NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

IEA Wind Task 24 

Integration of Wind and 

Hydropower Systems 

Volume 2: Participant Case 

Studies 

Authors: Tom Acker, Northern Arizona 

University on behalf of

National Renewable Energy Laboratory

U.S. Department of Energy Wind and 

Hydropower Program

Prepared for the International Energy Agency Implementing 

Agreement for Co-operation in the Research, Development, 

and Deployment of Wind Energy Systems 

Technical Report

NREL/TP-5000-50182

December 2011

National Renewable Energy Laboratory

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IEA Wind Task 24 Final Report, Vol. 2

i

Preface

In November 2003, the International Energy Agency (IEA) Wind Implementing Agreement (IA) 

for Cooperation in the Research and Development of Wind Turbine Systems held its Topical 

Expert Meeting #41 on the Integration of Wind and Hydropower Systems. This meeting 

convened a group of industry, academic, and government officials with expertise in wind power, 

hydropower, and utility and transmission system planning and operation. 

Their purpose was to discuss the potential for coordinated operation of wind and hydropower in 

serving load, the benefits and detriments in doing so, and to identify the related opportunities and 

issues. As a result of this meeting and interactions with the IEA Hydropower IA, a 

recommendation was made to IEA Wind to establish a formal research task to address the 

myriad of questions and unresolved issues pertaining to the topic. Subsequent to this meeting, in 

2004, IEA Wind established a research and development (R&D) task to investigate the potential 

for integrating wind and hydropower resources on the electrical grid. The research task, also 

known as an “Annex,” was the twenty-fourth such task established by IEA Wind, and was 

entitled, Task 24: Integration of Wind and Hydropower Systems. Seven member countries of 

IEA Wind joined the task: Australia, Canada, Finland, Norway, Sweden, Switzerland, and the 

United States. When established, an R&D task is assigned an Operating Agent (i.e., managing 

director). For Task 24, the National Renewable Energy Laboratory (NREL) in the United States, 

on behalf of the U.S. Department of Energy, was selected as the Operating Agent.

The primary purposes of Task 24 were to conduct cooperative research concerning the 

generation, transmission, and economics of integrating wind and hydropower systems, and to 

provide a forum for information exchange. The former of these two purposes was addressed 

through case study projects performed at participating institutions within each member country. 

The latter purpose related to information exchange was accomplished via a series of 

collaborative R&D meetings, seven of which were held: a kickoff meeting (February 2005 in the 

United States); one web meeting (June 2006); and five R&D meetings (September 2005 in 

Switzerland, September 2006 in Australia, May 2007 in Italy, 

September 2007 in Norway, and June 2008 in Quebec, Canada).

The IEA Wind Task 24 Final Report summarizes and presents the 

results of the work conducted by the task participants, the important 

issues and analysis methods identified, and the related conclusions. 

The report was assembled in two volumes: the first providing 

objectives, background, summary results, and conclusions; and the 

second describing the methods of study employed and details about 

the participant case studies upon which the conclusions of the task 

were drawn.
IEA Wind Task 24 Final Report, Vol. 2

ii

Acknowledgments

There were many organizations and people that contributed to the Task 24 final report via 

contributions to the various case study projects conducted by the participants, more than can be 

acknowledged here. Thanks are due to all these people. Special recognition is due to the 

following contributors for their participation in the task, its meetings, for oversight of their 

country’s contributions to the case studies, and for organizing their contributions to this report: 

Tom Acker, Ph.D., Northern Arizona University, Operating Agent representative on 

behalf of NREL, primary author and editor

United States

Mr. Robert Zavadil, EnerNex Corporation

Mr. Brian Parsons, National Renewable Energy Laboratory

Ms. Pat Weis-Taylor, PWT Communications, LLC, Secretariat IEA Wind

Marian Piekutowski, Ph.D., Hydro Tasmania

Australia

Mr. Mike Underwood, Hydro Tasmania

Ms. Simone Lelande, Natural Resources Canada

Canada

Mr. André Robitaille, Hydro Québec

Mr. Tom Molinski, Manitoba Hydro

Hannele Holltinen, Ph.D., VTT Technical Research Centre

Finland

Juha Kiviluoma, Ph.D., VTT Technical Research Centre

Mr. John Olav Tande, SINTEF Energy Research

Norway

Mr. Magnus Korpås, SINTEF Energy Research

Lennart Söder, Ph.D., KTH Swedish Royal Institute of Technology

Sweden

Mr. Niels Nielsen, Kator Research, Secretary of IEA Hydropower IA

IEA Hydropower Implementing Agreement
IEA Wind Task 24 Final Report, Vol. 2

iii

List of Acronyms

AEMO Australian Energy Market Operator

AGC Automatic Generation Control

BA Balancing Area

BPA Bonneville Power Administration

CAISO California Independent System Operator

CCGT Combined Cycle Gas Turbine

CHP Combined Heat and Power

ELCC Effective Load Carrying Capacity 

EMPS Multi-Area Power Market Simulator

FCAS Frequency Control Ancillary Services

HLH High Load Hour

HVDC High-Voltage Direct Current

IA Implementing Agreement

IEA International Energy Agency

IEA Wind The IEA Implementing Agreement for Co-operation in the Research, 

Development and Deployment of Wind Energy Systems

ISO Independent System Operator

LOLP Loss of Load Probability

LLH Low Load Hour

MM5 Mesoscale Model

NERC North American Electricity Reliability Corporation

NREL National Renewable Energy Laboratory, U.S. Department of Energy

NordPool The Nordic Power Exchange: The Single Power Market for Norway, 

Denmark, Sweden, and Finland

O&M Operations and Maintenance

NPC Non-Power Constraint

PIRP Participating Intermittent Resources Program

PNCA Pacific Northwest Coordination Agreement 

PUD Public Utility District

R&D Research and Development

RMSE Root Mean Square Error

RNL Reanalysis

RTO Regional Transmission Organization

SINTEF The Foundation for Scientific and Industrial Research at the Norwegian

Institute of Technology

SMUD Sacramento Municipal Utility District

TSO Transmission System Operator

TWD Tail Water Depression

U.S. United States

USACE U.S. Army Corps of Engineers 

VTT Technical Research Centre of Finland

WAPA Western Area Power Administration
IEA Wind Task 24 Final Report, Vol. 2

iv

Executive Summary 

Worldwide, hydropower facilities possess a significant amount of installed electric generating 

capacity. IEA statistics indicate that at the end of 2001 there was in excess of 450,000 MW of 

installed capacity within IEA member countries, with about half in Europe and half in North 

America. In addition to conventional hydropower, there is more than 80,000 MW of installed 

pumped-hydro capacity in IEA countries. In contrast, utility-scale wind power is relatively new 

in the electric market, but increasing rapidly. In 2003, when the topic of Task 24 was initially 

being discussed, there were just over 31,000 MW of wind power installed, an amount that 

increased to in excess of 140,000 MW by the end of 2009. Due to its competitive costs, coupled 

with the fact that it is a clean energy resource, wind energy capacity is likely to continue to grow 

substantially over the next two decades. Because of the potential for synergistic operation of 

wind and hydropower facilities, many countries are investigating the opportunity to integrate 

wind and hydropower systems in order to optimize their output through coordinated operation.

The hope is to realize such benefits as lowering the cost of ancillary services required by wind 

energy, taking advantage of the built-in energy storage available at hydro facilities; the 

opportunity to more effectively utilize existing hydro and transmission facilities; the potential for 

improving hydrologic operations; and an overall energy supply portfolio that is more diverse, 

robust, and cleaner. With wind power penetrations increasing worldwide, the topics of Task 24 

are more relevant than ever.

For the reasons described above, in 2004, IEA Wind formed R&D Task 24,1

Goal 1) Establish an international forum for exchange of knowledge, ideas, and experiences 

related to the integration of wind and hydropower technologies within electricity 

supply systems.

entitled, 

“Integration of Wind and Hydropower Systems.” The primary purposes of this task are to 

conduct cooperative research concerning the generation, transmission, and economics of 

integrating wind and hydropower systems, and to provide a forum for information exchange. The 

following are specific goals of Task 24:

Goal 2) As it pertains to wind and hydropower integration, share information among 

participating members concerning grid integration, transmission issues, hydrological 

and hydropower impacts, markets and economics, and simplified modeling 

techniques.

Goal 3) Through information sharing and exchange of ideas, identify technically and 

economically feasible system configurations for integrating wind and hydropower, 

including the effects of market structure on wind-hydro system economics with the 

intention of identifying the most effective market structures.

Goal 4) Document case studies pertaining to wind and hydropower integration, and create an 

on-line library of reports.

 1 It is worth noting here that the topics of the task were discussed and the objectives formed through conversations 

with the IEA Hydropower IA, and formation of a joint task (i.e., sponsored by both IAs) was seriously considered. 

Though a joint task did not materialize, the collaboration strengthened the work plan of the task and the robustness 

of the analysis and conclusions.
IEA Wind Task 24 Final Report, Vol. 2

v

Case studies that analyze the feasibility, benefits, detriments, and costs of specific wind-hydro 

integration projects were the mechanism through which the goals of the task were addressed and 

the feasibility of wind-hydro integration was investigated. Although specific wind-hydro 

integration projects may differ substantially, there are many characteristics common to each. 

Consequently, there was ample opportunity for each participant of the task to leverage one 

another’s case study projects to enhance their own findings, discuss difficulties faced in analysis 

and interpretation of results, and debate methods and conclusions. 

Volume 2 of the Task 24 final report is devoted to the case studies performed by the participants. 

Chapter 1 presents a uniform framework for describing the methodologies employed in the case 

studies and from which they will be compared. Furthermore, this chapter presents a few methods 

for defining wind penetration because it is an important metric when considering wind 

integration into a power system. Because of the significant differences in constraints, flexibility, 

and operating environment/market that frequently exist between different hydropower plants and 

systems, the thought process related to simplified modeling of the potential for wind/hydro 

integration evolved to characterizing the “flexibility” inherent in a given system; therefore, a 

portion of Chapter 1will be dedicated toward the topic of “system flexibility.” After this 

introductory chapter, there are six chapters devoted to the case studies performed on behalf of the 

member countries. As described in Volume 1 of the Task 24 final report, the case studies are 

each intended to address at least one of the following: (1) grid integration impacts; (2) 

hydropower impacts; and (3) economic impacts. The case studies, which are described below, 

each fall within one or more of these broad categories (as indicated by the text in brackets and 

italics at the end of each description below). 

Australia:

• Case Study 1: Large-Scale Wind Integration to the Tasmanian System (hydropower impacts 

study)

• Case Study 2: The Costs of Wind-Firming Service Provided by a Hydro Plant (hydropower 

impacts study)

• Case Study 3: Inertia Support in a Hydro, Wind, and High-Voltage Direct Current (HVDC)

Hybrid Power System (grid integration impacts study)

Canada:

• RETScreen2

• Hydro Quebec case study providing a summary of the impacts of a wind- and hydrodominated power system on the electricity markets and the characteristics of Nordic 

hydropower (grid integration impacts and economics study)

analysis for a case study involving the financial feasibility of wind integration 

by the Okanogan Public Utility District (PUD) in Washington State, United States

(economics study)

 2 See www.retscreen.net/ang/home.php; RETScreen stands for Renewable-energy and Energy efficient 

Technologies Screening tool, and was developed for Natural Resources Canada.
IEA Wind Task 24 Final Report, Vol. 2

vi

Finland:

• A case study focusing on the handling of wind power prediction errors for a single 

hydrothermal power producer in Finland (grid integration impacts study)

• A summary of the impacts of a wind- and hydro-dominated power system on the electricity 

markets and the characteristics of Nordic hydropower (grid integration impacts and 

economics study)

Norway:

• The first study looked at wind power in areas with limited power transfer capacity and 

subject to grid congestion. The question to be addressed here was to see how much wind 

power could be integrated without deleteriously affecting the hydropower production. (grid 

integration impacts study)

• The second case study considers the impact of wind power on system adequacy. Considering 

that the region has favorable wind resources, the study was conducted to determine whether 

or not adding wind power to the hydro-based system will be sufficient or if additional 

measures must be taken to secure system adequacy (grid integration impacts study)

Sweden:

• Case study for balancing of wind power integrated into the Swedish system using balancing 

resources located along just one river (hydropower impacts study)

• Case study for balancing large amounts of wind power integration by coordinating operation 

with hydropower in North Sweden (grid integration impacts study)

Unites States:

• Case studies are from three different river systems and electrical balancing areas, as listed 

below:

o Missouri River and the Western Area Power Administration (grid integration impacts 

study)

o Upper American River and the Sacramento PUD (grid integration impacts study)

o Columbia River and the Grant County Public Utility District (hydropower impacts study)

• The first and third of these studies focus on statistically estimating the impacts of wind 

integration on balancing area requirements for regulation (minute-to-minute) and load 

following. The Grant County study further estimates the impacts on system flow and 

reliability constraints caused by wind integration. The study performed for Sacramento PUD 

aimed at simulating the system and arrived at estimates of the wind integration impacts and 

costs.

The aforementioned case studies are presented in a uniform manner to aid in comparison and in 

discerning the most relevant characteristics and results of the studies. In this sense, the case study 

chapters are summaries of more complete and detailed reports (references provided), and focus 

on highlighting the results that pertain most directly to the objectives of Task 24. For further 

details about the case studies than are presented here, see the appropriate reference reports.
IEA Wind Task 24 Final Report, Vol. 2

vii

Table of Contents

Preface .......................................................................................................................................................... i

Acknowledgments ...................................................................................................................................... ii

List of Acronyms........................................................................................................................................ iii

Executive Summary................................................................................................................................... iv

1 Introduction........................................................................................................................................... 1

1.1 Study Methodologies...........................................................................................................2

1.2 Wind Penetration and System Flexibility ..........................................................................11

1.3 Report Organization...........................................................................................................18

2 Australia .............................................................................................................................................. 19

2.1 Introduction........................................................................................................................19

2.2 Hydro Tasmania Case Studies...........................................................................................19

2.2.1 Introduction to Studies..............................................................................................19

2.2.2 Overview of Power System ......................................................................................20

2.2.3 Case Study 1: Large-Scale Wind Integration............................................................21

2.2.4 Study Methodology...................................................................................................22

2.2.4.1 Assumptions..................................................................................................23

2.2.4.2 Limitations....................................................................................................23

2.2.5 Tasmanian System Characteristics ...........................................................................24

2.2.5.1 Frequency......................................................................................................24

2.2.5.2 Fault Level ....................................................................................................24

2.2.6 Wind Power Characteristics......................................................................................25

2.2.7 Wind Generator Dispatching Rules in Australia ......................................................25

2.2.8 Wind Power Penetration and System Flexibility......................................................25

2.2.9 Hydro System Characteristics...................................................................................26

2.2.10 Conclusions.............................................................................................................26

2.3 Case Study 2: Wind Firming – Case Study of Costs and Effects to the Hydro Tasmania 

System................................................................................................................................29

2.3.1 Study Methodology...................................................................................................29

2.3.1.1 Assumptions..................................................................................................30

2.3.1.2 Limitations....................................................................................................31

2.3.2 Impacts of Wind Generation.....................................................................................31

2.3.3 Hydro System Characteristics...................................................................................32

2.3.4 Wind Power Penetration and System Flexibility......................................................33

2.3.5 Conclusions...............................................................................................................35

2.3.5.1 Isolated Tasmanian System...........................................................................35

2.3.5.2 Tasmanian System Interconnected with Mainland.......................................35

2.4 Case Study 3: Inertia Support in a Hydro/Wind/HVDC Hybrid Power System ...............36

2.4.1 Study Methodology...................................................................................................36

2.4.2 Modeling Assumptions.............................................................................................37

2.4.3 Wind Power Characteristics......................................................................................38

2.4.4 Hydro System Characteristics...................................................................................38

2.4.5 Wind Power Penetration and Hydro System Flexibility...........................................38

2.4.6 Conclusions...............................................................................................................41
IEA Wind Task 24 Final Report, Vol. 2

viii

3 Canada................................................................................................................................................. 43

3.1 Introduction........................................................................................................................43

3.1.1 General Description of Study and Goals ..................................................................43

3.1.2 Organizations Involved and Who Conducted the Study...........................................43

3.1.3 Why the Study was Performed .................................................................................43

3.2 Intended Outcomes ............................................................................................................44

3.3 Overview of Power System ...............................................................................................45

3.3.1 Study Methodology...................................................................................................46

3.3.1.1 Assumptions..................................................................................................48

3.3.1.2 Limitations....................................................................................................48

3.3.2 Wind Power Characteristics......................................................................................48

3.3.3 Hydro System Characteristics...................................................................................49

3.3.4 Wind Power Penetration and System Flexibility......................................................50

3.3.5 Wind and Hydro Integration – Benefits and Impacts ...............................................50

3.4 Conclusions........................................................................................................................51

4 Finland................................................................................................................................................. 52

4.1 Introduction........................................................................................................................52

4.2 Case Study for a Single Power Producer...........................................................................52

4.2.1 Introduction to Study ................................................................................................52

4.2.2 Overview of Power System ......................................................................................52

4.2.3 Study Methodology...................................................................................................53

4.2.4 Wind Power Characteristics......................................................................................53

4.2.5 Hydro System Characteristics...................................................................................53

4.2.6 Wind Power Penetration and System Flexibility......................................................53

4.2.7 Wind and Hydro Integration – Benefits and Impacts ...............................................53

4.2.7.1 Step One: Balancing Cost from the Day-Ahead Market ..............................53

4.2.7.2 Step Two: Using Intra-Hour Trading (the Elbas Market operating in 

Nordic countries) ................................................................................................54

4.2.7.3 Step Three: Using Internal Balancing by Hydropower.................................54

4.2.7.4 Step four: Passive Internal Balancing Aggregating the Imbalances of 

Wind with other Imbalances of a Large Producer ..............................................55

4.2.8 Summary of Results..................................................................................................56

4.2.9 Conclusions...............................................................................................................57

4.3 Market Impacts for Nordic Countries................................................................................58

4.3.1 Introduction to Study ................................................................................................58

4.3.2 Overview of Power System ......................................................................................59

4.3.3 Study Methodology...................................................................................................59

4.3.3.1 Assumptions..................................................................................................60

4.3.3.2 Limitations....................................................................................................60

4.3.4 Wind Power Characteristics......................................................................................60

4.3.5 Hydro System Characteristics...................................................................................60

4.3.6 Wind Power Penetration and System Flexibility......................................................61

4.3.7 Wind and Hydro Integration – Benefits and Impacts ...............................................61

4.3.8 Conclusions...............................................................................................................62

4.4 Market Impacts for Nordic Countries................................................................................62

4.4.1 Introduction to Study ................................................................................................63

4.4.2 Overview of Power System ......................................................................................64
IEA Wind Task 24 Final Report, Vol. 2

ix

4.4.3 Study Methodology...................................................................................................64

4.4.3.1 Assumptions..................................................................................................65

4.4.3.2 Limitations....................................................................................................65

4.4.4 Wind Power Characteristics......................................................................................65

4.4.5 Hydro System Characteristics...................................................................................65

4.4.6 Wind Power Penetration and System Flexibility......................................................66

4.4.7 Wind and Hydro Integration – Benefits and Impacts ...............................................66

4.4.8 Conclusions...............................................................................................................67

5 Norway................................................................................................................................................. 69

5.1 Introduction........................................................................................................................69

5.2 SINTEF 1: Areas with Limited Power Transfer Capacity.................................................69

5.2.1 Introduction to Study ................................................................................................69

5.2.2 Overview of Power System ......................................................................................70

5.2.3 Study Methodology...................................................................................................71

5.2.3.1 Assumptions and Limitations .......................................................................74

5.2.3.2 Wind Power Characteristics..........................................................................74

5.2.4 Hydro System Characteristics...................................................................................74

5.2.5 Wind Power Penetration and System Flexibility......................................................75

5.2.6 Wind and Hydro Integration – Benefits and Impacts ...............................................75

5.2.7 Conclusions...............................................................................................................77

5.3 SINTEF 2: Regional Hydro-based Power System with Weak Interconnections...............77

5.3.1 Introduction to Study ................................................................................................77

5.3.2 Overview of Power System ......................................................................................78

5.3.3 Study Methodology...................................................................................................80

5.3.3.1 Energy Balance Calculations........................................................................80

5.3.3.2 Power Balance Calculations .........................................................................80

5.3.3.3 Assumptions and Limitations .......................................................................82

5.3.4 Wind Power Characteristics......................................................................................82

5.3.5 Hydro System Characteristics...................................................................................83

5.3.6 Wind Power Penetration and System Flexibility......................................................83

5.3.7 Wind and Hydro Integration – Benefits and Impacts ...............................................83

5.3.8 Conclusions...............................................................................................................85

6 Sweden ................................................................................................................................................ 86

6.1 Introduction........................................................................................................................86

6.2 Case Study for Balancing of Wind Power in One River ...................................................86

6.2.1 Introduction to Study ................................................................................................86

6.2.2 Overview of Power System ......................................................................................87

6.2.3 Study Methodology...................................................................................................87

6.2.4 Wind Power Characteristics......................................................................................88

6.2.5 Hydro System Characteristics...................................................................................89

6.2.6 Wind Power Penetration and System Flexibility......................................................89

6.2.7 Wind and Hydro Integration – Benefits and Impacts ...............................................89

6.2.8 Summary and Conclusions .......................................................................................90

6.3 Case Study for Balancing of Wind Power in North Sweden.............................................90

6.3.1 Introduction to Study ................................................................................................90

6.3.2 Overview of Power System ......................................................................................91

6.3.3 Study Methodology...................................................................................................91
IEA Wind Task 24 Final Report, Vol. 2

x

6.3.4 Wind Power Characteristics......................................................................................91

6.3.5 Hydro System Characteristics...................................................................................92

6.3.6 Wind Power Penetration and System Flexibility......................................................92

6.3.7 Wind and Hydro Integration – Benefits and Impacts ...............................................92

6.3.8 Summary and Conclusions .......................................................................................93

7 United States....................................................................................................................................... 94

7.1 Introduction........................................................................................................................94

7.2 Case Study: Western Area Power Administration, Missouri River...................................94

7.2.1 Introduction to Study ................................................................................................94

7.2.2 Overview of Power System ......................................................................................95

7.2.3 Study Methodology...................................................................................................96

7.2.3.1 Assumptions..................................................................................................97

7.2.3.2 Limitations....................................................................................................97

7.2.4 Wind Power Characteristics......................................................................................98

7.2.5 Hydro System Characteristics.................................................................................101

7.2.6 Wind Power Penetration and System Flexibility....................................................102

7.2.7 Wind and Hydro Integration – Benefits and Impacts .............................................102

7.2.7.1 Regulation and Load Following Statistics..................................................104

7.2.7.2 Morning and Evening Ramping..................................................................106

7.2.7.3 Wind Generation Forecast Error.................................................................108

7.2.8 Conclusions.............................................................................................................109

