AMANO LAB: Energy and Systems Engineering for Smart Society

FacilityYoshiharu AMANO, @SICE2022 2022/9/7

message58

Evaluate your system from the perspective of
Evolution of Technology.
degree of complexity UP!

Automation level is the key indicator of
the Evolution of the Technology.

AMANO LAB: Energy and Systems Engineering for Smart SocietyYoshiharu AMANO, @SICE2022 2022/9/7

Technology for Sustainability

59

RENEWABLESFINITE MATERIALSRENEWABLES FLOW MANAGEMENT

ecosystem

STOCK MANAGEMENTFARMING/COLLECTION1PARTS MANUFACTURER

BIOCHEMICALFEEDSTOCKREGENERATIONPRODUCT MANUFACTURER
RECYCLEBIOSPHERESERVICE PROVIDERREFURBISH/
REMANUFACTURE

SHARE

REUSE/REDISTRIBUTE
BIOGAS

MAINTAIN/PROLONG

CASCADES
6 2803 0006 9

ANAEROBICDIGESTION

CONSUMERUSER

COLLECTION

COLLECTION

EXTRACTION OF
BIOCHEMICALFEEDSTOCK2
1 Hunting and fishing
2 Can take both post-harvest and post-consumer waste as an input
SOURCE
Ellen MacArthur Foundation
Circular economy systems diagram (February 2019)www.ellenmacarthurfoundation.org
Drawing based on Braungart & McDonough,Cradle to Cradle (C2C)

https://emf.thirdlight.com/link/7kvazph93afk-owveai/@/preview/1?o
MINIMISE SYSTEMATICLEAKAGE AND NEGATIVE
EXTERNALITIESAMANO LAB: Energy and Systems Engineering for Smart SocietyYoshiharu AMANO, @SICE2022 2022/9/7

Summaryn Optimization is the key technology to design and operate alarge/complicated systemnTo build an artificial system in societyp Organize the system as an ecosystem
p Set broader boundaries and prepare data(information) flows with all stakeholders

to enable them to participate• System that circulates energy + matter + data(information) at every levelp If we understand the inner/outer mechanism of our mind, management system will

evolve.
AMANO LAB: Energy and Systems Engineering for Smart SocietyYoshiharu AMANO, @SICE2022 2022/9/7

end

61

Thank you for your attention.

AMANO LAB: Energy and Systems Engineering for Smart Society