7.3 Case Study: Sacramento PUD, Upper American River...................................................110

7.3.1 Introduction to Study ..............................................................................................110

7.3.2 Overview of Power System ....................................................................................111

7.3.3 Study Methodology.................................................................................................112

7.3.3.1 Assumptions................................................................................................113

7.3.3.2 Limitations..................................................................................................113

7.3.3.3More Details About the Simulation .............................................................114

7.3.3.4 Hourly Simulation.......................................................................................114

7.3.4 Wind Power Characteristics....................................................................................116

7.3.5 Hydro System Characteristics.................................................................................117

7.3.6 Wind Power Penetration and System Flexibility....................................................117

7.3.7 Wind and Hydro Integration – Benefits and Impacts .............................................117

7.3.8 Conclusions.............................................................................................................120

7.4 Case Study: Grant County PUD, Columbia River...........................................................121

7.4.1 Introduction to Study ..............................................................................................121

7.4.2 Overview of Power System ....................................................................................122

7.4.3 Study Methodology.................................................................................................123

7.4.3.1 Assumptions................................................................................................124

7.4.3.2 Limitations..................................................................................................125

7.4.3.3 Wind Power Characteristics........................................................................125

7.4.4 Hydro System Characteristics.................................................................................127

7.4.5 Wind Power Penetration and System Flexibility....................................................130

7.4.6 Wind and Hydro Integration – Benefits and Impacts .............................................131

7.4.7 Conclusions.............................................................................................................135

8 References ........................................................................................................................................ 136

Appendix A: Hydro Quebec Case Study............................................................................................... 139
IEA Wind Task 24 Final Report, Vol. 2

xi

List of Figures

Figure 1. Illustration of the range of complexity that can be employed in wind 

integration studies ............................................................................................................................... 3

Figure 2. Generation request and wind power input to Grant PUD for January 2, 2004 

(Source: Acker 2007)............................................................................................................................ 4

Figure 3. Tabulation of the 1-minute “regulation” values for the Grant County PUD for various 

levels of wind penetration (= nameplate wind capacity / peak load) 

(Source: Northern Arizona University and Grant County PUD)....................................................... 5

Figure 4. Methodology often employed for conducting detailed wind integration studies 

(Source: C. Smith presentation, Task 24/25 Joint R&D Meeting, Milan 2007)................................ 7

Figure 5. Deducing integration costs by comparing the costs of operating two variations of the 

electrical system, one with wind and one with some other set of generation resources 

(Source: L. Söder presentation, Task 24/25 Joint R&D Meeting, Milan, Italy, May 2007; 

also Söder and Holttinen 2008)........................................................................................................... 8

Figure 6. Values for various measures of the wind penetration and peak load for several recent 

wind integration studies (Source: Adapted from A. Robitaille presentation, “Wind Power 

Integration Into Hydropower System: Discussion,” Task 24 R&D Meeting #5, Quebec City, 

Canada, June 2008, using data from Table 18 of Holttinen et al. 2008a)...................................... 15

Figure 7. Comparison of (peak load – minimum load) / (average system load) for several recent 

wind integration studies against corresponding peak system load and wind penetration 

computed as a percentage of (minimum load + interconnection capacity) (Source: Adapted 

from A. Robitaille presentation on “Wind Power Integration Into Hydropower System: 

Discussion,” Task 24 R&D Meeting #5, Quebec City, Canada, June 2008, using data from 

Table 18 of Holttinen et al. 2008a)..................................................................................................... 16

Figure 8. Annual distribution of the net hydro system yield and a mean wind speed; lower 

figure shows typical daily wind patterns ......................................................................................... 32

Figure 9. Increase in spill due to installation of wind generation; isolated operation of 

Tasmanian system ............................................................................................................................. 33

Figure 10. Increase in spill due to installation of wind generation; interconnected operation ........ 33

Figure 11. Costs (Australian dollars) per megawatt-hour of wind firming service ............................ 34

Figure 12. Average annual cost of firming up wind energy and reduced wind production costs ... 35

Figure 13. Fast FCAS raise requirements as a function of system stored energy and the size of 

largest generator contingency (all units in megawatts)................................................................. 38

Figure 14. FCAS R6, a function of system-stored rotating energy and system demand 

(all units in megawatts)...................................................................................................................... 39

Figure 15. Fast FCAS R6 requirements (vertical axis, in megawatts) as a function of system 

inertial energy (N-1) (horizontal axis, in megawatts). ..................................................................... 40

Figure 16. RETScreen outputs for risk analysis and net present value.............................................. 44

Figure 17. Correlation of wind power output and snow pack runoff, along with wind value 

and cost for the study period of 2003–2005 .................................................................................... 50

Figure 18. Example of how 400 MW of hydropower production could change when correcting 

errors on day-ahead forecasts of 400 MW of wind power.............................................................. 55

Figure 19. Costs for balancing day-ahead forecast errors for wind power (cases 200 MW and 

400 MW wind power); extra cost range is from using different assumptions for the cost 

of the balancing option...................................................................................................................... 56

Figure 20. Overview of the case study power system in which AGC is regarded for keeping 

the power transmission below the maximum export capacity of 420 MW ................................... 70

Figure 21. Left Graph: Standard deviation of the hourly wind power variations for individual 

wind farms and for the sum of generation for one to three wind farms; Right Graph: 

Cumulative probability distribution of wind power (i.e., the probability that the wind 

power is less than or equal to a specific value).............................................................................. 74

Figure 22. Left graph: Hydro reservoir level for the case with no wind power. Right graph: 

Annual variations in wind and hydro generation............................................................................ 75
IEA Wind Task 24 Final Report, Vol. 2

xii

Figure 23. Cumulative distribution function of wind power (Pw) and hydropower (Ph) for three 200-

MW wind farms ................................................................................................................................... 76

Figure 24. Annual income (wind+hydro) from energy sales to electricity market relative to the 

non-congested case (max, min, and avg plots refer to the 30-year samples used in 

this study)............................................................................................................................................ 77

Figure 25. Overview of the case study power system .......................................................................... 79

Figure 26. Cumulative distribution function of wind power for Case A .............................................. 83

Figure 27. Left graph: Average seasonal pattern for load, wind, and hydro inflow Right graph: 

Annual variations in load (due to temperature), wind, and hydro generation ............................. 84

Figure 28. Cumulative distribution function of the generating capacity margin................................ 84

Figure 29. Capacity value of wind power with and without geographical smoothing effect: (a) 

Capacity value in megawatts; (b) Capacity value in percentage of installed wind power; (c) 

Capacity value as a function of wind penetration level.................................................................. 85

Figure 30. An illustration of the seven hydro stations studied on the Ume River............................. 89

Figure 31. Mean export to Norway and Finland per week in comparison with maximal export 

capability; Y-axis: “Mean delivery” in megawatt-hours, X-axis: “Week” ..................................... 93

Figure 32. The general location of regions and sites for the MM5 meteorological simulation......... 99

Figure 33. The inner grid layout deployed for the MM5 meteorological simulation (Numbered 

black dots correspond to the “proxy” meteorological towers, and the large, magenta dot 

labeled “RNL PT” indicates the location where long-term climatological data was extracted 

for comparison of wind and hydropower resources) ..................................................................... 99

Figure 34. The 2003 WAPA system load plotted with the output from a 500-MW wind power 

plant ................................................................................................................................................... 100

Figure 35. The average daily profile for summer months for the 500-MW scenario........................ 101

Figure 36. The Missouri River drainage basin and the six dams and hydropower facilities 

operated by USACE (highlighted in red)........................................................................................ 101

Figure 37. Time series of monthly runoff and wind speed departures, 1964–2003 ......................... 104

Figure 38. Statistical distribution of 10-minute regulation for the 500-MW scenario ...................... 105

Figure 39. A sample of the actual WAPA load, load trend, and average hourly load ...................... 105

Figure 40. Hourly load changes during ramp periods for 500-MW wind scenario, morning (left) 

and evening (right) ........................................................................................................................... 107

Figure 41. Distribution of morning ramps for load and load net 500-MW wind generation............ 107

Figure 42. Time series plot of the load forecast error and the wind forecast error for the 

500-MW scenario .............................................................................................................................. 108

Figure 43. Distribution of load and load net wind day-ahead forecast errors for the 500-MW 

scenario............................................................................................................................................. 109

Figure 44. Integration cost summary by case set ............................................................................... 119

Figure 45. Integration cost summary by case (wind penetration level) ............................................ 120

Figure 46. Frequency of 1-minute wind power generation changes for data year 2006, as 

computed by finding the difference between the actual generation and a 10-minute rolling 

average .............................................................................................................................................. 126

Figure 47. Frequency of hourly generation changes found by comparing the wind generation 

at the top of each hour for data year 2006 ..................................................................................... 126

Figure 48. Illustration of the method to identify the magnitude and duration of wind power 

ramps ................................................................................................................................................. 127

Figure 49. Cumulative exceedence curve showing the proportion of wind power ramps that 

surpass a given ramp rate............................................................................................................... 127

Figure 50. Map of dams in the Columbia River Basin (Source: USACE, 

www.nwd.usace.army.mil/ps/home.asp)........................................................................................ 127

Figure 51. Variation of the maximum and minimum capacity (MW) available to Grant PUD 

at the Priest Rapids and Wanapum dams...................................................................................... 130

Figure 52. Average diurnal profile of Grant PUD system load (left) and generation request 

(right), for the study year 2006........................................................................................................ 131

Figure 53. Bar chart showing frequency of one-minute “regulation” values (MW) for the 

Grant PUD net load (load + purchases – sales – wind power)..................................................... 132

Figure 54. Bar chart showing frequency of hourly load following values (MW) for the 
IEA Wind Task 24 Final Report, Vol. 2

xiii

Grant PUD net load (load + purchases – sales – wind power)..................................................... 132

Figure 55. Number of exceedences of the 95% available capacity limit during the 2006 model 

year for the system as run and with 63.7 MW of wind planned into the system in the preschedule ............................................................................................................................................ 134

Figure 56. Number of exceedences (dropping below) the minimum allowable capacity due to a 

flow constraint for the system as run and with 63.7 MW of wind planned into the system in 

the pre-schedule............................................................................................................................... 134

Figure 57. Load and wind scheduling during a summer morning with high wind generation ....... 143

Figure 58. Daily maximum and minimum of hourly penetration and ramping rates........................ 146

Figure 59. Comparison of Hydro-Quebec and BPA supplemental reserves at 3,000-MW wind 

generation (up-regulation)............................................................................................................... 147

Figure 60. Annual values of supplemental AGC and imbalance based on the BPA approach ...... 148

Figure 61. Seasonal assessment of the n-sigma increase of frequency regulation reserves in 

percent of rated installed wind generation.................................................................................... 149

Figure 62. Qualitative illustration of the various components used in the risk calculation ........... 152

Figure 63. Qualitative illustration of the risk and additional balancing reserves for two different 

wind generation penetration levels ................................................................................................ 153

Figure 64. Risk encountered as a function of lead time for various levels of balancing reserves 154

Figure 65. Balancing reserves required as a function of lead time for various levels of risk ........ 154

Figure 66. Risk with and without wind generation, and added balancing reserves to maintain 

the same risk..................................................................................................................................... 155
IEA Wind Task 24 Final Report, Vol. 2

xiv

List of Tables

Table 1. Study themes originally identified to be addressed by Task 24 member countries ............. 1

Table 2. First half of the Task 24 matrix that defines important power system characteristics 

and some basic parameters of the setup for an integration study ............................................... 10

Table 3. Second half of the Task 24 matrix that lists variations of underlying assumptions and 

modeling approaches used in wind integration studies ................................................................ 10

Table 4. Relevant methods of defining “wind penetration” (Source: Adapted from A. Robitaille 

presentation, “Wind Power Integration Into Hydropower System: Discussion,” Task 24 R&D 

Meeting #5, Quebec City, Canada, June 2008) ................................................................................ 14

Table 5. Summary of issues surrounding wind integration and system flexibility (Source: A. 

Robitaille presentation, “Wind Power Integration Into Hydropower System: Discussion,” 

Task 24 R&D Meeting #5, Quebec City, Canada, June 2008)......................................................... 17

Table 6. First half of the Task 24 matrix that defines important power system characteristics 

and some basic parameters of the setup for the Tasmanian case studies.................................. 20

Table 7. Second half of the Task 24 matrix that lists variations of underlying assumptions and 

modeling approaches used in the first Tasmanian wind integration studies.............................. 22

Table 8. First half of the Task 24 matrix for the second Tasmanian case study that differs from 

the first Tasmanian case study......................................................................................................... 29

Table 9. Second half of the Task 24 matrix that lists variations of underlying assumptions and 

modeling approaches used in the second Tasmanian wind integration studies ........................ 29

Table 10. First half of the Task 24 matrix for the second Tasmanian case study that differs from 

the first Tasmanian case study......................................................................................................... 36

Table 11. Second half of the Task 24 matrix that lists variations of underlying assumptions and 

modeling approaches used in the third Tasmanian wind integration studies............................. 36

Table 12. First half of the Task 24 matrix that defines important power system characteristics 

and some basic parameters of the setup for the NRCanada case study ..................................... 45

Table 13. Second half of the Task 24 matrix that defines important power system characteristics 

and some basic parameters of the setup for the NRCanada case study ..................................... 47

Table 14. RETScreen outputs for estimated wind power...................................................................... 49

Table 15. First half of the Task 24 matrix that defines important power system characteristics

and some basic parameters of the setup for an integration study ............................................... 59

Table 16. Second half of the Task 24 matrix that lists variations of underlying assumptions and 

modeling approaches used in wind integration studies ................................................................ 59

Table 17. Top: Yearly mean production (GWh) from hydropower in Norway (NO) and Sweden 

(SE) divided into three groups that have clearly different operation principles; Bottom:

Characteristics of hydropower plants in the third group, which have reservoir capacity 

upstream.............................................................................................................................................. 61

Table 18. First half of the Task 24 matrix that defines important power system characteristics 

and some basic parameters of the setup for the second VTT (Finland) integration study ........ 64

Table 19. Second half of the Task 24 matrix that lists variations of underlying assumptions and 

modeling approaches used in the second VTT (Finland) case study........................................... 64

Table 20. Top: Yearly mean production (GWh) from hydropower in Norway (NO) and Sweden 

(SE) divided into three groups that have clearly different operation principles; Bottom:

Characteristics of hydropower plants in the third group, which have reservoir capacity 

upstream.............................................................................................................................................. 66

Table 21. First half of the Task 24 matrix that defines important power system characteristics 

and some basic parameters of the setup for the first SINTEF (Norway) integration study........ 70

Table 22. Second half of the Task 24 matrix that lists variations of underlying assumptions and 

modeling approaches used in the first SINTEF (Norway) wind integration studies ................... 72

Table 23. First half of Task 24 matrix defines important power system characteristics 

and basic parameters of the setup used in the second SINTEF (Norway) integration study .... 78

Table 24. Case study annual load and generation ................................................................................ 79

Table 25. Case study max load and generating capacity ..................................................................... 79

Table 26. Unit sizes and availability factors........................................................................................... 79
IEA Wind Task 24 Final Report, Vol. 2

xv

Table 27. Second half of the Task 24 matrix that lists variations of underlying assumptions and 

modeling approaches used in the second (Norway) wind integration studies ........................... 81

Table 28. First half of the Task 24 matrix that defines important power system characteristics 

and some basic parameters of the setup for the first Swedish case studies .............................. 87

Table 29. Second half of the Task 24 matrix that lists variations of underlying assumptions and 

modeling approaches used in the first Swedish case study......................................................... 88

Table 30. Mean hydropower efficiencies with 0–90 MW of wind power.............................................. 90

Table 31. First half of the Task 24 matrix that defines important power system characteristics 

and some basic parameters of the setup for the second Swedish case studies ........................ 91

Table 32. First half of the Task 24 matrix that defines important power system characteristics 

and some basic parameters of the setup for U.S. WAPA Missouri River integration study ...... 95

Table 33. Second half of the Task 24 matrix that lists variations of underlying assumptions and 

modeling approaches used in the U.S. WAPA Missouri River wind integration study............... 96

Table 34. The study cases and wind-generation scenarios ............................................................... 100

Table 35. Incremental 10-minute regulation requirements for wind generation .............................. 106

Table 36. Summary of statistics: variation of 2-hour rolling average from average hourly 

value................................................................................................................................................... 106

Table 37. First half of the Task 24 matrix that defines important power system characteristics 

and some basic parameters of the setup for U.S. SMUD wind integration study ..................... 111

Table 38. Second half of the Task 24 matrix that lists variations of underlying assumptions and 

modeling approaches used in the U.S. SMUD wind integration study....................................... 112

Table 39. Wind turbine population and capacity by case................................................................... 117

Table 40. Wind generation increase figures......................................................................................... 118

Table 41. Operating reserve requirement hourly variability............................................................... 118

Table 42. Integration costs ($ USD/MWh) for the four cases.............................................................. 119

Table 43. First half of the Task 24 matrix that defines important power system characteristics and 

some basic parameters of the setup for the U.S. Grant PUD wind integration study............... 122

Table 44. Second half of the Task 24 matrix that lists variations of underlying assumptions and 

modeling approaches used in the U.S. Grant PUD wind integration study ............................... 124

Table 45. First half of the Task 24 matrix that defines important power system characteristics and 

some basic parameters of the setup for the Hydro-Québec case studies ................................. 140

Table 46. Second half of the Task 24 matrix that lists variations of underlying assumptions and 

modeling approaches used in the first Hydro-Quebec (Canada) wind integration studies ..... 141

Table 47. Summary statistics of minute/minute data from: November 1, 1995 through 

October 31, 2006............................................................................................................................... 145

Table 48. Frequency regulation reserves requirements for 3,000-MW wind generation 

integration in the Quebec interconnection.................................................................................... 150

Table 49. Results for every month over the day-ahead horizon ........................................................ 156
IEA Wind Task 24 Final Report, Vol. 2

1

1 Introduction

Volume 1 of IEA Wind Task 24: Integration of Wind and Hydropower Systems presented

foundational information recounting the operational issues electric utilities, or Transmission 

System Operators (TSOs), face when balancing load with generation, and it described what is 

meant by “wind and hydropower integration” (Chapter 1). Volume 1 also discussed the 

characteristics of wind energy (Chapter 2) and hydropower (Chapter 3) relevant to wind and 

hydropower generation. Volume 1, Chapter 4, addressed the questions posed in the formation of 

Task 24 regarding wind-hydro integration and referenced Task 24 participant case studies. The 

purpose of Task 24, Volume 2, is to describe in more detail the case study reports performed by 

participants.

Each member country of Task 24 agreed to perform and submit at least one case study 

addressing some or all of the study themes defined in the Task 24 proposal and work plan, which 

are as follows: grid integration, hydropower impacts, market and economics, and simplified 

modeling of wind-hydro integration potential. As is the case with many IEA tasks, not every 

member country was either interested in or able to address all study themes. However, one of the 

great strengths of the IEA implementing agreements is their ability to bring together the research 

capabilities and interests of many countries in a collaborative environment, more thoroughly 

address an important and broad topic, and take advantage of the unique and diverse expertise 

possessed by the member countries. Table 1 summarizes the Task 24 case study themes that were 

to be addressed by each member country’s case studies. 

Table 1. Study themes originally identified to be addressed by Task 24 member countries

Country Effort 

(man-months/year)

Grid Integration 

Case Studies

Hydrologic Case 

Studies

Economic and 

Market Case Studies

Simplified Modeling

 Comments

Australia 6 x x x x Three to five studies

Canada 4.5 x x x Two studies plus RETScreen

Finland 4-6 x x x Three studies

Norway 6 x x x x Two studies 

Sweden 2-4 x x Four studies

Switzerland 3-4 x x Two to four studies

USA 6-12 x x x x Three or more studies

Early on in the formation of Task 24, the participants recognized that even though the electricity

grids operated by the member countries were all tasked with the same objective of balancing 

load and generation while maintaining system reliability, they differed in many ways—some 

trivial and some significant. Additionally, the vernacular used to describe electrical system 

operation and balancing differ in subtle yet important ways, and the case studies themselves

differ in many important areas, including the base assumptions upon which the studies were 

built, the types of study tools employed, and the study outcomes desired. Therefore, prior to 

delving into the case study reports, readers must understand these differences so that the results 
IEA Wind Task 24 Final Report, Vol. 2

2

presented in each case study can be understood in the correct context and in comparison to the 

body of case studies. 

The remaining portions of this introductory chapter are devoted to presenting a uniform 

framework for describing the study methodologies employed in the case studies and from which 

they will be compared. Furthermore, this chapter presents a few methods for defining wind 

penetration since it is an important metric when considering wind integration into a power 

system. As listed in Table 1, four of the member countries originally intended to perform 

simplified modeling of potential for wind-hydro integration within their given electrical 

systems—however, as the work of the case studies progressed, it was decided that trying to 

model the potential for wind-hydro integration of a given system (i.e., predicting how much wind 

power could be reasonably integrated/balanced using the hydro resources available in a given 

system) was not necessarily a practical problem to solve. Due to the significant differences 

among constraints, flexibility, and operating environment/market that may exist between 

different hydropower plants and hydro systems engaged in different grid systems and electrical 

markets, it did not appear fruitful to try to construct a general, simplified model. Rather, the 

thought process related to this question evolved to characterizing the “flexibility” inherent in a 

given system; therefore, some chapters will be dedicated toward this topic.

1.1 Study Methodologies

As mentioned, power system studies considering the impacts from and influences of wind power 

may differ in many ways, some trivial and some significant, beginning with the complexity and 

detail of the study method and its assumptions to the questions the study intends to answer. This 

issue led to much useful discussion and debate among the Task 24 participants concerning how 

to frame the study objectives, contrast the methodologies employed, and compare the results 

obtained. The result of these discussions is presented below.

In regard to overall scope, wind integration studies can vary from simple to detailed to 

evolutionary, as illustrated in Figure 1. A simple study typically looks only at the effect of wind 

power on system net load, and in particular the variability of that net load (net load will be 

defined shortly). A detailed study involves simulation of the system operation in order to deduce 

the impacts and costs of wind integration. And an evolutionary study simulates the system like a 

detailed study does, but goes further to consider the overall value of the wind energy (i.e., goes 

beyond addressing the impact of wind energy’s variability and uncertainty), and possibly 

considers changes in how the system is constituted, constrained, and operated, in search of better 

ways to incorporate wind into the power system. It is worth noting that studies are also 

conducted to determine dynamical effects on the electrical grid, for network stability, etc.; these 

types of studies are not included as part of Task 24 and were addressed in Task 21 (Dynamic 

Models of Wind Farms for Power System Studies).
IEA Wind Task 24 Final Report, Vol. 2

3

Simple Statistical evaluation of wind power impact on load variability and 

forecast uncertainty. Provides a basic indicator of how much 

additional flexibility may be needed to accommodate the additional 

variability, and uncertainty wind power adds to the system load 

variability and uncertainty. This type of study is conducted most 

often to determine impacts on time scales for the operational 

reserves: seconds, minutes, hourly data.

Detailed System planning and commitment operations are simulated in a 

detailed production cost-type model, including interaction with 

neighboring systems via the electricity market. The results of this 

type of study provide estimates of wind “integration costs” due to 

increased need for ancillary services. These studies are performed 

for operational impacts at time scales of 15-minute, hourly, daily, 

weekly, or monthly, and typically cover a year or more of system 

operation.

Evolutionary A detailed study as described above, but including comparisons of 

the power system simulated with wind power versus with some 

other set of generation resources, in order to deduce the overall 

value (positive or negative) of wind energy in the power system. 

This value incorporates the wind integration costs due to increased 

ancillary services as done in a detailed study, but also considers 

wind energy’s overall value in the electric power system. It is 

considered “evolutionary” since it compares different approaches to 

evolving the future electrical power system, market structures, 

operating rules, etc. 

Figure 1. Illustration of the range of complexity that can be employed in wind integration studies

Wind power is typically absorbed into the power system when it is generated, just as the load is 

served at the time it is required; therefore, it is convenient to look at the system’s load net wind

(i.e., the system load with the wind power subtracted), also referred to as net load. The remaining 

power system must balance load net wind via unit commitment, load following, and regulation. 

In essence, wind power is treated similar to negative load.

An example of how a load signal may differ from a load net wind signal is shown in Figure 2, 

which presents the wind power input at a hydropower utility in the United States (the Grant

County PUD, No. 2, in Washington State, U.S.) and the generation request for a single day 

(Acker 2007). The uppermost of the two dark blue lines represents the actual generation requests

made on this day (1-minute data), corresponding to 12 MW of wind power absorbed into the 

balancing area. The lower dark blue line represents the actual wind power input to the system on 

this day (and refers to the scale on the right-hand side of the plot). If wind power were not 

coming into the system, then the red line would have represented the generation request by Grant

PUD. In this case, the request would have been increased in the morning by the amount of wind 

power no longer coming into the system, but the evening request would be the same, since there 

was no wind production during the evening. Furthermore, the minute-to-minute variations do not 
IEA Wind Task 24 Final Report, Vol. 2

4

differ much between these lines (i.e., the minute-to-minute regulation), nor do the hourly 

variations. Projecting the impact of wind generation beyond the 12-MW wind input to the 

system, the uppermost yellow line shows what the generation request would have been if the full 

63.7-MW output of the Nine Canyon wind power plant were coming into the balancing area, and 

the lower yellow line shows the wind input in this scenario. Comparing the generation requests 

of 0 MW and 63.7 MW of wind input, one can see that on this particular day, the wind input 

would create a noticeable effect in the minute-to-minute variations and in the hourly variations, 

but the magnitude of the ramps during the ramping periods is unchanged. Additionally, the peak 

generation request for the day is essentially unchanged, but the minimum request during the 

early morning hours is reduced somewhat. During this day, the maximum instantaneous 

penetration of wind power (= wind power capacity / maximum generation request) is 4.3% when 

incorporating 12 MW of wind, and 28.3% when incorporating 63.7 MW of wind. An important 

point to take away from this plot is how the load is influenced by the wind to create the load net 

wind, and how the load net wind is inherently more variable than the load alone. Furthermore, 

the load variability by itself is significant and must be considered when analyzing the influence 

of wind power. One last point of importance related to this figure: the wind power is coming 

from one plant, and the system load is relatively small; in larger systems with wind power 

coming from multiple wind power plants, the relative impact of the wind power on the net load is 

smaller. 

GCPD Limited Request (1-min data)

With and Without Wind

January 2, 2004

0

50

100

150

200

250

300

350

400

450

500

0:00 4:00 8:00 12:00 16:00 20:00

Time of day

MW request

-20

0

20

40

60

80

100

120

140

160

180

Wind Input (MW)

Generation request with 12 MW wind (actual)

Generation request with 63.7 MW of wind

Generation request with 0 MW wind

Nine Canyon wind output - 63.7 MW max

Nine Canyon wind output - 12 MW max

Figure 2. Generation request and wind power input to Grant PUD for January 2, 2004

(Source: Acker 2007)
IEA Wind Task 24 Final Report, Vol. 2

5

Useful information can be deduced about the general effect of wind power’s variability on the 

overall variability that a system operator must manage by comparing statistics resulting from the 

load net wind time series versus the variability of the load time series alone. Such a statistical 

study that does not include a chronological simulation of the system would be classified as a 

simple study in Figure 1. Following the example presented in Figure 2, if one tabulates the 

“regulation” burden for Grant County PUD, computed here as the difference in the actual 

1 minute load (or load net wind) data from a suitable rolling average, in this case a 10-minute 

rolling average, a histogram as shown in Figure 3 results (using 11 months of data from 2006). In

Figure 3, the dark blue, light blue, yellow, and red bars correspond to 0, 12, 63.7, and 150 MW 

of wind power input into the system, respectively. The distribution shape is close to normal and 

effectively centered around zero, and the effect on regulation of including wind energy at these 

levels is very small. The standard deviation in 1-minute regulation generation changes ranges 

from 3.47 MW for 0 MW of wind, to 3.51 MW for 63.7 MW of wind, and up to 3.72 MW for 

150 MW of wind. One method to estimate the increase in need for regulation would be to 

multiply the difference in the standard deviation between these two cases by 5 (see Holttinen et 

al. 2008). For example, at 63.7 MW of wind, the additional spinning reserve required to 

compensate for the increased minute-to-minute regulation during every hour of the year would 

be 0.2 MW (e.g., 3.51 – 3.47 = 0.04 MW; 0.04 × 5 = 0.20 MW). Thus, hydro generation 

resources at Grant County PUD can likely absorb the minute-to-minute variations introduced in 

the system by wind power from the Nine Canyon Wind Project. A similar histogram could be 

constructed for “load following” to demonstrate the changes in load variation within an hour or 

hour-to-hour due to the load net wind as compared to the load alone. While these calculations do 

a reasonable job of indicating the increase in regulation or load following needed, and provide a 

distribution of expected hourly changes, they are completely decoupled from the actual system 

operation and do not provide insight into any specific problems that may arise.

Wind Reg.

Wind Penetration Std.Dev.

(MW) (%) (MW)

0 0.0% 3.47

12 1.8% 3.47

63.7 7.8% 3.51

150 18.6% 3.72

2006 Data; 480,960 min

GCPD LimitedRequest

10 minute rolling ave

1

1 2

2

−

+ = − ∑

+

= −

n t

n i t

t t i x

n

R x

Figure 3. Tabulation of the 1-minute “regulation” values for the Grant County PUD for various 

levels of wind penetration (= nameplate wind capacity / peak load) 

(Source: Northern Arizona University and Grant County PUD) 
IEA Wind Task 24 Final Report, Vol. 2

6

In other words, during any given minute or hour of system operation, a large wind ramp-up or 

cut-out could cause problems in balancing load and generation, if, for example, there were not 

sufficient flexible generation resources online or rapidly available through the electric market to 

compensate for an unforeseen change the wind output causes in system net imbalance.

Therefore, although statistical summaries of wind power’s influence on the net load are useful 

and supply valuable insights, they provide only part of the information required to understand 

wind’s impact. Furthermore, a significant fraction of the costs incurred in handling the variability 

of wind power is due to the uncertainty in forecasting the wind, resulting in increased costs due 

to sub-optimal unit commitment and the need to retain more reserves (Smith et al., 2007). The 

unit commitment impacts and costs are not captured in the statistics of load net wind. 

Typically, to complement and enhance the information provided by a simple study, wind 

integration studies often employ a chronological simulation of system operation. Many of the 

recent integration studies, including many of those in the case study chapters to follow, would be 

classified as detailed studies. In regard to detailed integration study methods, best practice 

suggests simulating system operation at some fine time resolution (1-hour time steps or less, if 

possible) using coincident wind and load data (Smith et al. 2007). In such a simulation, system

planning and commitment operations are simulated using a production cost model that includes 

interaction with neighboring systems via the electricity market. The load data in these studies are 

typically historical, often scaled to some future load level, and the wind power data is typically 

created using a meso-scale weather model to “backcast” what a hypothetical set of wind power 

plants would have generated during the same year(s) as the load data. With this method, any 

innate correlation between the load and the wind is preserved. The data, along with coincident

load and wind power forecast data (or simplifying assumptions to approximate them) is then 

used to drive a deterministic (or a stochastic) production cost simulation of the power system. 

The simulation proceeds to compute the cost to operate the system over some time frame, most 

typically 1 year, by mimicking the decision processes made day-ahead and hour-ahead and then 

deducing the impact of wind integration by comparing some base case scenario to cases that 

include increased levels of wind energy. In performing the simulation, the modeling platform 

(e.g., production cost simulation software) is constrained to honor all system reliability 

requirements and possibly include transmission limitations, while optimizing some performance 

criteria, such as minimizing the operating costs. A block diagram illustrating a variant of this 

methodology employed in detailed wind integration studies is shown in Figure 4. The results of 

this type of study provide, among other things, estimates of wind “integration costs” caused by 

an increased need for reserves and balancing resources due to the ancillary services of regulation, 

load following, and unit commitment.

In the methodology used for a detailed study described in Figure 1, the wind integration cost is 

defined as the difference in cost to operate the system with increased variability and uncertainty

due to wind power versus the cost to operate the system in some fashion without those 

influences. Often, this deduction of the integration cost is made through a system comparison in 

which the wind power is replaced by some other generation resource. In some cases, this 

replaced resource is defined as a benign generation resource possessing none of the variability 

and uncertainty of wind energy, but available at the same cost as wind energy and in the same 

quantities. (This is the comparison suggested by the “flat” profile in the block diagram of 

Figure 4).
IEA Wind Task 24 Final Report, Vol. 2

7

Figure 4. Methodology often employed for conducting detailed wind integration studies

(Source: C. Smith presentation, Task 24/25 Joint R&D Meeting, Milan 2007)

This technique provides useful information about the integration costs due to variability and 

uncertainty of wind power, and avoids the difficulty of accurately predicting wind power 

costs/prices and any complex market interactions. However, it is a somewhat limited view of 

wind’s impact in a power system, and an improvement would be to consider wind power’s 

overall influence in the power system, including the value it may bring to the system as a 

relatively low-cost form of new power generation. 

Söder and Holttinen (2008), pursuant to discussions held as part of the Task 24 dialogue, 

published an article describing different ways of setting up and performing a wind integration 

study in order to provide a consistent framework to formulate an integration study as well as 

interpret and compare results from the various studies that have been performed. Adapted from 

their paper, Figure 5 provides an illustration of two systems (that is, balancing areas) that are 

otherwise identical except that System 2 possesses some amount of wind power while System 1 

includes some set of “other power” generation resources that would be “replaced” by the wind 

power. The other power sources in the replaced system could be thermal generators, hydro 

generators, nuclear systems, etc., existing on the system or they could be new, but in either case 

would be some realistic set of resources that could be employed.

The “remaining system” shown in both Systems 1 and 2 represents the remaining power system 

and is identical in both systems, as are neighboring systems A and B and the interties with them. 

Depending on how the replaced system is defined, and the assumptions employed in modeling 

both Systems 1 and 2, the study could be classified as either detailed or as evolutionary, as 
IEA Wind Task 24 Final Report, Vol. 2

8

presented in Figure 1. The essential difference between these classifications, as defined here, is 

that a detailed study will mimic system operation in a way to deduce the operating cost 

increment associated with wind energy’s variability and uncertainty, whereas an evolutionary 

study will consider the “integration cost” as the difference in operating costs (O) for the two 

systems, assuming each is run optimally given its technical and economical specifications, 

including any investments (I) that might be made that reduce the overall operating cost. Thus,

Integration cost = (Owind – Oreplaced) – (Iwind – Ireplaced)

This simple equation applies for any type of integration cost study. However, for an

evolutionary study, modeling of the system need not be conducted given the existing 

transmission constraints, market rules, scheduling intervals, etc., of the electrical system as 

presently configured, but rather can consider changes intended to evolve the system to a more 

efficient, profitable, and/or lower cost realm of operation. 

Figure 5. Deducing integration costs by comparing the costs of operating two variations of the 

electrical system, one with wind and one with some other set of generation resources

(Source: L. Söder presentation, Task 24/25 Joint R&D Meeting, Milan, Italy, May 2007; also Söder 

and Holttinen (2008)]

Whether a study is considered detailed, evolutionary, or something in between depends on the 

specific questions to be answered by the study and the assumptions and methods employed.

Furthermore, the composition of the replaced system can have great bearing on the results 

obtained. As such, Söder and Holttinen go on to present several questions whose answers are 

significant in determining integration costs; these questions are summarized as follows (with the 

parenthetical capital letters and their associated category terms later referenced in Table 2 and 

Table 3):

1. Is the aim to study the consequence of a certain amount of wind power and/or to find the 

limit of what is possible? (A = Aim) 

2. What method is used for considering wind in the system (i.e., as suggested in the detailed 

to evolutionary studies already described)? For example, is wind power seen as an extra 

source that is added to an existing system or is wind power seen as one expansion 

alternative that is compared with another one? (M = Methods) 

Neighboring 

System A

Neighboring 

System B

Replaced 

system

Remaining 

system

Neighboring 

System A

Neighboring 

System B

New 

power source

Remaining 

system

Wind Power

Other Power

System 1

System 2
IEA Wind Task 24 Final Report, Vol. 2

9

3. How is the system simulated (e.g., deterministically or with stochastic optimization)? (S 

= Simulation)

4. What is the time resolution of the model (e.g. minute, hour, day)? (R= Time Resolution)

5. Is the integration cost calculated as the “physical cost” (i.e., fuel and investment costs) or 

is it calculated as the “market cost” (i.e., what different actors can be assumed to get 

paid)? Is it assumed that the balancing power is traded on the cost level or on the value 

level? The formal problem is that wind power owners need balancing (i.e., are ready to 

pay a lot), while sellers of regulating power want to maximize their profit. What is then 

assumed about the pricing of this service? (P = Pricing)

6. When wind power is expanded, is it assumed that the rest of the system, including the 

grid, is optimized from the economical point of view? Is the system designed for optimal 

operation with wind or other power sources, or does it operate under existing conditions? 

Is it assumed that the trading rules between neighboring systems are optimal from the 

total system economic point of view or are existing rules used? (D = Design)

7. How is the imbalance in the power system calculated? Are wind speed forecasts included 

in the studies? Is it assumed that the accuracy of these is on the best available level? Are 

imbalances in the load and other production units taken into account when determining 

the reserve requirements? (I = Imbalance)

8. How detailed is the description of the power system especially in its flexibility? For 

example, what balancing resources are considered (B = Balancing), and how are grid

transmission and interconnection limits handled (G = Grid)?

9. How are uncertainties in the system modeled (e.g., uncertainties in hydropower, 

transmission limits, wind power, load, thermal generation, and forecasting)? (U = 

Uncertainties)

10. How are the hydropower plants modeled (e.g., are head height and coupling of facilities 

on a river system considered)? (H = Hydropower Plant)

11. In addition to modeling of hydropower plants, does the hydropower provide any type of 

special capacity service for the wind energy (e.g., store excess wind in off-peak times for 

redelivery during peak hours)? (HC = Hydropower Capacity Service)

12. How are the thermal power plants modeled (e.g., are ramp rates and stop/start time 

considered)? (T = Thermal Power Plant)

13. And finally, how is the wind power output modeled? (W = Wind Power Plant) 

For the purpose of comparing and contrasting the various wind/hydro integration studies 

developed by Task 24 participants, as well as other wind integration studies that have been 

conducted, all of these issues were summarized along with other relevant system information 

onto a form called the matrix. The matrix is split into two parts and displayed in Table 2 and 

Table 3 because it is too long to fit on a single page. Table 2 shows power system and market 

characteristics, and parameters describing the system setup and simulation detail. (The capital 

letters in the left-hand column under Setup and Simulation Detail in Table 2 and Uncertainty and 

Balancing and Power System Detail in Table 3 correspond to the categories defined by these 

letters in the previous bulleted list.) For each issue listed on the left, the corresponding rows on 

the right-hand side of the table present options describing the possible approaches or 
IEA Wind Task 24 Final Report, Vol. 2

10

assumptions that may be employed. More than one of these options, or none of these options, 

may apply in any given study. For more options descriptions, please see the related paper by 

Söder and Holttinen (2008).

Table 2. First half of the Task 24 matrix that defines important power system characteristics and 

some basic parameters of the setup for an integration study

Geographic area of study:

Power system characteristics: 

Load Conventional 

Generation Interconnection Wind Power

Peak (MW) Min (MW) TWh/a Capacity (MW) Capacity (MW) MW TWh/a

Other relevant characteristics of power system:

Characteristics of system planning:

Description of market:

Integration time frames of importance: 

Yes/No Time Frame

Frequency, system dynamics, grid stability, LVRT, PSS, V-Reg

Regulation, AGC

Load following; intrahour ramping; economic dispatch

Unit commitment and day-ahead scheduling; economic utilization of resources

Resource and capacity planning; reliability

Table 3. Second half of the Task 24 matrix that lists variations of underlying assumptions and 

modeling approaches used in wind integration studies

Setup

A Aim of Study 1 what happens with x GWh wind

2 how much wind is possible

M Method to Perform Study 1 add wind energy

2 wind also replaces capacity

3 optimal system design

S Simulation Model of 

Operation 

1 deterministic simulation, one case

2 deterministic simulation several cases

3 Stochastic simulation several cases

Simulation Detail

R Resolution of Time 1 day/week

2 hour

3 minute/second

P Pricing Method 1 costs of fuels, etc.

2 prices for trading with neighbors

3 market actor simulation

4 market dynamics included

D Design of Remaining 

System 

1 constant remaining system

2 optimized remaining production

3 optimized remaining transmission

4 perfect trading rules

Uncertainty and Balancing
IEA Wind Task 24 Final Report, Vol. 2

11

I Imbalance Calculation 1 only wind

2 wind+load

3 wind+load+ production

B Balancing Location 1 dedicated source

2 from the same region

3 also outside region

U Uncertainty Treatment 1 transmission margins

2 hydro inflow uncertainty

3 no wind forecasts (assume persistence)

4 best possible wind forecasts

5 load forecasts considered

6 thermal power outages considered

Power System Details

G Grid Limit on Transmission 1 no limits

2 constant MW limits

3 consider voltage

4 N-1 criteria

5 dynamic simulation

H Hydropower Modeling 1 head height considered

2 hydrological coupling included (including reservoir capacity)

3 hydrological restrictions included

4 hydro optimization considered

5 consider most relevant aspects of affected hydro resources

HC Hydro Capacity Service 1 interaction with hydro resources not significant

2 real-time integration with impact of wind variability on system 

managed with hydro

3 capacity service: addition of capacity value through redelivery 

of wind energy at a later time

T Thermal Power Modeling 1 ramp rates considered

2 start/stop costs considered

3 efficiency variation considered

4 heat production considered

W Wind Power Modeling 1 few wind speed time series 

2 many wind power time series

3 time series smoothing considered

This matrix, as presented in Table 2 and Table 3, was used as an organizational tool in describing 

the case studies performed as part of Task 24 and for aiding in comparing the various studies. It

is used prominently in the case study chapters that follow in this volume.

1.2 Wind Penetration and System Flexibility

Before proceeding to the case study chapters, it is useful to discuss what is meant by “the 

penetration level of wind energy.” When wind integration costs or impacts are presented, “wind 

penetration” is almost universally cited to provide an indication of how much wind power is 

being assimilated into the balancing area or system for which the integration impacts and costs 

are incurred. There is usually some implication that systems of a similar wind penetration should 

experience similar wind integration impacts, but this is not necessarily the case. Perhaps the two 

most frequently cited definitions of wind penetration are given by the following simple 

expressions, based upon either capacity and load or energy:

Wind Penetration (WP) = (installed wind power capacity) / (system peak load)

Wind penetration (WP) = (annual wind power production) / (annual system demand, energy)
IEA Wind Task 24 Final Report, Vol. 2

12

These expressions correspond to WPC and WPA shown in Table 4, respectively. The system peak 

load in the denominator of the former equation can correspond to the peak indigenous load of the 

system, or the peak of the indigenous load plus synchronous exchanges with neighboring 

systems (in other words, it includes load obligations exchanged between neighboring systems 

based upon purchases or sales into or out of the system). For the case in which the calculation is 

carried out using the peak of the indigenous system load, it is easy to deduce the wind capacity 

knowing the system peak load, a useful number for wind power political targets. It is generally 

more useful to the utility system operator and planner when WP is calculated with the peak of the 

indigenous load plus synchronous exchanges.

Similarly, the wind penetration calculation represented by WPD in Table 4, which uses the 

system minimum load including synchronous exchanges in the denominator, is also useful for 

system planners/operators. The reason for this is that these latter two methods of computing the 

wind penetration hint at the flexibility required to balance the system. It may be more difficult to 

balance wind power in the system when the load obligation of the system operator is lowest. For 

example, there are times in Denmark when the wind generation exceeds the load, and the excess 

is transferred out of the system along synchronous ties (Holttinen 2008a). A chart showing the 

wind penetration calculated as defined by WPA, WPC (without synchronous interconnections), 

and WPD (with synchronous interconnections) in Table 4 for several recent wind integration 

studies is presented in Figure 6. As the wind penetration based on gross demand increases, the 

penetration based on peak load generally increases. The wind penetration based on the lowest 

system load plus interconnection demonstrates system dependency and indicates that even with 

lower penetration levels some systems will have more challenges in low load times as other 

systems with better interconnections. In general, impacts of wind power integration increase with 

the quantity of wind generation in a system. However, it may be more difficult to accommodate 

the variability and uncertainty of wind power in systems with a higher WPD (which results in

higher integration costs). That said, although a system may have a large WPD, its ability to 

handle the impacts of wind are directly related to the flexibility of the system generation 

resources, frequency of scheduling intervals, and setup of the market to trade power.

The impacts and costs of wind integration are directly related to the operational flexibility a 

system possesses to absorb wind power variations and inaccuracies in forecasting, and these 

costs should be calculated for the different time scales of interest (minute-to-minute regulation, 

minute-to-hourly load following, hour ahead and day ahead commitment, etc.). For example, in a 

balancing area possessing ample capacity of simple cycle gas turbines or relatively nonconstrained hydropower (e.g., not run-of-the-river hydro), there may be sufficient flexibility in 

the system to easily absorb the wind power variations. Alternatively, if a system is heavy on base 

load resources (e.g., nuclear, coal, constrained hydro), it may not have the flexibility to deal with 

variations introduced by wind in the load net wind signal. In addition to the physical generator 

capabilities, operational flexibility can be influenced substantially by the flexibility of the 

institutions that manage the generation resources. This is particularly true with some hydropower 

resources, where there may be numerous organizations or non-power factors that constrain use of 

the resource. Hydropower resources such as these may be able to increase their generation 

flexibility through altering institutional practices. 
IEA Wind Task 24 Final Report, Vol. 2

13

Utility systems will differ in how much flexibility needs there are (load variability), in how much 

flexibility exists in their generation and interconnections, and also in how easy it is to increase 

flexibility with growing needs. Perhaps one useful gauge of existing system flexibility can be 

inferred from the range of variations currently managed in a given system. One method of 

gauging the existing flexibility is to consider the overall load variation currently managed, 

computed as (peak load – minimum load) / (average system load). The blue, monotonically 

increasing line in Figure 8 illustrates a plot showing this value for several recent integration 

studies. Plotted along with this are the peak system load and the wind penetration computed 

using the minimum load plus interconnections (WPD), demonstrating the variety of 

characteristics/differences across the many studies.

The various definitions of wind penetration and this method of estimating system flexibility 

provided are straight-forward to compute, and can be used as indirect ways to gauge the relative 

flexibility of a system and the flexibility required of a system to handle the additional variability 

and uncertainty of wind energy. Beyond these fairly simple, indirect measures, one would need 

to drill into the more complex, detailed system data describing the actual generation sources 

available, the load and wind characteristics, and the interconnections to get a more direct 

indicator of the potential impact of wind integration and the ease or difficulty of incorporating it. 

A summary of issues regarding system flexibility, including market aspects, is provided in Table 

4. Flexibility that is practicably available during operational situations will depend on the 

scheduling of the units, as flexibility will result from units that are on-line at a given moment, 

which can adjust their production level. It is also possible to change operational routines so that 

some power plants will be operated more flexibly to some extent in future. Wind power can also 

bring about flexibility if it is used in an “active power regulation” mode restricting the possible 

generation and using pitch regulation of the rotor blades to provide more or less power when 

needed. Building new flexible capacity (or storage) is the most expensive measure to increase 

flexibility.
IEA Wind Task 24 Final Report, Vol. 2

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Table 4. Relevant methods of defining “wind penetration” (Source: Adapted from A. Robitaille presentation, “Wind Power Integration 

Into Hydropower System: Discussion,” Task 24 R&D Meeting #5, Quebec City, Canada, June 2008)

Wind Penetration Type

Gross demand (energy) In term of installed 

capacity

Peak Penetration

(during peak load)

Maximum penetration

(during lowest load)

Calculation WPA =

Annual system demand 

(energy)

Annual wind production

WPB =

System generating 

capacity

Wind capacity

WPC =

System peak load

Wind capacity

WPD =

System lowest load

Wind capacity

Entity (country, 

state or 

company)

Renewable Energy 

Standard

(question of energy 

independence and 

ecological target)

Renewable Energy 

Standard (an easily 

defined target; relates to 

peak generation capacity)

Renewable Energy Standard 

(an easily defined target; relates

directly to peak power demand in 

the system)

Possible to compute, but 

typically not employed.

Synchronous 

Network

Renewable Standard 

Target(a large region within 

or beyond country borders)

Renewable Energy 

Standard (a large region 

within or beyond country 

borders)

Questions of system stability, regulation 

(horizon less than a few minutes)

Balancing Area Renewable Energy 

Standard (question of 

energy independence and 

ecological target)

System generation 

capacity (including 

synchronous import 

capacities)

Questions of load following reserve, efficiency, unit commitment, 

system adequacy

(cases not taking into account synchronous exchanges to be 

avoided)

In the calculation, use the 

system most probable peak 

load (including synchronous 

exchanges)

In the calculation, use the 

system most probable lowest 

load (including synchronous 

exchanges)
IEA Wind Task 24 Final Report, Vol. 2

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33,00014,000

7,0009,933

67,000

155,500

6,900

26,000

6,127

77,955

3,7805,000

53,40015,50020,000

8,800

76,000

6,5003,700

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000

0%

20%

40%

60%

80%

100%

120%

140%

160%

180%

Peak Load (MW)

Percent Wind Penetration

Wind Integration Study

Peak Load (MW) % of peak load

% of gross demand % of (min load + interconn)

Figure 6. Values for various measures of the wind penetration and peak load for several recent wind integration studies

(Source: Adapted from A. Robitaille presentation, “Wind Power Integration Into Hydropower System: Discussion,” Task 24 R&D Meeting 

#5, Quebec City, Canada, June 2008, using data from Table 18 of Holttinen et al., 2008a)
IEA Wind Task 24 Final Report, Vol. 2

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0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000

0%

20%

40%

60%

80%

100%

120%

140%

160%

180%

Peak System Load (MW)

Peak Load (MW)

(Peak Load - Min Load) /(Ave System Load)

Wind Capacity / (min load + interconn)

Figure 7. Comparison of (peak load – minimum load) / (average system load) for several recent wind integration studies against 

corresponding peak system load and wind penetration computed as a percentage of (minimum load + interconnection capacity)

(Source: Adapted from A. Robitaille presentation on “Wind Power Integration Into Hydropower System: Discussion,” Task 24 R&D 

Meeting #5, Quebec City, Canada, June 2008, using data from Table 18 of Holttinen et al., 2008a)
IEA Wind Task 24 Final Report, Vol. 2

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Table 5. Summary of issues surrounding wind integration and system flexibility (Source: A. Robitaille presentation, “Wind Power 

Integration Into Hydropower System: Discussion,” Task 24 R&D Meeting #5, Quebec City, Canada, June 2008)

Aspect Parameters To Improve the Flexibility

Relative Patterns of Wind 

Production and Load 

Wind influence on net load: 

• Ratio of additional reserve / initial reserve

• Ratio of additional operating margin / initial margin

Currently, the ratio Additional reserve / wind capacity is often 

used.

1. Load management

2. Wind power plant geographical dispersion 

strategy

3. Wind forecast improvement 

Generation System Capability Type of generation in the system:

[1] Base load vs. peaking facility

[2] Energy source: hydro or thermal

[3] Interconnections

Impacts evaluation: start and stop frequency, inefficiency, etc.

• System operation optimization

• Plan new flexible installations

• Use of existing flexibility

Market Level Player and Market 

Flexibility 

1. Local or national

2. Operating reserve cost

3. Day ahead market

4. Hour ahead market

5. Penalties for scheduling errors

• Additional interconnections

• Intra-hourly market

• Special conditions for renewable energy 

sources

• Increase the size of control area
IEA Wind Task 24 Final Report, Vol. 2

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1.3 Report Organization

Following this introductory chapter, Volume 2 chapters devoted to the case study or studies

contributed by each country, one chapter per country. If a member country submitted more than 

one case study, then these studies are presented as different sections of the chapter. Each case 

study report will:

1. describe the study methodology in detail and define the purpose of study, using the 

matrix described above as a descriptive and unifying template;

2. present defining characteristics of the power system, wind power, hydropower, market 

characteristics, and study methodology;

3. present the relevant results of study; 

4. provide conclusions from the study relevant to the Task 24 objectives and expected 

results; and 

5. provide references to more detailed and lengthy study reports created by the participants 

for each case study (if applicable).

The case study reports presented here are not full-length project reports typically created by the 

participants when conducting a study; but rather, they are condensed versions of these reports 

focused specifically on the information relevant to Task 24.
IEA Wind Task 24 Final Report, Vol. 2

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2 Australia

2.1 Introduction

Hydro Tasmania is Australia’s leading renewable energy business, providing renewable energy 

to the national grid and trade energy and environmental products in the National Electricity 

Market. Hydro Tasmania Consulting provides energy and water solutions for hydropower and 

renewable energy, dams, catchment management, environment and power engineering in the 

Asia-Pacific region. The studies in this chapter are based on Hydro Tasmania’s power system. 

Because of the potential for significant integration of wind power into the hydro-dominant 

Tasmanian power system, or provision of ancillary services to the greater Australian system 

(load net wind), Hydro Tasmania carried-out the studies described below to address various 

aspects of wind integration.

2.2 Hydro Tasmania Case Studies

2.2.1 Introduction to Studies

Hydro Tasmania has carried out three case studies that are reported in Task 24, which are as 

follows:

Case Study 1: Large-Scale Wind Integration to the Tasmanian System

Case Study 2: The Costs of Wind-Firming Service Provided by a Hydro Plant

Case Study 3: Inertia Support in a Hydro, Wind, and high-voltage direct current (HVDC) Hybrid 

Power System

All three reported Tasmanian studies are system-based.

Case Study 1 has been updated twice during the period of the task. The most recent May 2009 

update is based on Transend studies). The objective of these studies is to identify limits for the 

penetration of wind generation in Tasmania based on power system performance. Initial studies 

focused on the impacts of the addition of the first 300 MW of wind generation; however, the 

most recent study looks at operation up to full utilization of the interconnector.

Case Study 2 evaluates the impact on the storage system of installed wind generation assuming 

coordinated operation. Two systems are investigated, including operation of an islanded system 

and an interconnected system within the Australian mainland. For an isolated system, the wind 

displaces hydro generation during wet, windy periods, this results in considerable increase in 

spill. The interconnected system provides much better opportunities for additional wind 

generation to either be stored or exported to the mainland system.

Case Study 3 focuses on operating issues typical for a small system. The main issue is the effects

of large-scale wind generation displacing hydro generators and resulting in very low system 

inertia and an associated high rate of change of the frequency during system disturbances. The 

study has identified that the limiting factors in developing wind generation in Tasmania are due 

to low system inertia and very fast frequency changes affecting the operation of back protection 

schemes (i.e., under-frequency load shedding). The report also identifies that commitment of 

additional hydro generators operating in either synchronous condenser mode or tail water 

depression (TWD) mode can largely improve the integration of the wind generation in Tasmania.
IEA Wind Task 24 Final Report, Vol. 2

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In particular, the use of TWD mode allows fast machine start up from motoring operation and 

provides three valuable services including voltage/reactive power control, additional inertia, and 

additional Frequency Control Ancillary Services (FCAS). However, at present, inertia is not a 

recognized market service and the work on recognition of this service is not accepted as an offmarket service, as is the case for reactive power support.

2.2.2 Overview of Power System 

Table 6. First half of the Task 24 matrix that defines important power system characteristics and 

some basic parameters of the setup for the Tasmanian case studies

Study conducted by: Hydro Tasmania, inertia support in a hydro/wind/HVDC hybrid power 

system

Geographic area of study: Tasmania, Australia

Population: 492,700 (March 2007) 

Area: 68,332 square kilometers (26,383 sq mi).

Power system characteristics: 

Load Conventional 

Generation Interconnection Wind Power

Peak (MW) Min (MW) TWh/a Capacity (MW) Capacity (MW) MW TWh/a

1,850 900 11.1 2,810 630/480

export/import 

via HVDC

140 0.5

Other Relevant Characteristics of the Power System: Conventional generation comprises 

mostly hydropower generation with a mixture of storage and run-of-the-river schemes (2,267 

MW). The installed capacity of thermal generation is 400 MW, and the capacity of wind 

generation is 140 MW. The HVDC interconnector links the Tasmanian power system to the 

four-state network on mainland Australia. The Tasmanian power system is small in comparison 

to its largest generator (210 MW) or load (200 MW), and the frequency standards have been 

recently tightened for a single contingency to 48.0 to 52.0 Hz.

Characteristics of System Planning: 

System capacity is managed in the short- and medium-term through the Projected Assessment of 

System Adequacy. This covers a 2-year window of power generation and consumption.

Daily dispatch is undertaken as a 24-hour pre-dispatch followed by a 5-minute look-ahead 

during actual dispatch. 

In terms of generation planning, it is expected that market demand will signal when new 

generation should enter the market and where that entry could best be made. Annually, the 
IEA Wind Task 24 Final Report, Vol. 2

21

market management company publishes a Statement of Opportunities, which details changes to 

the power system and operational issues that may be relieved through investment in 

transmission or generation infrastructure.

In terms of renewable energy, the Australian government has a Renewable Energy Target 

incentive scheme based on Renewable Energy Certificates that requires an additional 9,500 

GWh per year of renewable energy by 2010. An expansion in the size of the target to 45,000 

GWh per year is being considered by Parliament at present.

Description of Market: 

The Australian National Electricity Market is a spot market based on 5-minute dispatch intervals 

(and bids). The market price is set by the marginal generation bid. The market objective is to 

supply energy at the least cost—practically, this is a constrained least cost due to the limited 

physical capability of the power system. There is a co-optimization between the spot energy 

market and the FCAS market. The FCAS market and the mechanism by which constraints apply 

to the energy market is the focus of this chapter.

Integration time frames of importance: 

Yes/No Time Frame

Yes Frequency, system dynamics, grid stability, low-voltage ride through (LVRT), PSS, 

V-Reg

Yes Regulation, AGC

Yes Load following; intra-hour ramping; economic dispatch

Yes Unit commitment and day-ahead scheduling; economic utilization of resources3

Yes Resource and capacity planning; reliability

2.2.3 Case Study 1: Large-Scale Wind Integration

This study addressed coordinated operation of hydro and wind generation. The operation of 

large-scale wind results in reduced hydro generation production. The HVDC interconnector 

provides some buffer, with the level of import or export depending on availability of both wind 

and water in storage. However, hydro plants often need to be scheduled at low output to 

accommodate wind. This is required not only to accommodate the wind but also to supply 

ancillary services required to maintain the system frequency. This case study considered the 

following issues:

• The variable nature of wind generation

• Characteristics of doubly-fed induction generators

o No contribution to system inertia

o Limited contribution to fault level

o Limited voltage control capability

o Operation of fault ride through devices

• System frequency control

• Quality control impacts

 3 Uses day-ahead scheduling to determine timing of additional inertial support
IEA Wind Task 24 Final Report, Vol. 2

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The original 2004 study was reviewed and updated in 2009 during the task, and results reflect the 

most recent updates (Future Wind Generation in Tasmania Study, Transend June 2009).

2.2.4 Study Methodology

Table 7. Second half of the Task 24 matrix that lists variations of underlying assumptions and 

modeling approaches used in the first Tasmanian wind integration studies

Set Up

A Aim of Study 2 – Define limits of integration of wind energy in 

Tasmania

M Method to Perform Study 1 – add wind energy

S Simulation model of Operation 2 – deterministic simulation several cases

Simulation Detail

R Resolution of Time 3 – minute/second

P Pricing Method N/A – it was assumed that wind generation is nonscheduled or self-dispatched (i.e., if available will run)

D Design of Remaining System 1 –constant remaining system

2 – optimized remaining production

Uncertainty and Balancing

I Imbalance Calculation 3 – wind + load + production

B Balancing Location 2 – from the same region

3 – also outside region4

U Uncertainty Treatment N/A – simulations based on worst-case approach

Power System Details

G Grid Limit on Transmission 2 – constant MW limits

3 – consider voltage

4 – N-1 criteria

5 – dynamic simulation

H Hydropower Modeling N/A – historical record of hydro availability was used

HC Hydro Capacity Service N/A – individual cases were considered in isolation

T Thermal Power Modeling N/A – Note: thermal generation modeled using 

emergency trigger for Case I

W Wind Power Modeling N/A – constant wind power output for duration of 

dynamic simulation

Issues considered in the study:

1. Generation variability

 4 The study considered the Tasmanian power system as an isolated system as well as linked to the wider Australian 

power system through Basslink, an HVDC interconnector.
IEA Wind Task 24 Final Report, Vol. 2

23

2. Reduced capability to supply frequency control ancillary services

3. Wind farms are usually connected to weak part of the system

4. Power quality

5. Generation scheduling and FCAS (reserves) requirements

6. System inertia

7. Management of HVDC interconnection

8. Wind generator fault ride through

9. System fault level

2.2.4.1 Assumptions

The assumptions for Case Study 1 are as follows:

1. Tasmania operates as an isolated system

2. Only the main transmission network is represented in the study model; however, the 

effect of predominant North-South flow constraints is modeled.

3. The system is modeled using hourly production scheduling, historical wind speed data,

and hydro inflows; wind generation is scheduled before hydro generation.

4. The following six wind generation scenarios have been studied:

• Only hydro system with no wind generation scenario

• Five wind generation development scenarios ranging from 100 MW (350 GWh) to 

500 MW (1750 GWh) installed capacity.

5. Energy cost is measured as a change in the energy equivalent of storage volume.

Common assumptions between Case Study 1 and Case Study 2 are as follows:

1. Fixed Annual Base Load (equal to hydro system long term rating), and hourly load 

profile

2. Change in the Tasmanian load between scenarios is equal to the expected wind 

generation for that scenario

3. Wind farms are modeled as having an approximate 40% plant capacity factor (based on 

present experience, this value can be as high as 45%).

2.2.4.2 Limitations

Limitations affecting Case Study 1 are as follows:

1. Fixed load assumption means that a fixed amount of the load will be met by hydropower

generation plus an emergency gas generation, if required. This load is approximately 

equal to the long-term rating of the hydro system. The approach does not consider that 

the demand on the hydro system in the future may well be different to this rating.
IEA Wind Task 24 Final Report, Vol. 2

24

2. The transmission network is not modeled in any great detail, so local network issues are 

not considered. However, main power transfer limitations of the network are reflected.

3. The hydro inflows are modeled randomly, based on 1924–2005 inflow sequences. 

Analysis done now would not consider any inflows prior to 1976 because it is believed 

that there has been a statistically significant change (less) in hydro inflow since that date.

4. The heuristic operating rules, employed in the modeling, reflect best practice by Hydro 

Tasmania system operators as of the time of the study. At that time, there was only minor 

wind integration in the Tasmanian system and, as such, these rules do not properly reflect 

how the system may be operated with large wind integration.

2.2.5 Tasmanian System Characteristics

2.2.5.1 Frequency

In 2008, the Australian Energy Market Commission changed Tasmanian frequency standards in

order to accommodate integration of Combined Cycle Gas Turbine (CCGT) plants, which cannot

handle a wide range of frequency variations. In the assessment, the following criteria were 

applied:

Following a single contingency, Tasmanian frequency must remain within:

Study 1 (2004): 47.5–53Hz

Study 2 (2009): 48–52Hz

Following a multiple contingency, Tasmanian frequency must remain within:

Study 1 (2004): 46–55 Hz

Study 2 (2009): 47–53 Hz

In the Case Study 1, a 0.5-Hz simulation error margin has been used, while Case Study 2 was

based on improved dynamic models and no frequency error margin.

2.2.5.2 Fault Level

Displacing synchronous generators with generators connected via power electronics reduces 

system fault level with consequences of affecting existing designs. To achieve adequate 

performance of HVDC, a short-circuit ratio can be linked George Town (converter station site), 

which has to be above minimum acceptable value of 3 (i.e., the fault level must be at least three 

times larger than the HVDC power transfer required). Additionally, the minimum fault level 

must be provided to keep the effect of filter switching at an acceptable level. The alternating 

current filters support reactive demand at the converter station as Basslink power transfer

changes. (Tasmania is interconnected to the Australian National Electricity Market via an HVDC 

monopolar interconnector known as Basslink.) There is a limitation on the maximum number of 

filter switching operations per hour, which depends on the magnitude of voltage dip and the fault 

level.

Fault-level limitations also apply to all system locations with installed shunt capacitors. Voltage 

change on capacitor switching depends on the fault level, and the capacitor sizes have been 

selected based acceptable quality of power supply. Consequently, any reduction in fault level 

may impact the quality of power supply. This could be particularly visible in remote locations 

(i.e., weak systems).
IEA Wind Task 24 Final Report, Vol. 2

25

Finally, minimum short circuit level is required to provide satisfactory operation of protective 

relays.

2.2.6 Wind Power Characteristics 

The doubly-fed induction generators were used to represent wind generation. All wind turbines 

were assumed to be equipped with fault ride-through devices.

2.2.7 Wind Generator Dispatching Rules in Australia

Prior to 2008, all wind generators were classified as non-scheduled, meaning they did not 

directly participate in the market solution process. Non-scheduled generators:

1. cannot set the energy spot price (they are price takers);

2. are treated as negative loads in the market solution;

3. get priority access to the transmission network over scheduled generators where there are 

constraints;

4. can cause constraints to be violated even with all scheduled generation constrained off; 

and

5. are assumed to maintain the same output level across a dispatch interval (persistence 

forecasting).

With the large increase in wind generation in Australia, dispatching the wind generation as nonscheduled has caused problems with maintaining system security and reliability. As of May 

2008, a new classification for intermittent generators was introduced, called semi-scheduled. 

Semi-scheduled generation dispatching became operational at the end of March 2009.

All new wind generation developments with a total installed capacity of greater than 30 MW will 

now be classified as semi-scheduled. In some cases, developments of less than 30 MW will also 

be classified as semi-scheduled. Current generators, those under development prior to May 2008 

and some future developments of less than 30 MW, are able to remain non-scheduled under the 

new rules.

The semi-scheduling rules have some similarities with the rules for scheduled generators, but 

with some freedom to vary output away from the dispatch target. Semi-scheduled generators bid 

into the market as scheduled generators do, but are not required to maintain output at the 

dispatch target unless a constraint could be violated. Following the market solution, each semischeduled generator will receive a dispatch target and a flag to indicate whether it must maintain 

output at or below the target. There is therefore no limit on deviating from the target in the lower 

direction, but there could at times be limits on the deviation in the upper direction.

2.2.8 Wind Power Penetration and System Flexibility 

No diversity in wind generation has been assumed. This power system study explored margins of 

system capability.

Flexibility of hydro system and HVDC allows for accommodating more wind generation. 

However, the difficulty of operating the Tasmanian system is due to inflexibility of the thermal 

plant (CCGT), its relatively large output (200 MW), and minimal contribution to FCAS—
IEA Wind Task 24 Final Report, Vol. 2

26

particularly during periods of low load when wind generation displaces hydro units, reducing the 

system inertia and increasing FCAS requirements. Because there is no other plant to supply 

FCAS, some hydro must be scheduled and typically operates at low output. Low load operation 

results in increased cavitations, increased maintenance requirements, loss of assets life, and 

inefficient use of water. Unfortunately, Hydro Tasmania is forced to such operation under low 

storage conditions when there is a need to minimize water use and consequently maximize 

HVDC import and contribution of wind energy.

2.2.9 Hydro System Characteristics 

Hydro units are not the best providers of fast FCAS. The initial response is slow and usually 

opening of the guide vanes results in reduction of the pressure and temporary reduction of 

machine output. This usually takes about 1–2 seconds. Considering that the FCAS contribution is 

calculated for the 6 seconds after frequency exceeds the 0.15-Hz threshold, there are small time 

delays in movement of the pilot valve, distributing valve and the main servo. The initial response 

to opening of guide vanes is a temporary reduction in hydro machine output for typically 1–2 

seconds before the output starts ramping out. Consequently, there are only 3–4 seconds within a 

period covered by R6 definition (6-second reserve) when hydro units ramp its output.

2.2.10 Conclusions

The 2004 report concluded that in order to ensure the security of the power system with 

minimum system load of about 900 MW together with 300 MW of import to Tasmania through 

the (then) proposed Basslink HVDC interconnector, wind generation would have the following 

implications:

• Between 130 MW and 150 MW of wind generation would require very little change in 

system operation.

• Operation with between 150 MW and 300 MW of wind generation would require increased 

FCAS (balancing5

The 2004 report did not attempt to define a maximum limit of wind generation in Tasmania; it 

merely confirmed that 300 MW could be integrated. Some earlier work has indicated that at least 

600 MW could be absorbed, depending on the assumed mode of operation of hydro machines. 

However, these earlier reports may not have fully considered the implications of the impact on 

Basslink and thermal generation in Tasmania.

) operation from conventional synchronous plant or advanced wind-plant 

control systems.

The 2009 paper reviewed the 2004 wind-penetration limits in light of the following issues:

• Two years of operating experience of wind farms in the market environment with the HVDC 

interconnector

• Significant changes in thermal generation in Tasmania with imminent closure of the Bell Bay 

plant (April 2009); new, CCGT (200-MW, Babcock and Brown, now AETV); and the 

expected net export of approximately 60 MW from 122-MW Gunns pulp mill co-generation

 5 In the Australian National Electricity Market, the balancing function is provided by FCAS products offered into a 

5-minute market and co-optimized with energy. 
IEA Wind Task 24 Final Report, Vol. 2

27

• Improvements in wind forecasting that reduced concern about wind variability and provided 

a better understanding of the requirements for frequency balancing

• Better modeling of FCAS response of hydro generators, allowing removal of the arbitrary 

0.5-Hz safety margin used in 2004

According to the 2004 report, Tasmania has now reached the level of wind generation capacity 

that can be absorbed with little change to system operation. Due to the new Australian 

government policy commitments to managing anthropogenic climate change, it is now likely that 

the pace of development of wind generation in Tasmania will increase in the near future, 

provided that system integration issues can be dealt with in a commercial manner.

If Tasmania is to carry a reasonable share of the national renewable energy target of 20% by 

2020, then the levels assessed in 2004 must be surpassed. This review of the 2004 work was 

unable to form conclusions on the feasibility of operating with more than 500 MW of wind 

generation in Tasmania. Consequently, it was considered timely to repeat the modeling process. 

A scope of work was developed for further modeling to reflect the current perspective. The new 

studies should address the following issues:

• Assessment of the impact in the context of both energy and the system security limitations, 

rather than the single worst-case of 900-MW, minimum-load and medium-import scenario 

(300 MW)

• Change in assumption of balanced energy flow across the link to predominant import 

scenario due to a long period of low inflows has resulted in low hydro storage levels with the 

consequence that currently the duration and magnitude of imports are larger than originally 

assumed

• Possible changes to allowable Tasmanian extreme frequency and emergency load-tripping 

response can manage a low-inertia system, which also includes changes to design of underand over-frequency management schemes

• The impact of the proposed Australia National Electricity Market rule change, bringing wind 

farms into the dispatch control

• Actual performance of the existing Bluff Point and Studland Bay wind farms in respect to 

meeting the original assumptions

• Increase in FCAS (balancing) requirements during low loads and high Basslink imports; 

imports of up to 480 MW (previously 300 MW) and higher Tasmanian demand

• Impact of increasing output of wind generation on generation ramp rates and HVDC power 

reversal (impact on speed to access the market), which is relevant for both directions of ramp 

rates

• Clear costing for system services required by wind generation (i.e., impact of multiple asset 

owners)

• Adequacy of the regulatory framework in relation to scheduling additional inertia or other 

support from hydro machines
IEA Wind Task 24 Final Report, Vol. 2

28

• Management of the shortage of fast frequency lowering response at high exports up to 

630 MW (including wind generation participation in the frequency control scheme during 

export)

• Market impacts of increasing wind penetration in Tasmania

The 2009 Transend Study shows that up to 1,300 MW of wind generation could be incorporated 

into the Tasmanian system with Basslink in service if mitigation measures identified in the paper 

are put in place. This figure reduces to approximately 620 MW with Basslink out of service. In

both cases, higher wind penetrations would occasionally require wind generation curtailment.

These studies indentified several concerns. First, a mechanism needs to be put in place for 

maintaining system inertia above current minimum levels. Current minimum system inertia is 

between 3,500 MW and 4,000 MW. Under the new frequency operating standards for Tasmania, 

system inertia will need to be maintained above the current minimum. Because there are many 

hydro generators in Tasmania that can operate in synchronous condenser mode, one possible 

mechanism is to bring generators online in this mode when inertia falls below minimum levels. 

Synchronous condensers are traditionally used for voltage control support; however, they also 

provide inertia and fault contribution to the system as added benefits. Where new supplementary 

voltage control equipment or dynamic reactive power sources are required, synchronous 

condensers should also be considered in place of power electronic devices (e.g., switched virtual 

circuit, static synchronous compensators), which do not provide inertia and fault contribution.

In addition, the future supply of local frequency control ancillary services is highly uncertain due 

to the imminent change in frequency operating standards in Tasmania. In order to maintain 

system security and to allow Basslink flow direction reversal, wind generators may be required 

to provide FCAS. When faults occur, wind generators must remain connected and their active 

power must return quickly following fault clearance.

The study also indicated some issues of less concern. One such concern is that system fault 

levels will be maintained above minimum acceptable levels as long as system inertia is 

maintained. Although it is possible to have very low fault levels with high levels of wind 

generation, fast FCAS requirements become unmanageable before the fault levels drop below 

current minimum levels. In addition, generation scheduling and regulation reserve requirements 

should be manageable, even for very high wind penetrations. A modest increase in regulation 

FCAS requirements is likely to be required. Another concern is that Basslink import constraints 

will not be adversely affected if system inertia is maintained. Basslink export constraints will not 

be adversely affected under most circumstances if system inertia is maintained; however, import 

conditions were concentrated upon in this study and export conditions with very high levels of 

wind generation require further study. Finally, simulations were performed with system inertia 

maintained through the use of generators operating in synchronous condenser mode and with 

wind generators offering FCAS. Maintaining system inertia has the added benefits of improving 

Basslink import and export constraints and increasing system fault levels, although shortage of 

FCAS during Basslink flow reversals still remains a problem. To remove problems in achieving 

Basslink flow reversals, which are an issue even at present, either a significant quantity of 

inexpensive fast FCAS must become available in Tasmania, or the mechanisms for handling 

flow reversals must be changed.
IEA Wind Task 24 Final Report, Vol. 2

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2.3 Case Study 2: Wind Firming – Case Study of Costs and Effects to the Hydro 

Tasmania System

The objective of this study was to estimate the cost of supporting various levels of wind 

generation in Tasmania to the owners of hydro generation. This service is referred to locally as 

wind firming. Two systems were considered: the first covering an isolated operation of a 

Tasmanian system and the second covering interconnected operation. The study focused on the 

efficiency of water storage and spill control.

Table 8. First half of the Task 24 matrix for the second Tasmanian case study that differs from the 

first Tasmanian case study

Integration Time Frames of Importance: 

Yes/No Time Frame

No Frequency, system dynamics, grid stability, LVRT, PSS, V-Reg

No Regulation, AGC

Yes Load following; intra-hour ramping; economic dispatch

No Unit commitment and day-ahead scheduling; economic utilization of resources

Yes Resource and capacity planning; reliability

2.3.1 Study Methodology

Table 9. Second half of the Task 24 matrix that lists variations of underlying assumptions and 

modeling approaches used in the second Tasmanian wind integration studies

Set Up

A Aim of Study 1 – investigate an impact of installed 1,752-GWh wind generation 

on operation of hydro system

Note: the study covers the range of energy increments from 

350 GWh to 1,752 GWh

2 – estimate energy cost of providing various level of wind firming

M Method to Perform Study 1 – add wind energy

2 – wind also replaces capacity

S Simulation Model of 

Operation 

3 – stochastic simulation several cases

Simulation Detail

R Resolution of Time 2 – hour

P Pricing Method 2 – prices for trading with neighbors

4 – market dynamics included

D Design of Remaining 

System 

2 – optimized remaining production

4 – perfect trading rules

Uncertainty and Balancing

I Imbalance Calculation 3 – wind + load + production

B Balancing Location 2 – from the same region

3 – also outside region
IEA Wind Task 24 Final Report, Vol. 2

30

U Uncertainty Treatment 1 – transmission margins

2 – hydro inflow uncertainty

3 – no wind forecasts (assume persistence)

4 – best possible wind forecasts

5 – load forecasts considered

Power System Details

G Grid Limit on Transmission 2 – constant MW limits

5 – dynamic simulation

H Hydropower Modeling 1 – head height considered

2 – hydrological coupling included (including reservoir capacity)

3 – hydrological restrictions included

4 – hydro optimization considered

Note: employed heuristic operation rules

HC Hydro Capacity Service 2 – real-time integration with impact of wind variability on 

system managed with hydro

T Thermal Power Modeling Note: thermal generation modeled using emergency 

trigger for Case I and marginal price in Case II

W Wind Power Modeling 2 – many wind speed time series 

2.3.1.1 Assumptions

This case study was carried out excluding the interconnection of Tasmania with mainland 

Australia. The generation at that time was predominantly hydro with a single gas-fired station 

providing draught relief. After interconnection, Tasmania has experienced a significant drought 

period with high interconnector import flow most of the time. Lower yields have encouraged

additional wind generation and significant growth in gas generation. The assumptions for Case 

Study 2 are as follows:

1. Tasmania is interconnected to the Australian National Electricity Market via an HVDC 

monopolar interconnector known as Basslink.

2. The Tasmanian transmission network is modeled using a simplified current 

configuration.

3. The system is modeled using an hourly time step, national Victorian generators

(interconnect with the national Australian grid in Victoria), and marginal dispatch cost 

(wind generation is self-dispatched).

4. Historical wind and hydro inflows sequences are randomly selected and used as input to 

the Monte Carlo simulation model.

5. The same as in the case of isolated operation, five scenarios of wind development have 

been used including:

• One case using100 MW as a base case

• Four wind generation cases ranging from 200 MW to 500 MW installed capacity

6. Energy shortfall between hourly wind generation and firm capacity is valued at the spot 

market price. For the purpose of this study, wind firming” implies the service of 

supporting wind power production that is assumed to have a firm 40% capacity factor 
IEA Wind Task 24 Final Report, Vol. 2

31

(40% is a typical, if not low, capacity factor for a Tasmanian wind resource). If the wind 

production falls short of 40% capacity during any given hour, the energy shortfall 

between hourly wind generation and firm capacity is valued at the spot market price. 

Wind energy in excess of the firm capacity is sold into the market. Thus, wind firming

refers to the service of guaranteeing a firm capacity from the wind power production on a 

yearly basis, via use of the hydro storage system (either storing water when wind is in 

excess of its firm capacity or spilling if necessary, and using water for generation when 

wind production falls short), with energy transactions valued at the spot market price. 

Two systems were considered: the first covering an isolated operation of a Tasmanian 

system and the second covering interconnected operation. The study focused on the 

efficiency of water storage and spill control.

7. Wind energy in excess of the firm capacity is sold into the market. (Effectively, all wind 

generation is used and hydro spill is marginal comparable to a case with no wind).

Common assumptions between Case Study 1 and Case Study 2 are as follows:

1. Fixed Annual Base Load (equal to hydro system long term rating), and hourly load 

profile.

2. Change in the Tasmanian load between scenarios is equal to the expected wind 

generation for that scenario.

3. Wind farms are modeled as having an approximate 40% plant capacity factor (based on 

present experience, this value can be as high as 45%).

2.3.1.2 Limitations

The limitations for Case Study 2 are as follows:

1. Hydro storage balance issues are not considered.

2. The same inflows (1924–2005) as for Case Study 1 were used.

3. Similar issues relating to simplification of the network as for Case Study 1.

4. Cost of floor contracts was not considered.

2.3.2 Impacts of Wind Generation

Figure 8 illustrates the primary reason why the hydro system is affected by substantial wind 

generation in a case of isolated operation of a Tasmanian system. Because there is very low load 

growth, the additional energy generated by wind is scheduled as a priority and, consequently,

hydro storage is increasing and reaching spill during substantial portions of a year. During the 

period of high hydro yields, typically June through October, the average wind speed is also high 

and the system cannot effectively store the wind generation. In other words, adding wind 

generation to an already spilling hydro system will result in increasing the duration and 

frequency of spill events. This is mitigated in the second study case by addition of the 

interconnector. The statement is a generalization that mainly applies to run-of-the-river and 

minor storage plants. Low inflow periods from January through April mean that the hydro 

system has to support a larger proportion of a bigger load when it is least capable of doing so.
IEA Wind Task 24 Final Report, Vol. 2

32

2.3.3 Hydro System Characteristics 

Figure 8. Annual distribution of the net hydro system yield and a mean wind speed; lower figure 

shows typical daily wind patterns
IEA Wind Task 24 Final Report, Vol. 2

33

2.3.4 Wind Power Penetration and System Flexibility

Figure 9. Increase in spill due to installation of wind generation; 

isolated operation of Tasmanian system

Figure 10. Increase in spill due to installation of wind generation; interconnected operation
IEA Wind Task 24 Final Report, Vol. 2

34

Firming Cost

0.000

5.000

10.000

15.000

20.000

25.000

30.000

35.000

40.000

45.000

50.000

2005 2007 2009 2011 2013 2015 2017 2019 2021 2023 2025

Year

$M

+100MW Wind Buy +100MW Wind Sell +200MW Wind Buy +200MW Wind Sell +300MW Wind Buy

+300MW Wind Sell +400MW Wind Buy +400MW Wind Sell +500MW Wind Buy +500MW Wind Sell

Interconnected operation reduces monthly spill of generated energy from 30–50 GWh to less 

than 2 GWh (see Figure 9 and Figure 10 [top graph]). The annual spill of hydro energy varies 

between 50 GWh per year to 200 GWh per annul under isolated operation. This amount is 

reduced to approximately 10 GWh per year with interconnected operation. Even with 500 MW 

of wind generation, interconnection reduces the spill to about 100 GWh after 10 years. The same 

spill would have occurred with less than 100 MW of wind generation in a case of isolated 

Tasmanian system operation.

Figure 11. Costs (Australian dollars) per megawatt-hour of wind firming service

Figure 11 shows that the annual costs of firming up wind generation vary from $5 million AUD

to $45 million AUD. The cost varies with the magnitude of wind generation and with the 

direction of interconnector flow. For a small installed wind generation capacity (100 MW), the 

firming price for import is the same as firming up export price. This gap tends to grow with 

increasing capacity of wind generation. It is noted that the firming price for exporting wind 

energy is lower than in the case of buying wind energy and using it in Tasmania.
IEA Wind Task 24 Final Report, Vol. 2

35

Figure 12. Average annual cost of firming up wind energy and reduced wind production costs

Figure 12 summarizes annual average cost of firming up wind energy as varying $1 million

AUD per annum for 100 MW of wind to $14 million AUD per annum in a case of 500 MW of 

installed wind capacity. If these costs are added to the cost of a wind project, this effectively 

results in increasing average production costs by $3 AUD/MWh in a case of 100-MW wind 

development to $8 AUD/MWh in a case of 500-MW wind farm development.

2.3.5 Conclusions

2.3.5.1 Isolated Tasmanian System

In the case of islanded operation of a Tasmanian power system, the system is unable to 

effectively absorb all output from large-scale wind generation due to coincident of high winds 

and high inflows. There is an increasing negative impact on storages as wind generation capacity 

is increased. The system energy yields (hydro and wind) in the first quarter of the year critically 

determine how large this effect is. Also, the coincidence of high wind and hydro inflows in the 

period between September and November led to a small increase in spill. Coincidence of low 

wind and hydro inflows in the period between February and June resulted in a greater 

requirement on the hydro system to meet load when storages are low. Under such conditions, 

there may be minimal reduction of using stored water.

2.3.5.2 Tasmanian System Interconnected with Mainland

The addition of the high-capacity interconnection (HVDC, 600-MW export and 480-MW 

import) with Australian mainland is a major improvement, allowing the ability to significantly 

increase penetration of wind generation in Tasmania without a negative effect on the energy in 

storage. A small increase in system spill is noted. The expected marginal cost of firming up wind 

generation can be as high as $14 AUD/MWh at 500-MW installed capacity.

-16

-14

-12

-10

-8

-6

-4

-2

0

+0MW Wind +100MW Wind +200MW Wind +300MW Wind +400MW Wind +500MW Wind

Firming Cost

Average Firming Cost ($M/yr)

Marginal Cost by Wind Production ($/MWh)

Average Cost by Wind Production ($/MWh)
IEA Wind Task 24 Final Report, Vol. 2

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2.4 Case Study 3: Inertia Support in a Hydro/Wind/HVDC Hybrid Power System

It is well recognized that the regulating capability of hydro-based systems assists with the 

integration of wind generation. However, these systems are not the best contributor to 

contingency reserves. The Tasmanian system, with a large proportion of hydro generation, has 

relatively low inertia (using heavy and low-speed machines) in comparison to thermal units; 

therefore, frequency disturbances result in a high rate of change of frequency. The initial 

response of hydro generators to a frequency disturbance is slow. Isolated hydro systems with 

significant non-synchronous sources (HVDC, wind, and photovoltaic generation) may exhibit a 

shortage of fast raise6

2.4.1 Study Methodology

FCAS. This section explores linkages between the system inertia and fast 

FCAS demand, and presents formulas for calculation of additional inertia allowing avoidance of

FCAS shortages and consequently allowing integration of greater wind generation to the system.

It also addresses strategies for improving frequency control in a Tasmanian system with largescale wind generation and the large-capacity monopolar HVDC link (energy import results in 

limiting condition) through dispatching selected hydro generators as synchronous condensers to 

increase system inertia. Subsequent work recommends gradual conversion of synchronous 

condenser operation to TWD mode supporting fast start up of units directly from motoring. In 

this mode, hydro machines contribute both inertia and some fast FCAS raise.

Table 10. First half of the Task 24 matrix for the second Tasmanian case study that differs from 

the first Tasmanian case study

Integration Time Frames of Importance: 

Yes/No Time Frame

Yes Frequency, system dynamics, grid stability, LVRT, PSS, V-Reg

No Regulation, AGC

No Load following; intra-hour ramping; economic dispatch

Yes Unit commitment and day-ahead scheduling; economic utilization of resources7

No Resource and capacity planning; reliability

Table 11. Second half of the Task 24 matrix that lists variations of underlying assumptions and 

modeling approaches used in the third Tasmanian wind integration studies

Set Up

A Aim of Study 2 – how much wind is possible

M Method to Perform Study 2 – wind also replaces capacity

S Simulation Model of 

Operation

2 – deterministic simulation several cases

3 – Stochastic simulation several cases

Simulation Detail

 6 In Australia, the term fast FCAS refers to the megawatt reserve available within 6 seconds after the frequency 

crosses ±0.15-Hz band. The market recognises two services raise (R6) and lower (L6). Other contingency reserves 

include slow service (R60 and L60) available in 60 seconds and delayed service available in 5 minutes. 7 Uses day-ahead scheduling to determine timing of additional inertial support
IEA Wind Task 24 Final Report, Vol. 2

37

R Resolution of Time 3 – minute/second

P Pricing Method 2 – prices for trading with neighbors

4 – market dynamics partially included

D Design of Remaining 

System 

1 – constant remaining system

2 – optimized remaining production

Uncertainty and Balancing

I Imbalance Calculation 3 – wind+load+ production

B Balancing Location 2 – from the same region

3 – also outside region

U Uncertainty Treatment 2 – hydro inflow uncertainty

4 – best possible wind forecasts

Power System Details

G Grid Limit on Transmission 2 – constant MW limits

H Hydropower Modeling 1 – head height considered

2 – hydrological coupling included (including reservoir capacity)

3 – hydrological restrictions included

4 – hydro optimization considered

HC Hydro Capacity Service 2 – real-time integration with impact of wind variability on 

system managed with hydro

T Thermal Power Modeling 2 – start/stop costs considered

W Wind Power Modeling 2 – many wind power time series

2.4.2 Modeling Assumptions

Stochastic modeling was performed to select a representative system dispatch. These snapshots 

were used in a dynamic simulation to determine additional inertia requirements. A further round 

of stochastic modeling was then undertaken to evaluate the cost of proposed inertial support.

Hydro inflows were selected from historical records. High-average and low-inflow years were 

selected to determine the range of inertia costs. Wind forecasts are based on wind measurements 

across several years.

It was assumed that an increase of wind generation will not alter the transmission system 

capacity, the frequency support arrangements, or HVDC inter-connector operation beyond the 

current operating philosophies.

At the time of the study, the thermal units in Tasmania were operated as drought relief and were 

therefore only enabled if hydro storages were low. Once thermal unit were committed, a 

minimum run-time was applied, so start-stop costs were implicitly considered. New thermal 

generation in Tasmania includes base load combined cycle plants and open cycle gas turbines 

increasing peaking capability.

The study only considered, in detail, the cost of additional inertia. Dispatch of additional FCAS 

(balancing reserves) is more compatible with the existing market services and more easily 

implemented.
IEA Wind Task 24 Final Report, Vol. 2

38

2.4.3 Wind Power Characteristics 

Existing wind turbines for large-scale wind farms are either double-fed induction generators or 

full-size converters. These types of wind turbines must be equipped with fault ride through 

devices to avoid tripping during faults. However, if the frequency disturbance is initiated by 

depressed system voltage (fault), wind farm output is drastically reduced for about 0.5 seconds,

increasing the magnitude of the frequency excursion. Studies have shown that the combination of 

switching power supply action (trip of generators) and fault ride through (FRT) can result in 

significant frequency swings from over-frequency to under-frequency. It was recommended that 

wind generation be placed on the switching power supply generator-tripping list with a high 

priority to improve wind integration.

2.4.4 Hydro System Characteristics 

The initial response of a hydro generator during a frequency disturbance is to change the position 

of guide vanes. When low frequency is detected, guide vanes open to increase water flow.

However, until the water column is accelerated, machine output is reduced. This takes usually 

1–2 seconds, and the time delay significantly reduces the ability to provide fast FCAS raise from 

the machine. In Australia, fast FCAS raise (R6) service is defined as twice the integral of 

machine active power response to the disturbance over a period of 6 seconds. The 6-seconds

period starts from the instant when frequency exceeds -0.15 band.

2.4.5 Wind Power Penetration and Hydro System Flexibility 

Hydro machines provide very good regulation and can easily adjust to particular output 

requirements. Considering that hydro systems are energy limited, the addition of wind is very 

beneficial, providing that the hydro stations are supported by large water storage. It is much 

more difficult to regulate active power output when the storage is small as it is in a case with 

run-of-the-river stations. Further complications are encountered during periods of high water 

inflow or specific environmental or irrigation water release requirements.

In practice, there are very few exclusively hydro systems, and characteristics of other generation 

and interconnections will influence the integration of wind generators. 2500 3200 3900 4600 5300 6000 6700 7400 200 180 160 140 120 80 100

0

100

200

300

400

500

400-500

300-400

200-300

100-200

0-100

FCAS R6 Required

Fast Raise

Inertia

Size of Contingency

Figure 13. Fast FCAS raise requirements as a function of system stored energy and the size of 

largest generator contingency (all units in megawatts).
IEA Wind Task 24 Final Report, Vol. 2

39

Hydro generator output is not fully flexible, and characteristics depend on the type of turbine. In 

Tasmanian systems, medium-head Francis turbines are dominant. They exhibit rough running 

zones, typically in the 20–60% output range, and longer periods of operation in this range are not 

recommended even with additional injection of air. If the output of Francis units is limited to 

50% in a hydro system, then integration of wind generation would be limited. Operation below 

the rough running zone is not recommended due to loss of efficiency and plant impact.

It is noted that Kaplan turbines have minimum load limitations due to potential turbine uplift.

Pelton turbines can operate at low load output because they can have a variable number of 

nozzles in operation.

The main restriction on integration of wind comes from a significant increase in fast FCAS 

requirements, both raising and lowering (the frequency). As mentioned, hydro machines provide 

slow and delayed reserves well, but fast response capability is limited. 2500 3300 4100 4900 5700 6500 7300900 1100 1300 1500 1700 1900

0

50

100

150

200

250

200-250

150-200

100-150

50-100

0-50

FCAS R6 required

for 144MW generator

contingency

Inertia

Demand

Figure 14. FCAS R6, a function of system-stored rotating energy and system demand 

(all units in megawatts).

Figure 14 indicates that for the fixed generator contingency size (144 MW) and with system 

stored rotating energy above 4,000 MW, the R6 requirements are below 100 MW. Only when the 

system inertial energy is below 4,000 MW and Tasmanian demand is low can R6 requirements 

be more than 200 MW. This figure appears to be fairly small; however, it is a difficult target to 

achieve in a hydro-based system considering the following:

• Overnight Tasmania load is 1,000 MW or less.

• With low-spot market price, Tasmania may import energy from the mainland (e.g., 

400 MW).

• The heavy, 1,700-MW CCGT plant is in service operating at half output (100 MW); the

inertia of the largest machine is excluded from the calculation of FCAS.
IEA Wind Task 24 Final Report, Vol. 2

40

• There is significant wind generation with an output of 200 MW

The above scenario leaves only 200 MW of hydro plant scheduled. Assuming a 50% load factor,

400 MW of hydro capacity is in service contributing to stored inertial energy of about 1,600 MW 

(average inertia time constant of hydro machines is 4). Securing this dispatch would require 

more than 200 MW of R6 being scheduled. Under such conditions, available FCAS is typically 

limited to 30 MW, and HVDC contributes approximately 80 MW. To meet the requirements of 

constrained dispatch, Hydro Tasmania can either:

• operate hydro generators on low loading with all consequences of such operation considered, 

which would add both additional FCAS and increase system inertia, or

• add some synchronous condensers to increase system inertia and reduce FCAS requirements. 

Adding six large-size hydro generators operating in synchronous condenser mode would increase 

inertia to above 4,000 MW and reduce R6 required to below 100 MW. It is noted that adding 

first one or two synchronous condensers drastically reduces FCAS requirements; however, as the 

flat part of the characteristic is approached, the incremental gains are much smaller (Figure 15).

0

50

100

150

200

250

300

2000 3000 4000 5000 6000 7000 8000 9000

47.5Hz 120MW

47.5Hz 144MW

48Hz 144MW

Figure 15. Fast FCAS R6 requirements (vertical axis, in megawatts) as a function of system inertial 

energy (N-1) (horizontal axis, in megawatts).

Operation of hydro machines in TWD mode (i.e., a mode similar to a synchronous condenser but 

with the main inlet valve open and governor and hydraulic system active) allows rapid change 

over to the generation mode with some contribution to FCAS. Tests have shown that not all 

Francis turbines are equally effective, and units with short water acceleration time and fast guide 

vanes opening can provide more FCAS. This double contribution of additional inertia and some 

FCAS (10–15 MW) provides an equivalent support to six synchronous condensers with only two 

TWD machines.

Unfortunately, there are some institutional constraints imposed by current market rules that are 

presently being addressed. The Australian electricity market cannot dispatch at present machine 

in synchronous condenser mode or in TWD mode. This means that it is possible to operate a 

synchronous condenser, but it will not receive any payment unless it is contracted as network 

control ancillary service.
IEA Wind Task 24 Final Report, Vol. 2

41

TWD mode is used in New Zealand, but it cannot currently dispatched in Australia. For TWD to 

be recognized, the provider needs to be registered as a FCAS provider and a switching controller.

Assigned frequency triggers activate switching controllers, and the trigger frequency results in 

slower activation of the controller and lesser FCAS contribution. The rate of change of frequency

is not currently recognized as a trigger for switching controllers.

2.4.6 Conclusions

Large penetration of wind generation in the isolated hydro system would result in commitment of 

fewer hydro generators under strong wind conditions. Also, generators in service would operate 

at lower than efficient output. This situation is made more difficult with the HVDC 

interconnector. Recent experience in Tasmania shows that some generators operate at output as 

low as 10% to increase system reserves (i.e., FCAS) and to make the system heavier (i.e., add 

inertia).

The cost of low output operation is high due to low efficiency. Increased cavitation damage and 

higher maintenance requirements have also been reported. The cost of supplying additional 

inertia in the hydro-based system with machines capable of operation as synchronous condensers 

or in tail water depression mode is low in comparison with the potential benefits. The cost of 

motoring relates to a load of about 2% of machine rating. These benefits include the following:

• Allows maximization of inter-connector flows

• Allows greater fraction of inertia less generation (variable speed doubly-fed induction 

generators wind generation) in Tasmania

• Improves efficiency of using water comparing to low load operation

• Reduces maintenance requirements on hydro units

Frequency control in the Tasmanian system has become more challenging in recent years due to 

the following factors:

• An outage of HVDC interconnector (bi-directional flows) has become effectively the largest 

generator contingency (630 MW) or the largest load (480 MW) contingency. Switching 

power supply schemes controlling load and generator tripping make the HVDC 

interconnector outage appear the same as normal load contingencies (200 MW) or generator

(144 MW) contingencies. 

• Import over the interconnector enables a number of hydro generators to be displaced as well 

as reduces loading of generators in service. This reduces the inertia of the Tasmanian system 

and increases FCAS requirements. Some hydro machines are forced into low load operation 

in order to supply the required FCAS services (with fast FCAS being the limiting constraint). 

This practice causes deterioration of condition of hydro assets, increased maintenance cost, 

and loss of machine life. It is noted that HVDC is equipped with a frequency controller that 

equalizes frequencies in both systems and modulates target HVDC power transfer, also 

allowing transferring of FCAS.
IEA Wind Task 24 Final Report, Vol. 2

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• Commissioning a large-size CCGT plant has resulted in the following:

o The inertial contribution of 1,700 MW from these units is much larger than for the largest 

hydro unit (650 MW), which increases the severity of a single generator contingency and 

contributes to higher FCAS demand.

o To accommodate the connection of the CCGT plant, Tasmanian frequency standards 

have to be tightened with the generator contingency band being reduced by 0.5 Hz from 

47.5–48 Hz. This change has increased fast FCAS raise (R6) requirements by at least 

30 MW.

o The increased size of the largest generator contingency to 144 MW (with the remaining 

70 MW of CCGT capacity provided by a matching load tripping) increases fast FCAS 

raise and lower requirements by an additional 30 MW.

Tasmanian wind farm sites located in the “roaring 40s zone” (i.e., the 40th parallel south of the 

equator, a notoriously windy latitude in the southern oceans) are very attractive to developers

because they offer wind utilization of 40–45% (capacity factor). Wind developments tend to 

displace some hydro generators from the dispatch and further reduce the system inertia.

The work described in this chapter is still in progress. Considering that current wind generation 

in Tasmania is 140 MW and no significant problems with integration of wind generation are 

expected until there is more than 300 MW, the issues described are being progressively 

discussed to improve market rules (i.e., overcome institutional constraints) and allow more 

renewable energy sources to be integrated. Note the full reports on these projects, from which 

this case study is condensed, provide formulas allowing calculations for additional inertia 

required to make FCAS available sufficient to satisfy system security requirements.
IEA Wind Task 24 Final Report, Vol. 2

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3 Canada

3.1 Introduction

3.1.1 General Description of Study and Goals

The purpose of this study was to perform a RETScreen8

3.1.2 Organizations Involved and Who Conducted the Study

analysis for a case study involving a 

wind power purchase option by the Okanogan PUD in Washington State, U.S.. The RETScreen 

process is a tool used to evaluate the financial feasibility, energy production estimates, emissions 

impacts, and general risk assessment for the development of renewable energy projects. The 

particular focus of this study was assessment of the above objectives regarding plans by the 

utility to purchase a 25% share of wind power from the Nine Canyon Wind Farm project for 

integration into its hydro-dominated power structure. Detailed site, system, and wind turbine 

characteristics were blended with financial information to form a report for use in decisionmaking. This study differs from most of the other case studies that have been contributed to 

Task 24; it is an economic evaluation of incorporating wind energy into a system versus other 

alternatives (an estimate of the value of wind energy), and not a grid integration study that seeks 

to determine the impacts and costs of wind power’s variability and uncertainty.

The study was conducted by CANMET Energy Technology Center-Varennes, which is part of 

Natural Resources Canada. Additional information was obtained from Okanogan PUD of 

Washington State, U.S.

3.1.3 Why the Study was Performed

The study was performed as a post-project comparison of RETScreen estimations and actual 

results from an existing purchase agreement. The RETScreen process was used to compile 

predictions for a 22-year contract period as if the project were still in the planning phase, 

although the purchase agreement already in effect and 3 years of data were available for 

validation against RETScreen estimations. The goal was to compare options for continued 

purchase of supplemental power from the market to options for the acquisition of additional 

power purchased from wind generation. Okanogan PUD purchases all of its power, 

approximately 60% of which is supplied by the Bonneville Power Administration (BPA) and

30% by the Wells Hydroelectric Project. The purpose of the study was to determine if the 

additional energy procured by wind at the proposed rate would be economically favorable to 

purchasing power from the market when supplementary amounts are needed. Wind resource 

availability predictions were compared with actual delivered power over the 3-year time period, 

and correlations between wind output and drought were investigated. Regional market prices 

tend to be higher during times of drought, when hydro resources are limited. Wind would most 

likely add to the available power during these times, making costly market purchases less 

frequent.

 8 See www.retscreen.net/ang/home.php; RETScreen stands for Renewable-energy and Energy efficient 

Technologies Screening tool, and was developed for Natural Resources Canada.
IEA Wind Task 24 Final Report, Vol. 2

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3.2 Intended Outcomes

The RETScreen analysis sought to evaluate the economic effects of integrating wind generation 

into a hydro-dominated utility that was often required to purchase power from the market to 

supplement the scheduled resources. Particular attention was paid to wind resource availability 

during times of drought, when hydro resources are strained and market prices tend to be higher.

Grid integration was addressed from the standpoint of system characteristics, as well as a need to 

procure transmission contracts ensuring power would be delivered to the utility over a distance 

of 320 km from the wind site.

Detailed economic analysis was performed that included total intended financing, proposed cost

per megawatt-hour to power purchasers, avoided cost of energy due to wind, government 

Renewable Energy Production Incentives, operations and maintenance estimates, and land 

leasing costs. Net present value calculations were also made, and the cumulative cash flows for 

the wind project were estimated for the 22-year contract period. Risk analysis was quantified by 

a set of parameters with outputs shown in Figure 16.

Figure 16. RETScreen outputs for risk analysis and net present value
IEA Wind Task 24 Final Report, Vol. 2

45

3.3 Overview of Power System 

Table 12. First half of the Task 24 matrix that defines important power system characteristics and 

some basic parameters of the setup for the NRCanada case study

Geographic Area of Study: Okanogan County, 

Washington is the location of the public utility 

considering the purchase of power from a proposed 

wind power project. The proposed wind power project 

is to be located on a high bench overlooking 

Kennewick, WA, about 340 km east of Portland, 

Oregon, along the Columbia River Gorge. The bench is 

roughly perpendicular to the dominant winds, which 

are channelled through the Columbia River Gorge from 

the southwest.

Map source: Wikipedia

Power System Characteristics: 

Load Conventional 

Generation Interconnection Wind Power

Peak 

(MW) Min (MW) TWh/a Capacity (MW) Capacity (MW) MW TWh/a

1459 4010 0.845 N/A – all power 

is purchased

75 MW 15.3 MW 0.038

TWh/a

Estimated

CF = 31.4%

Other Relevant Characteristics of Power System: 

The wind power project’s underground 34.5-kV power lines and 115-kV substation were constructed 

by one of the purchasers, Benton County PUD, and interconnects with the BPA 115-kV system. 

Okanogan PUD’s share of the wind project’s output is delivered to the network on BPA lines to 

Douglas PUD’s Load Control Area. The Load Control Area operator uses Okanogan’s 65-MW part of

hydropower capacity at Wells Dam (840 MW) on the Columbia River to smooth wind variability.

The system has a dual peak in the winter and summer of approximately 145 MW. Its average load is 

about 90 MW (based on an assumed 60% system load factor). Its generation resources are almost 

entirely hydroelectric (86%) with several limiting flow restrictions through dams located on the MidColumbia river. (Source: Professional Opinions of Jean-Claude Deslauriers and Jacques Fontaine, 

energy R&D, and prediction experts)

Interconnections to the system are mainly hydro, which is purchased from the BPA sources of which 

include 30 federal hydro projects, one non-federal nuclear plant, and other non-federal power plants. 

The Okanogan County PUD receives approximately 60% of its energy supply from BPA. 

65 MW of hydro is purchased from Wells Hydroelectric Project (840 MW) on the Columbia River 

operated by Douglas County PUD. Okanogan County PUD receives 30% of its energy from Wells 

 9 Estimate based on non-coincidental maximum demand (160 MW), the sum of power purchases, which is typically 

10% higher than the actual peak load (Source: 2005 PUD Annual Report and Professional Opinions of Jean-Claude 

Deslauriers and Jacques Fontaine, energy R&D, and prediction experts)

10 Estimate based on assumption of 27% of peak load (Source: Professional Opinions of Jean-Claude Deslauriers 

and Jacques Fontaine, energy R&D, and prediction experts)

Proposed 

wind project
IEA Wind Task 24 Final Report, Vol. 2

46

through a long-term power purchasing agreement. 

Hydro 86.41%

FUEL breakdown (%) for the year 2005:

Nuclear 6.87%

Coal 3.56%

Wind 1.80%

Other (NG, Biomass, Petroleum) 1.36%

Total 100.00% 

Wind purchases from Energy Northwest: a membership of 13 public utilities and 3 city municipal 

utilities, which developed and now own the Nine Canyon Wind Project, a 49-turbines wind farm with a 

total generating capacity of 63.7 MW. Okanogan PUD has a 24% share of the farm’s energy output.

Characteristics of System Planning: 

Priorities in planning: (1) Fish migration, (2) Flood control (note flood control will take precedence 

over fish migration when dam structures are threatened), (3) Recreation and lake levels, (4) Navigation, 

(5) Power production (Source: Grant County PUD case study example as it is also in Washington State 

and the utility’s hydro projects are also on the Columbia river system)

Description of Market: 

Purchases and sales are made at the Mid-Columbia hub. 

Integration Time Frames of Importance:

Yes/No Time Frame 

No Frequency, system dynamics, grid stability, low-voltage ride through, 

Pumped Storage Station, Voltage Regulation

No Regulation, Automatic Generation Control

No Load following; intra-hour ramping; economic dispatch 

Yes Unit commitment and day-ahead scheduling; economic utilization of 

resources (shorter time frame issues are handled by the Douglas PUD Load 

Control Area operator)

Yes Resource and capacity planning; reliability

3.3.1 Study Methodology

The study was conducted as a post-project assessment of the RETScreen process. The 

RETScreen analysis was conducted as if the project was still in development, and results were 

subsequently compared to the actual performance of the project for the years 2003–2005. Study 

inputs included wind project site information, applicable system characteristics, wind turbine 

characteristics, and financing information. Basic parameters of the study analysis are provided in 

Table 13.
IEA Wind Task 24 Final Report, Vol. 2

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Table 13. Second half of the Task 24 matrix that defines important power system characteristics 

and some basic parameters of the setup for the NRCanada case study

Set Up 

A Aim of Study 

2 how much wind is possible 

1 what happens with x GWh wind

M Method to Perform 

Study 2 wind also replaces capacity 

1 add wind energy 

3 optimal system design 

S Simulation Model of 

Operation 2 deterministic simulation several cases 

1 deterministic simulation, one case 

3 Stochastic simulation several cases 

Simulation Detail 

R Resolution of Time 

2 hour 

1 day/week 

3 minute/second 

P Pricing Method 

2 prices for trading with neighbours 

1 costs of fuels etc 

3 market actor simulation 

4 market dynamics included 

D Design of Remaining 

System 2 optimized remaining production 

1 constant remaining system 

3 optimized remaining transmission 

4 perfect trading rules 

Uncertainty and Balancing 

I Imbalance Calculation 1 only wind 

2 wind + load 

3 wind + load + production 

B Balancing Location 1 dedicated source 

2 from the same region 

3 also outside region – Douglas PUD local control area is a 

dedicated source. The contract between Okanogan and 

Douglas likely involves a balancing service.

U Uncertainty Treatment 1 transmission margins 

2 hydro inflow uncertainty 

3 no wind forecasts (assume persistence) 

5 load forecasts considered 

4 best possible wind forecasts (professional forecast)

6 thermal power outages considered 

Power System Details 

G Grid limit on 

Transmission 2 constant MW limits 

1 no limits

3 consider voltage 

4 N-1 criteria 

5 dynamic simulation 
IEA Wind Task 24 Final Report, Vol. 2

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H Hydropower Modeling 1 head height considered 

2 hydrological coupling included (including reservoir capacity) 

3 hydrological restrictions included 

4 hydro optimization considered 

5 consider most relevant aspects of affected hydro resources 

All of the above – this is done by the Federal Columbia River 

Power System.

HC Hydro Capacity 

Service 

1 interaction with hydro resources not significant 

2 real-time integration with impact of wind variability on system 

managed with hydro 

3 capacity service: addition of capacity value through 

redelivery of wind energy at a later time 

T Thermal Power 

Modeling 

1 ramp rates considered 

2 start/stop costs considered 

3 efficiency variation considered 

4 heat production considered 

None of the above – does not apply in this study

W Wind Power 

Modeling 

1 few wind speed time series 

2 many wind power time series 

3 time series smoothing considered

None of the above – no time series data was required

3.3.1.1 Assumptions

The assumptions made for this study were based on information that would have been available 

to the Okanogan PUD prior to project development. This information included such things as 

estimated wind energy production, estimated wind power purchase prices including 3% annual 

increases, estimated market prices, and all project development plus logistical costs.

3.3.1.2 Limitations

The comparison aspect of this study was limited by the fact that only 3 years of actual data were 

available for validation as opposed to the full 22 years projected by the RETScreen analysis. 

Additional complications were encountered with wind turbine malfunctions that could not be 

anticipated by the simulation tool. 

3.3.2 Wind Power Characteristics

The proposal was to purchase a 25% share of power from the 64-MW Nine Canyon Wind Farm 

located 320 km from the utility. The wind plant is located on an elevated bench along the 

Columbia River Gorge. The estimated amount of energy that would be delivered by wind was 

161,842 MWh/year. Table 14 shows the resource assessment outputs as given by the RETScreen 

tool.
IEA Wind Task 24 Final Report, Vol. 2

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Table 14. RETScreen outputs for estimated wind power

3.3.3 Hydro System Characteristics

In 2005, 86.4% of the generation mix for the Okanogan County PUD was obtained by hydro 

purchases from BPA and the Wells Hydroelectric Project on the Columbia River. Hydro 

planning is prioritized in the order of fish migration, flood control, recreation, navigation, and 

power production. 

Hydro availability is largely contingent on snow pack runoff into the Columbia River from 

January through July. This study sought to examine the correlation between periods of drought 

and wind behavior for the region. Positive correlations were found between periods of low snow 

pack runoff and low wind output for the 3 years of wind data (note a much longer time record 

would be required to determine if a correlation actually exists between wind production and 

snow pack runoff), as well as greater wind power cost and higher wind power value as shown by 

Figure 17.
IEA Wind Task 24 Final Report, Vol. 2

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Figure 17. Correlation of wind power output and snow pack runoff, along with wind value and cost 

for the study period of 2003–2005

3.3.4 Wind Power Penetration and System Flexibility

The Okanogan County PUD was interested in acquiring 25% of the power output from the Nine 

Canyon Wind Farm project. On a capacity basis, this amounts to about 16 MW, or 11.4% of the 

peak load levels of 140 MW. On an energy basis, this would amount to approximately 7% of the 

annual energy requirement for the utility, given an estimated capacity factor of 31.4% for the 

64 MW project. 

Wind variability is smoothed by the Load Control Area operator for Douglas County PUD at the 

Wells Dam on the Columbia River. This also allows Okanogan PUD’s share of the wind power 

to be delivered at a later time.

3.3.5 Wind and Hydro Integration – Benefits and Impacts

In order to integrate this wind power into the primarily hydro generation mix, the RETScreen 

report recognized the need for a revised service agreement with the Load Control Area operator 

for real-time balancing (conducted by Douglas County PUD). Wind energy production variations 

can be balanced by the flexible ramp characteristics and available storage of the Okanogan 

PUD’s hydro resources. The study found that the amount of wind being integrated from the Nine 

Canyon Project would lead to minimal need for additional ancillary services, as fluctuations in 

wind power production would hardly alter the utility’s existing load swings.
IEA Wind Task 24 Final Report, Vol. 2

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3.4 Conclusions

The conclusions of this study offered a comparison of the parameters that were predicted by the 

RETScreen analysis and those that were encountered in reality. The actual wind power delivered 

over the 3-year period of 2003–2005 was about 90% of that predicted to be normal by the 

RETScreen procedure. The net cost of delivered wind energy during this period was 

$44.1 CAD/MWh, which exceeded the cost of $43.9 CAD/MWh that would have been accrued 

if the energy had been purchased from the market. This negative value was made up for when the 

utility sold Renewable Energy Credits during 2005 for $3 CAD/MWh, resulting in a positive net 

value of $37,000 CAD for the wind power over the 3-year period. The report also concluded that 

the wind purchase would not have been economically favorable without the Renewable Energy 

Production Incentives.

The study highlighted results showing the ease of integrating wind into a hydro-centered Load 

Control Area, as balancing costs were kept at $0.9 CAD/MWh instead of the $4.5 CAD/MWh 

that would have been required for purchasing balancing services from BPA. It suggested that

there would be minimal requirements for additional ancillary services due to wind, as 

fluctuations were easily smoothed by the Load Control Area operator using hydro flexibility.

During the study years, there was also found to be a positive correlation between periods of 

drought and low wind for the region. This corresponded to an increase in both the cost and value 

of available wind power. However, market prices were also found to be higher during these same 

periods, helping to maintain the economic position of purchasing wind power.
IEA Wind Task 24 Final Report, Vol. 2

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4 Finland

4.1 Introduction

The IEA Wind Task 24 for Finland includes the following two projects: 

1. A case study focusing on the handling of wind power prediction errors for a single 

hydrothermal power producer in Finland 

2. A summary of the impacts of a wind- and hydro-dominated power system on the 

electricity markets and the characteristics of Nordic hydropower.

In this chapter, several words with specific meanings are used. The word regulation is used to 

mean 10–15-minute balancing. The balancing market refers to what is officially called the 

Regulating Power Market in the Nordic countries. The more neutral word balancing is also used.

4.2 Case Study for a Single Power Producer

The Technical Research Centre of Finland (VTT) conducted a case study focused on the

handling of wind power prediction errors for a single hydrothermal power producer in Finland.

4.2.1 Introduction to Study

The study’s description and goals are as follows:

• Scope of study: Different options for handling the forecast errors of wind power as well as 

balancing costs are studied from the producer’s point of view.

• Organizations involved: VTT conducted the study, with case data from a large hydrothermal 

producer in Finland.

• Reason study was performed: The study was performed to see the possibilities of a 

hydrothermal power producer with limited regulation possibilities to balance wind power in 

its portfolio.

• Published reports applicable to the case study: 

o Holttinen, H. Koreneff, G. 2007. Imbalance costs of wind power for a hydropower 

producer in Finland. Proceedings of European Wind Energy Conference EWEC2007. 

Milan, Italy, 7–10 May, 2007. European Wind Energy Association.

o Intended outcomes of the report relative to the objectives of Task 24: The study addresses 

market and economics; it highlights some issues regarding grid integration.

4.2.2 Overview of Power System

This case study is based on one producer using the Nordic electricity market. The producer has 

400 MW of run-of-the-river hydropower with very limited storage possibilities. The Task 24 

matrix that defines important power system characteristics and some basic parameters of the 

setup for an integration study is not applicable to this study. Wind power generation is forecast 

1 day ahead, and balancing can be left to be done by the system operator at the balancing market 

(which results in paying imbalance prices for forecast errors); or by the producer internally by 

adjusting hydropower production. Part of the forecast errors can be dealt with intra-day trade, 

which can reduce the imbalance prices paid.
IEA Wind Task 24 Final Report, Vol. 2

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4.2.3 Study Methodology

The case study was based on 1 year of historical data. A simulation model was not used; 

therefore, a matrix intended to explain simulation assumptions has not been included. A time 

series of forecast errors for 9 MW of wind in 2004 was up-scaled to 200 MW and 400 MW of 

wind power (representing 50% and 100% of the producer’s installed hydropower capacity, 

respectively). The first step in the study was pricing imbalances at current Nordic market rules 

(2004 price data). The second step was using 2004 prices for intra-day market pricing (closing 

1 hour before delivery) to calculate how intra-day trading would reduce imbalance costs. The 

third step was looking at the possibilities of hydropower to handle the imbalances, which was 

done by looking at the time series of produced hydropower in 2004 and calculating how much of 

the forecast errors of wind could be corrected by shifting hydropower production some hours. 

Limits for minimum and maximum hydropower production were kept the same, and the total 

energy for each day was kept at ±10 % of the original time series.

The study assumed that wind power in Finland (200–400 MW) would not affect the day-ahead, 

intra-day, and balancing market prices. A limitation of the study is that a simulation was not 

performed to estimate how much hydropower energy could be shifted from 1 day forward.

4.2.4 Wind Power Characteristics

Data from more than 10 sites along the Finnish west coast were used in the study, at maximum 

600 km apart, providing geographical dispersion. A wind forecast time series of 1 day ahead 

(14–38 hours ahead) was calculated with real wind forecasts for year 2004; errors were 

calculated based on real, measured wind power production data. An hourly time scale was used, 

as applied in Nordic electricity markets.

4.2.5 Hydro System Characteristics

The producer studied has a total capacity of 400 MW of run-of-the-river hydropower with 

limited storage possibilities (used to follow day-night load variations).

4.2.6 Wind Power Penetration and System Flexibility

Wind power penetration in Finland was less than 1% of gross demand (energy) and less than 3% 

of peak load (capacity). Two wind power scenarios, 200 MW and 400 MW, were studied, which 

were 50% and 100% of the producer’s installed hydropower capacity.

4.2.7 Wind and Hydro Integration – Benefits and Impacts

This section outlines the three steps used for integrating wind power and hydropower.

4.2.7.1 Step One: Balancing Cost from the Day-Ahead Market

The forecast errors for the 14–38 hours-ahead time series results in a mean absolute error of 8% 

for the aggregated wind farm data, normalized as a percent of the nominal capacity of wind 

farms. When calculating energy as a percent of average production, the result is that 31% of the 

energy generated will result in imbalances for the producer. For the imbalance payments of wind 

power alone, a fixed volume price is used (€ 0.7/MWh), and regulation price from the market 

applies for the hours when the net system imbalance has been to the same side as wind power 

imbalance. For example, if up-regulation has been used from the balancing market and wind 

power imbalance has been demanding up-regulation, then there is a payment according to the 

balancing market price. For year 2004 prices, this results in € 0.62/MWh costs for the year’s total 
IEA Wind Task 24 Final Report, Vol. 2

54

wind power production. The cost of regulation, when there is an extra cost, is on average 

€ 3–4/MWh. However, more than half of the time, the wind power imbalance is opposite to the 

total system imbalance and only the fixed volume price € 0.7/MWh is paid for the imbalances 

during those hours.

4.2.7.2 Step Two: Using Intra-Hour Trading (the Elbas Market operating in Nordic 

countries)

In this step, a 3-hours-ahead forecast leaves a balancing power requirement of 21% of 

production, compared to 31% for the 14–38-hour forecast. This results in an imbalance cost 

reduction of 34%. On the other hand, to manage the balance gap between the spot trades and the 

3-hours-ahead forecast requires Elbas market trading, which will bring costs. At the intra-day 

Elbas market, the trade is continuous, resulting in several prices for each hour. Using average 

Elbas prices for each hour in 2004 results in overall imbalance cost reductions, but calculating 

with maximum-minimum prices at Elbas shows that if the Elbas price is significantly deviating 

from the day-ahead market price, the trading costs can deplete the benefit of reducing balancing 

costs. This occurs especially at low penetration levels of wind power when at least 50% of the 

time, wind power will only pay the fixed volume price € 0.7/MWh for the imbalances due to an 

error being to opposite side than the regulation price.

4.2.7.3 Step Three: Using Internal Balancing by Hydropower

Even if the hydropower is operating with no large reservoirs, it is usually possible to change a 

part of the production some hours ahead or before the schedule starts. This was analyzed by 

using an up-scaled time series for wind power (200 MW and 400 MW) together with actual 

production of hydropower from the year 2004. In the internal balancing model, hourly values of 

hydropower generation were restricted to operating between the maximum and minimum 

production that had occurred during the day in question. In addition, the daily production 

flexibility (daily sum of generation) of hydropower was restricted to ±10% of original daily 

energy based on current operational practice. A deviation of 10% from the daily produced energy 

is assumed to be tolerable and manageable by the hydropower plants (Figure 18). Operating this 

way, 83% of prediction errors could be corrected by internal balancing for 200 MW of wind 

power. For 400 MW of wind power, 63% of the imbalances could be balanced internally. The 

internal balancing of wind production imbalances with hydropower would reduce the balancing 

costs by 84% for the 200-MW case and 63% for the 400-MW case, assuming the internal 

balancing were without costs. Depending on the price set for the internal balancing and the wind 

power capacity, the balancing costs for the wind power producer would be reduced by roughly 

20–85%. On the other hand, the same balancing offered to balancing markets would be worth an 

equivalent of € 1.31–1.32/MWh wind regulation.
IEA Wind Task 24 Final Report, Vol. 2

55

0

50

100

150

200

250

300

1 25 49 73 97 121 145 169 193 217 241 265 289 313

hour

Hydro power production (MW)

Hydro power production without wind

Hydro, internal balancing to 400 MW wind 

Figure 18. Example of how 400 MW of hydropower production could change when correcting 

errors on day-ahead forecasts of 400 MW of wind power

4.2.7.4 Step four: Passive Internal Balancing Aggregating the Imbalances of Wind with 

other Imbalances of a Large Producer

Aggregating wind power with all other imbalances of a Balance Responsible Player, from 

consumption and production, would benefit a wind power producer and reduce balancing costs. 

Even if wind power would get half of the benefit, this would reduce the imbalance costs 

considerably if there was a possibility to combine the imbalances with load. This is shown as the 

last option in Figure 19, where the black area shows how much imbalance costs increase as wind 

imbalances are added up with load imbalances, and the grey area shows how much this would be 

if wind would get only part of this benefit.
IEA Wind Task 24 Final Report, Vol. 2

56

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

Wind power

spot market

Spot + Intra

day market 

Hydro

balancing

Wind and load

imbalance

aggregation

Wind power

spot market

Spot + Intra

day market 

Hydro

balancing

Wind and load

imbalance

aggregation

Balance costs €/MWhproduced

Balance settlement costs Extra costs min... max

200 MW Wind 400 MW Wind

Figure 19. Costs for balancing day-ahead forecast errors for wind power (cases 200 MW and 

400 MW wind power); extra cost range is from using different assumptions for the cost of the 

balancing option

4.2.8 Summary of Results

Imbalance costs vary for different cases, as can be seen from Figure 19. The costs for Elbas 

intra-day market depend on luck at Elbas trade (e.g., average Elbas price, the minimum and 

maximum Elbas price for each hour). The cost for hydropower options depends on the internal 

balancing price (€ 0.0–1.3/MWh, internally regulated). The benefit from combining wind and 

load imbalances depends on how large a share wind gains from the total balancing crossover 

effect (100–50%).

This study shows that even with limited flexibility of hydropower (run-of-the-river with small 

reservoirs), a large part of wind power forecast errors can be provided for by shifting 

hydropower back and forth inside 1 day. This is because the wind power forecast errors are on 

average 0, so both up- and down-regulation are used and the side of balancing varies from up to 

down and back frequently.

The study also shows that when correcting the forecast errors of wind power at a large balancing 

market in which hydropower produces most of the balancing (such as in Nordic countries), there 

is not a great benefit of combining/integrating wind and hydro at a single producer. It is more 

cost effective to bid all flexibility of hydro to the balancing market, and use it from there to 

correct the system imbalances, than to use it for dedicated balancing of wind power. 
IEA Wind Task 24 Final Report, Vol. 2

57

4.2.9 Conclusions

The imbalance costs from day-ahead forecast errors for aggregated wind power in Finland is 

roughly € 0.62/MWh, when calculated per megawatt-hour total produced. The cost of regulation, 

when there is an extra cost, is on average € 3–4/MWh. But, more than half of the time, the wind 

power imbalance is opposite to the total system imbalance. For those hours, the only cost effect 

is the fixed volume cost of € 0.7/MWh for the imbalances. These results apply for the Finnish 

power system in which wind power is not affecting the price level or direction of regulation used 

in the power system. Also, the balance settlement rules affect the results. Two price models are 

used in this study, which means that the imbalance pricing is according to regulation market 

prices when the imbalances are to the same side as regulating market price (e.g up-regulation 

price and up-regulation required for wind power) and the spot market price is used for the hours 

when imbalances are to the opposite side of regulation market price (f.ex. up-regulation price and 

down-regulation required for wind power). The use of a one-price model where a single price is 

used for all imbalances, would drop the balancing costs to near 0. This is because wind power 

gets extra benefit for the imbalances during the hours where it has been opposite the regulating 

power market (e.g., during an up-regulation hour wind power has required down regulation 

which means providing up-regulation for the system).

Compared with leaving all day-ahead forecast errors to balance settlement, intra-day trading is 

only cost effective when trading close to spot price levels. Actively trading to reduce the forecast 

errors of wind means making trade almost all the time. Leaving the forecast errors to imbalance 

settlement means that more than half of the time there is no penalty (at low penetration of wind). 

Approximately 60% of the time, there is only a € 0.7/MWh volume fee for the imbalances so

usually the intra-day trade is not cost effective.

Additionally, 400 MW of hydropower could provide internal balancing to correct 83% of 

prediction errors for 200 MW of wind power. For 400 MW of wind power, 63% of the 

imbalances could be balanced internally. Using hydropower to balance wind power imbalances 

is profitable for both parties. Depending on the price set for the internal balancing and the wind 

power capacity, the balancing costs for the wind power producer would be reduced by roughly 

20–85%.

This study shows that even with the limited flexibility of hydropower (run-of-the-river with 

small reservoirs), a large part of wind power forecast errors can be provided for by shifting 

hydropower back and forth inside 1 day. This is because the wind power forecast errors average 

0, so both up- and down-regulation are used and the side of balancing varies from up to down 

and back frequently.

This study also shows that when correcting the forecast errors of wind power at a large balancing 

market in which hydropower produces most of the balancing (like in Nordic countries), there is 

not a great benefit of combining/integrating wind power and hydropower at a single producer. It 

is more cost effective to bid all flexibility of hydropower to the balancing market and use it from 

there to correct the system imbalances than to use it for dedicated balancing of wind power.
IEA Wind Task 24 Final Report, Vol. 2

58

4.3 Market Impacts for Nordic Countries

This section discusses the impacts of a wind- and hydro-dominated power system on the 

electricity markets and the characteristics of Nordic hydropower.

4.3.1 Introduction to Study

This study is part of a doctoral thesis that examines different ways to increase power system 

flexibility to decrease wind integration costs. Reservoir hydropower, where available, is often the 

cheapest method to increase system flexibility. The study also assesses the flexibility of reservoir 

hydropower in the Nordic system and tries to increase the accuracy and resolution of the 

hydropower description in the power system model.

The study was conducted with the WILMAR model, which was created with European Union

co-operation from Denmark, Finland, Germany, Norway, and Sweden. Juha Kiviluoma at VTT 

ran the model and made improvements considering reservoir hydropower. The model runs 

considered the Nordic power system in the year 2010 with different assumptions about wind 

power penetration.

This section is based on the following published reports:

• Kiviluoma, Juha and Hannele Holttinen, “Impacts of Wind Power on Energy Balance of a 

Hydro Dominated Power System,” European Wind Energy Conference, 27th February 

through 2nd March 2006, Athens, Greece.

• Kiviluoma, Juha; Peter Meibom and Hannele Holttinen, “Modelling of hydro and wind 

power in the regulation market,” Proceedings of Nordic Wind Power Conference, 22–23 May 

2006, Espoo, Finland.

In reference to the intended outcomes of the report relative to the objectives of Task 24, the 

market model WILMAR was used to model the behavior of the Nordic system with different 

wind power penetrations. The study analyzed the adequacy of hydropower to smooth the 

variability of wind power, what happens to spot prices when the system is dominated by hydro 

and wind power, and the use of transmission lines and conventional power plants due to 

increased wind power production. This work was part of Task 24 efforts on grid integration as 

well as markets and economics. The latter part of the research includes a separate river system 

model, including almost all hydropower plants and reservoirs in the Nordic countries. The most 

important limitations arising from chains of stations and reservoirs were taken into account. This 

river system model can be used to check the accuracy of dispatch from a more coarse market 

model, which has aggregated the hydropower plants into larger groups. The database for the 

hydropower plants and reservoirs also enabled a more accurate and detailed aggregation of 

hydropower in the market model. This work belongs to the grid integration part of Task 24 and 

could be extended to hydrological impacts in the future.
IEA Wind Task 24 Final Report, Vol. 2

59

4.3.2 Overview of Power System

Table 15. First half of the Task 24 matrix that defines important power system characteristics and 

some basic parameters of the setup for an integration study

Geographic Area of Study: Nordic countries (Denmark, Finland, Norway, and Sweden)

Power System Characteristics: The study was made for an estimated system at year 2010 with 

good interconnections and 47 GW of hydropower; nuclear and CHP production was important.

Load

(2010 estimated)

Conventional 

Generation Interconnection Wind Power

(base case)

Peak (MW) Min (MW) TWh/a Capacity (MW) Capacity (MW) MW TWh/a

74,000 31,000 420 93,000 2,360 6,600 16

Other Relevant Characteristics of Power System: n/a

Characteristics of System Planning: n/a

Description of Market: Day-ahead, intra-day, and regulation markets

Integration Time Frames of Importance: 

Yes/N

o

Time Frame

No Frequency, system dynamics, grid stability, LVRT, PSS, V-Reg

Yes Regulation 

Yes Load following; economic dispatch

Yes Unit commitment and day-ahead scheduling; economic utilization of resources

Yes Resource and capacity planning; reliability

The Nordic system gets 60% of electricity from hydropower, of which most have large 

reservoirs. The study analyzed wind power penetrations of 10%, 20%, and 30% in terms of 

annual energy demand. Analysis tried to examine whether or not there is enough load following 

capability available from the hydropower to deal with wind power variation. Since a significant 

amount of wind power was added and only little conventional capacity was retired, system 

resource adequacy was not an issue. Study also examined the effect of a large amount of low 

marginal price production on market prices. In the second study, participation of hydropower 

plants in the balancing market was also under assessment.

4.3.3 Study Methodology

Table 16. Second half of the Task 24 matrix that lists variations of underlying assumptions and 

modeling approaches used in wind integration studies

Set Up

A Aim of Study 1 what happens with x GWh wind

M Method to Perform Study 1 add wind energy

S Simulation Model of 

Operation 

3 Stochastic simulation several cases

Simulation Detail

R Resolution of Time 2 hour
IEA Wind Task 24 Final Report, Vol. 2

60

P Pricing Method 1 costs of fuels etc

D Design of Remaining 

System 

2 optimized remaining production

3 optimized remaining transmission

4 perfect trading rules

Uncertainty and Balancing

I Imbalance Calculation Not applicable in this study since forecasts are perfect and only 

load following capabilities are being investigated.

B Balancing Location 3 also outside region

U Uncertainty Treatment 2 hydro inflow uncertainty

Power System Details

G Grid Limit on Transmission 2 constant MW limits

H Hydropower Modeling 2 hydrological coupling included (including reservoir capacity)

3 some hydrological restrictions included

5 consider most relevant aspects of affected hydro resources

HC Hydro Capacity Service 2 real-time integration with impact of wind variability on system 

managed with hydro

T Thermal Power Modeling 2 start/stop costs considered

3 efficiency variation considered partially

4 heat production considered

W Wind Power Modeling 2 many wind power time series

3 time series smoothing considered

4.3.3.1 Assumptions

The price of fuel and power plant variable costs determine the merit order and 

scheduling/dispatch. Wind power was perfectly forecasted in these model runs since the purpose 

was to check the energy balance in the system.

4.3.3.2 Limitations

Hydropower modeling was aggregated in the simulation study because there are more than 2,000 

hydropower plants and more than 1,000 reservoirs in the Nordic system.

4.3.4 Wind Power Characteristics

Wind power penetration was set to 10%, 20%, and 30% in terms of annual energy demand in 

different cases for each country: Finland, Norway, and Sweden. The penetration in Denmark was 

kept at 4,600 MW. The time series for wind power is based on data from existing wind power 

plants as well as wind speed measurements, which have been smoothed and up-scaled to 

represent production from a larger amount of turbines in the future.

4.3.5 Hydro System Characteristics

There are numerous large hydropower reservoirs in the Nordic region, especially in Norway and 

Sweden. There are some run-of-the-river power and river reservoirs with low time constants, 

especially in Finland.
IEA Wind Task 24 Final Report, Vol. 2

61

4.3.6 Wind Power Penetration and System Flexibility

Wind power penetrations were 10%, 20%, and 30% in terms of annual energy demand. Most of 

the system is very flexible due to large amount of reservoir hydropower. Inflexibilities arise from 

transmission limits, nuclear units, and those combined heat and power (CHP) units that operate 

based on the heat load.

4.3.7 Wind and Hydro Integration – Benefits and Impacts

In relation to grid integration, reserves were adequate to enable a system with little else than 

wind power and hydropower. However, old units were not retired when wind power was added. 

WILMAR makes a reservation for spinning and non-spinning reserves. The latter is influenced

by the wind power output. The model does not provide data for actual intra-hour changes, but 

because the reserves were adequate and hydropower is rather flexible, it is expected that there 

would be no trouble to meet the additional intra-hour changes due to wind. At all times capacity 

was adequate, but old units were not retired in the study.

System-level change in the operation of the reservoirs was apparent in the results. Wind power 

production during the winter helped keep the reservoir levels higher, and a spring-summer-fall 

with higher than usual rains forced some water spillage. It becomes more difficult to keep water 

levels at optimum when there is an additional stochastic variation in the form of wind power.

Work was begun to better understand the restrictions arising from river systems, and while the 

data were improved, further work is still required. The Nordic hydropower plants were 

aggregated based on the river systems and the restrictions arising from reservoirs being far from 

the downstream stations. The results, while inconclusive, indicate that the flexibility of most 

plants is excellent as shown in Table 17.

Table 17. Top: Yearly mean production (GWh) from hydropower in Norway (NO) and Sweden (SE) 

divided into three groups that have clearly different operation principles; Bottom: Characteristics 

of hydropower plants in the third group, which have reservoir capacity upstream

Group NO south NO middle NO north SE south SE middle SE north

Pure Run-of-River (RoR) 1 130 1 070 501 150 412 70

Base load (>8000 hours) 2 840 464 492 - 2 810 7 780

Fully reservoir and partly RoR 79 800 21 600 9 330 746 13 100 47 600

NO south NO middle NO north SE south SE middle SE north

Power (MW) 19 969 4 790 2 140 183 2 873 11 329

Reservoirs (GWh) 53 000 16 400 7 730 7 3 520 24 200

Yearly mean production (GWh) 79 800 21 600 9 330 746 13 100 47 600

Share of RoR production (%) 10.3% 5.9% 9.1% 0.2% 3.5% 1.0%

Average time from reservoir to station 2.0 0.1 0.0 0.6 4.6 1.8

Average full load hours in full reservoirs 2 600 3 900 3 400 42 1 300 2 300

The most difficult situations for the system took place during the high-wind power production 

periods because some conventional units were unable to decrease their production further. This 

resulted in water spillage or wind shedding. This is likely to be a somewhat artificial situation 

since the flexibility of the CHP plants was neither fully included in the model, nor could the

flexibility be increased with electric resistance coils or steam bleed points.
IEA Wind Task 24 Final Report, Vol. 2

62

The second study analyzed hydropower in the balancing markets. Hydropower can be very 

practical in balancing the market because adjustments to the production can be made remotely 

and at short notice. The cost of balancing service for reservoir hydropower is dependent on the 

water value of the reservoir. This creates a cost curve for balancing services sourced from 

hydropower and is dependant on the size and number of different reservoirs and their filling 

status. It is further affected by the participation of different hydropower plants in the balancing

market and the restrictions arising from river systems.

In a system with large hydropower penetration, it becomes difficult to build a significant amount 

of wind power based solely on market economics. As wind power pushes marginal production 

out of the system, there will be a larger amount of hours when the marginal price is very low or 

zero. The formation of water values for reservoir hydropower will be based on more speculation 

and, as a result, price volatility will increase.

It appears that only a quarter of the Nordic hydropower plants are participating in the balancing

market. This could be due to the low prices and would correct itself if a larger share of wind 

power would raise the balancing prices. However, the reasons for the low participation should be 

investigated, and this could yield some insight on how to organize the markets more effectively.

4.3.8 Conclusions

A large penetration of wind power in a hydro-dominated power system will lower the spot price 

of electricity dramatically, which creates a challenge to get new investments in the system. It is 

unclear whether this kind of system could arise based on the markets even if it would be the most 

cost-effective way to serve load from a system perspective. It appears that the load following

capability of hydropower in the Nordic countries is large enough to support at least 30% wind 

energy penetration.

Because the Nordic system has thousands of hydropower plants and more than a thousand 

reservoirs, they have to be aggregated for a market model in order to keep the model solvable. 

The study aggregated hydropower based on a database of river systems and on analyses of the 

restrictions that river systems and reservoir sizes place on the use of hydropower. Results show 

that a large part of hydropower capacity should be capable of flexible operation.

Relative to these conclusions, the expected results of Wind Task 24 for this study are as follows:

• The study identified a practical system configuration of 60% of electricity from hydropower, 

most of which being reservoir hydropower, and 30% of electricity from wind power. Because

old power plants were not retired, there were no problems with system resource adequacy.

• A large penetration of wind power in a hydro-dominated power system will lower the spot 

price of electricity dramatically, which creates a challenge to get new investments in the 

system. It is unclear whether this kind of system could arise based on the markets even if it 

would be the most cost-effective way to serve load from a system perspective.

4.4 Market Impacts for Nordic Countries

This section discusses the impacts of a wind- and hydro-dominated power systems on the 

electricity markets and the characteristics of Nordic hydropower.
IEA Wind Task 24 Final Report, Vol. 2

63

4.4.1 Introduction to Study

This study is part of a doctoral thesis that examines different ways to increase power system 

flexibility to decrease wind integration costs. Reservoir hydropower, where available, is often the 

cheapest method to increase system flexibility. The study also assesses the flexibility of reservoir 

hydropower in the Nordic system and tries to increase the accuracy and resolution of the 

hydropower description in the power system model.

For this study, the model WILMAR was created with European Union co-operation from 

Denmark, Finland, Germany, Norway, and Sweden. The model runs and model improvements 

considering reservoir hydropower were made at VTT by Juha Kiviluoma. 

This study was performed because reservoir hydropower is one of the most economic ways to 

increase system flexibility and is of vital importance in the Nordic system.

This section is based on the following published reports:

• Kiviluoma, Juha; Peter Meibom and Hannele Holttinen, “Modelling of hydro and wind 

power in the regulation market,” Proceedings of Nordic Wind Power Conference, 22–23 May 

2006, Espoo, Finland.

• Kiviluoma, Juha and Hannele Holttinen, “Impacts of Wind Power on Energy Balance of a 

Hydro Dominated Power System,” European Wind Energy Conference, 27th February 

through 2nd March 2006, Athens, Greece.

In reference to the intended outcomes of the report relative to the objectives of Task 24, the 

market model WILMAR was used to model the behaviour of the Nordic system with different 

wind power penetrations. The study analyzed the adequacy of hydropower to smooth the 

variability of wind power, the effects of combined very large penetration of wind power and

hydropower on spot prices, and the use of transmission lines and conventional power plants due 

to increased wind power production. This work was part of Task 24 efforts on grid integration as 

well as markets and economics. Later research includes a separate river system model, including 

almost all hydropower plants and reservoirs in the Nordic countries. The most important 

limitations arising from chains of stations and reservoirs were taken into account. This river 

system model can be used to check the accuracy of dispatch from a more coarse market model, 

which has aggregated the hydropower plants into larger groups. The database for the hydropower 

plants and reservoirs also enabled a more accurate and detailed aggregation of hydropower in the 

market model. This work belongs to the grid integration part of Task 24 and could be extended to 

hydrological impacts in the future.
IEA Wind Task 24 Final Report, Vol. 2

64

4.4.2 Overview of Power System

Table 18. First half of the Task 24 matrix that defines important power system characteristics and 

some basic parameters of the setup for the second VTT (Finland) integration study

Geographic Area of Study: Nordic countries (Denmark, Finland, Norway, and Sweden)

Power System Characteristics: The study was made for an estimated system at year 2010 with 

good interconnections and lots of hydropower; nuclear and CHP production was important.

Load

(2010 estimated)

Conventional 

Generation Interconnection Wind Power

(base case)

Peak (MW) Min (MW) TWh/a Capacity (MW) Capacity (MW) MW TWh/a

74,000 420 93,000 2,360 6,600 16

Other Relevant Characteristics of Power System: n/a

Characteristics of System Planning: n/a

Description of Market: Day-ahead, intra-day, and regulation markets

Integration Time Frames of Importance: 

Yes/N

o

Time Frame

No Frequency, system dynamics, grid stability, LVRT, PSS, V-Reg

Yes Regulation, AGC

Yes Load following; intrahour ramping; economic dispatch

Yes Unit commitment and day-ahead scheduling; economic utilization of resources

Yes Resource and capacity planning; reliability

The Nordic system gets 60% of electricity from hydropower, most of which has large reservoirs. 

The study analyzed wind power energy penetrations of 10%, 20%, and 30% (based upon annual 

energy required to serve load). Analysts tried to identify whether or not there is enough 

regulation available from the hydropower to deal with wind power variation and forecast errors. 

The model had stochastic wind power presentation. Since a significant amount of wind power 

was added and only little conventional capacity was retired, system resource adequacy was not 

an issue. Study analysts also examined the effect of a large amount of low marginal price 

production on market prices. In the second study, participation of hydropower plants in the 

regulation market was also under assessment.

4.4.3 Study Methodology

Table 19. Second half of the Task 24 matrix that lists variations of underlying assumptions and 

modeling approaches used in the second VTT (Finland) case study

Set Up

A Aim of Study 1 what happens with a certain number of GWh of wind

M Method to Perform Study 1 add wind energy

S Simulation Model of 

Operation 

3 Stochastic simulation several cases

Simulation Detail

R Resolution of Time 2 hour
IEA Wind Task 24 Final Report, Vol. 2

65

P Pricing Method 1 costs of fuels etc

D Design of Remaining 

System 

2 optimized remaining production

3 optimized remaining transmission

4 perfect trading rules

Uncertainty and Balancing

I Imbalance Calculation Not applicable in this study since forecasts are perfect and 

only load following capabilities are being investigated.

B Balancing Location 3 also outside region

U Uncertainty Treatment 2 hydro inflow uncertainty

Power System Details

G Grid Limit on Transmission 2 constant MW limits

H Hydropower Modeling 2 hydrological coupling included (including reservoir capacity)

3 some hydrological restrictions included

5 consider most relevant aspects of affected hydro resources

HC Hydro Capacity Service 2 real-time integration with impact of wind variability on system 

managed with hydro

T Thermal Power Modeling 2 start/stop costs considered

3 efficiency variation considered partially

4 heat production considered

W Wind Power Modeling 2 many wind power time series

3 time series smoothing considered

4.4.3.1 Assumptions

The price of fuel and power plant variable costs determines the merit order and 

scheduling/dispatch. Wind power is deterministic in these model runs since the purpose was to 

check the energy balance in the system.

4.4.3.2 Limitations

Hydropower modeling was aggregated in the simulation study because there are more than 2,000 

hydropower plants and more than 1,000 reservoirs in the Nordic system.

4.4.4 Wind Power Characteristics

Wind power energy penetration was set to 10%, 20%, and 30% in different cases for each 

country: Finland, Norway, and Sweden. The penetration in Denmark was kept at 4,600 MW. The 

time series for wind power is based on data from existing wind power plants as well as wind 

speed measurements, which have been smoothed and up-scaled to represent production from a 

larger amount of turbines in the future.

4.4.5 Hydro System Characteristics

There are numerous large hydropower reservoirs in the Nordic region, especially in Norway and 

Sweden. There are some run-of-the-river power and river reservoirs with low time constants, 

especially in Finland.
IEA Wind Task 24 Final Report, Vol. 2

66

4.4.6 Wind Power Penetration and System Flexibility

Wind energy penetrations were 10%, 20%, and 30%, based upon annual energy required to serve 

load. Most of the system is very flexible due to large amount of reservoir hydropower. 

Inflexibilities arise from transmission limits, nuclear units, and those CHP units that operate 

based on the heat load. 

4.4.7 Wind and Hydro Integration – Benefits and Impacts

In relation to grid integration, reserves were adequate to enable a system with little else than 

wind power and hydropower. However, old units were not retired when wind power was added. 

WILMAR makes a reservation for spinning and non-spinning reserves. The latter is influenced 

by the wind forecast. The model does not provide data for actual intra-hour changes, but because

the reserves were adequate and hydropower is rather flexible, it is expected that there would be 

no trouble to meet the additional intra-hour changes due to wind. WILMAR schedules day-ahead 

and then reschedules every 3 hours according to new wind forecasts. At all times capacity was 

adequate, but old units were not retired in the study.

System-level change in the operation of the reservoirs was apparent in the results. Wind power 

production during the winter helped keep the reservoir levels higher, and a spring-summer-fall 

with higher than usual rains forced some water spillage. It becomes more difficult to keep water 

levels at optimum when there is an additional stochastic variation in the form of wind power.

Work was begun to better understand the restrictions arising from river systems, and while the 

data were improved, further work is still required. The Nordic hydropower plants were 

aggregated based on the river systems and the restrictions arising from reservoirs being far from 

the downstream stations. The results, while inconclusive, indicate that the flexibility of most 

plants is excellent as shown in Table 20.

Table 20. Top: Yearly mean production (GWh) from hydropower in Norway (NO) and Sweden (SE) 

divided into three groups that have clearly different operation principles; Bottom: Characteristics 

of hydropower plants in the third group, which have reservoir capacity upstream
IEA Wind Task 24 Final Report, Vol. 2

67

The most difficult situations for the system happened during the high-wind power production 

periods because some conventional units were unable to decrease their production further. This 

resulted in water spillage or wind shedding. This is likely to be a somewhat artificial situation 

since the flexibility of the CHP plants was neither fully included in the model nor could the 

flexibility be increased in the future rather cheaply with electric resistance coils or steam bleed 

points.

Hydropower can be very practical in regulating the market because adjustments to the production 

can be made remotely and at short notice. The cost of regulating service for reservoir 

hydropower is dependent on the water value of the reservoir. This creates a cost curve for 

regulation services sourced from hydropower and is dependent on the size and number of 

different reservoirs and their filling status. It is further affected by the participation of different 

hydropower plants in the regulation market and the restrictions arising from river systems.

In a system with large hydropower penetration, it becomes difficult to build a significant amount 

of wind power based solely on market economics. As wind power pushes marginal production 

out of the system, there will be a larger amount of hours when the marginal price is very low or 

zero. The formation of water values for reservoir hydropower will be based on more speculation 

and, as a result, price volatility will increase.

It appears that only a quarter of the Nordic hydropower plants are participating in the regulation 

market. This could be due to the low prices and would correct itself if a larger share of wind 

power would raise the regulation prices. However, the reasons for the low participation should be 

investigated, and this could yield some insight on how to organize the markets more effectively.

4.4.8 Conclusions

A large penetration of wind power in a hydro-dominated power system will dramatically lower 

the spot price of electricity, which creates a challenge to get new investments in the system. It is 

unclear whether this kind of system could arise based on the markets even if it would be the most 

cost-effective way to serve load from a system perspective. A price drop caused by increase in 

wind production in the Nordic countries can be mitigated with new transmission capacity to the

continental grid or with additional consumption possibly in the form of heat pumps, electric 

vehicles or hydrogen production. Also the functioning of the market mechanism can be 

questioned, when most of the power comes from sources with low marginal costs. It appears that 

the regulation capacity of hydropower in the Nordic countries is large enough to support at least 

30% wind energy penetration.

Because the Nordic system has thousands of hydropower plants and more than a thousand 

reservoirs, they have to be aggregated for a market model in order to keep the model solvable. 

The study aggregated hydropower based on a database of river systems and on analyses of the 

restrictions that river systems and reservoir sizes place on the use of hydropower. Results show 

that a large part of hydropower capacity should be capable of flexible operation.
IEA Wind Task 24 Final Report, Vol. 2

68

Relative to these conclusions, the expected results of Wind Task 24 for this study are as follows:

• The study identified a practical system configuration of 60% of electricity from hydropower, 

most of which being reservoir hydropower, and 30% of electricity from wind power. Because 

old power plants were not retired, there were no problems with system adequacy.

• A large penetration of wind power in a hydro-dominated power system will lower the spot 

price of electricity dramatically, which creates a challenge to get new investments in the 

system. It is unclear whether this kind of system could arise based on the markets even if it 

would be the most cost-effective way to serve load from a system perspective.
IEA Wind Task 24 Final Report, Vol. 2

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5 Norway

5.1 Introduction

Because the wind resource in Norway is well correlated with the load, and due to the large 

amount of hydropower generation that is present, the Foundation for Scientific and Industrial 

Research at the Norwegian Institute of Technology (SINTEF) has investigated the ability to 

integrate wind and hydropower. For the purpose of the Task 24 research, two case studies were

contributed:

1. The first study looked at wind power in areas with limited power transfer capacity and 

subject to grid congestion. The question to be addressed here was to see how much wind 

power could be integrated without deleteriously affecting the hydropower production. 

2. The second case study considers the impact of wind power on system resource adequacy. 

Considering that the region has favourable wind resources, the study was conducted to 

determine whether or not adding wind power to the hydro-based system will be sufficient 

or if additional measures must be taken to secure system adequacy.

Each of these case studies will be described in the sections to follow.

5.2 SINTEF 1: Areas with Limited Power Transfer Capacity

5.2.1 Introduction to Study

When planning wind power in areas with limited power transfer capacity, conservative 

assumptions may lead to unnecessarily strict limitations on the possible wind installation. By 

introducing Automatic Generation Control (AGC) and coordinated power system operation, a

large increase in installed wind power is viable.

The purpose of this study was to assess grid integration of large wind farms subject to grid 

congestions. Emphasis was put on how different control strategies for handling congestion 

situations affect the operation and economics of the studied regional power system. When 

assessing the impact of wind power on the power system operation, it is necessary to take into 

account the stochastic and dispersed nature of wind power. This study and previous studies have 

shown that in the Nordic region, the periods with highest wind generation typically appear in the 

winter season when the consumption also is high, which has a positive impact on utilization of 

the existing transmission capacity. Moreover, this study shows that the power smoothing effect 

of geographically dispersed wind farms gives a significant reduction of discarded wind energy in 

constrained networks, compared to a single up-scaled wind farm site.

The specific case study presented consists of a regional power system with assumed 420-MW 

power transfer capacity. With an existing hydropower installation of 380 MW, and 75 MW 

minimum local, the most conservative approach limits the total wind power installation to 

115 MW. By using the developed methodology, the wind power capacity can be increased from 

115 MW to at least 600 MW without any noticeable income reduction from energy sales 

(compared to a case with unlimited grid capacity). The case study was presented at the European 

Wind Energy Conference 2006 (Korpås et al. 2006).
IEA Wind Task 24 Final Report, Vol. 2

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5.2.2 Overview of Power System 

Table 21. First half of the Task 24 matrix that defines important power system characteristics and 

some basic parameters of the setup for the first SINTEF (Norway) integration study

Geographic area of study: A generalized regional power system partly based on the 

characteristics of Northern part of Norway.

Power system characteristics: 

Load Conventional 

Generation Interconnection Wind Power

Peak (MW) Min (MW) TWh/a Capacity (MW) Capacity (MW) MW TWh/a

350 75 1.5 Hydro: 380 420 0–1,000 0–3.5

Other relevant characteristics of power system: 100% hydropower, limited export/import 

capacity, very good wind conditions, very low population density.

Characteristics of system planning: Transmission capacity planning is done by transmission 

system operators in the Nordic power system. Energy companies invest in power plants.

Description of market: Most of the power in Norway is traded at NordPool. NordPool has a 

spot market and a financial market. The total production in the Nordic area (Norway, Sweden, 

Denmark, Finland) was 395 TWh while the volume traded at the Elspot market was 176 TWh in 

2005.

Integration time frames of importance: 

Yes/No Time Frame

No Frequency, system dynamics, grid stability, LVRT, PSS, V-Reg

Yes 

(hourly)

Regulation, AGC

No Load following; intra-hour ramping; economic dispatch

Yes Unit commitment and day-ahead scheduling; economic utilization of resources

No Resource and capacity planning; reliability

Hydro power plants

380 MW, 760 GWh reservoir

Wind farms

Local loads: 75-350 MW

420 kV main transmission

132 kV corridor

Max transfer capacity 420 MW

AGC

Figure 20. Overview of the case study power system in which AGC is regarded for keeping the 

power transmission below the maximum export capacity of 420 MW

The studied system shown in the Figure 20 is a large, 132-kV regional power system connected 

to the 420-kV main grid by several 132-kV lines with an assumed total export capacity of 

420 MW. For simplicity, the grid capacity is represented as a constant value for transmission of 

active power, although the actual capacity depends on several other factors such as reactive 

power transfer, voltage stability margins, and steady-state voltage limits. Several hydropower 
IEA Wind Task 24 Final Report, Vol. 2

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plants, with a total generation capacity of 380 MW, are connected to the regional grid. The 

hydropower plants are represented as an aggregated power plant with 760 GWh of reservoir 

capacity. The water inflow to the hydropower plant is divided into storable (1325 GWh/year) and 

non-storable inflow (790 GWh/year). The non-storable inflow is used directly for run-of-theriver power generation. 

As shown in Figure 20, AGC is regarded for keeping the power transmission below the 

maximum export capacity of 420 MW. It is assumed that AGC is applied to following two

different control strategies:

• Using control wind: The power output of the wind farms is constrained, if required. The

hydropower plant is operated according to a generation schedule that is unaffected by the 

wind power output.

• Using control hydro: The output of the hydropower plant is decreased as much as possible to 

prevent overloading of the grid. If this is not sufficient, the wind power output is constrained 

as for control wind. 

The hydropower is increased above the generation schedule at a later stage to keep the annual 

hydro generation as close to the schedule as possible.

Since part of the hydropower is generated from a non-storable inflow of water, it is seldom 

possible to reduce the power output to zero.

5.2.3 Study Methodology

A simulation model of the regional power system has been implemented in MATLAB. To run 

the simulations of the regional power system, a 30-year time series with hourly resolution has 

been constructed for the following time-varying parameters:

• Normalized wind power output (non-congested) from three wind farms 

• Electricity consumption

• Storable inflow

• Non-storable inflow

• Scheduled hydro generation

• Electricity market price

In constructing a wind power time series for each wind farm site, a common 30-year wind speed 

series with weekly resolution has been combined with the 1-year wind speed series with hourly 

resolution. The weekly wind speed series is scaled to give a 30-year average of 10.5 m/s. The 

1-year time series is normalized and multiplied by the weekly wind speed averages to give an 

8760-hour by 30-year matrix of wind speed, which is converted to power by using a typical wind 

turbine power curve. The total hourly wind generation is simply calculated as the sum of power 

generation from the three wind farms. 

The other time series listed above have been constructed by using the Multi-Area Power Market 

Simulator (EMPS) model (see www.sintef.no/Home/SINTEF-Energy-
IEA Wind Task 24 Final Report, Vol. 2

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Research/Expertise/Hydro-thermal-operation-and-expansion-planning/EMPS/), a commercial 

model developed at SINTEF Energy Research in Norway for hydro scheduling and market price 

forecasting. This is a complex, stochastic optimization model that simulates the optimal 

operation of hydropower resources in a region with a stochastic representation of inflow to the 

hydropower stations and a number of physical constraints taken into account. The electricity 

consumption has been modeled as temperature-dependent, causing some yearly variations. Longterm increase in consumption has not been considered. An EMPS simulation of the Nordic 

power system has been run without wind power in the area of interest to provide a basis for the 

hydropower scheduling as well as the electricity market price.

It is possible to use EMPS to simulate the Nordic power system with geographically dispersed 

wind power, especially to assess the value of wind power in the electricity market and to 

determine the effects of large-scale wind power integration on optimum long-term hydro 

scheduling. In this case, on the other hand, EMPS is less suitable mainly due to the low time 

resolution of the EMPS model (1 week) and the limited flexibility of defining control strategies 

for wind-hydro coordination in an area with considerable transmission constraints.

Since the time resolution of the output from EMPS is 1 week, the hour-to-hour variations of 

consumption, inflow, hydro generation, and price have to be synthetically generated. The hourly 

values of the consumption and hydro generation have been constructed as products of the weekly 

average values and typical diurnal variations observed in the Nordic power system. The hourly 

values of the other parameters (storable inflow, non-storable inflow, and price) are simply 

constructed by interpolating the weekly values. 

Table 22. Second half of the Task 24 matrix that lists variations of underlying assumptions and 

modeling approaches used in the first SINTEF (Norway) wind integration studies

Set Up

A Aim of Study 2 – How much wind is possible to integrate in a power system 

with limited transfer capacity (also analyzing the benefit of windhydro coordination)

At many locations with excellent wind conditions, grid issues 

hinder wind farm development. Conservative assumptions are 

often applied that unnecessarily limit the wind power 

installation. This work shows that significantly more wind power 

can be allowed by taking proper account for the wind power 

characteristics and facilitating coordinated power system 

operation. The purpose is to assess grid integration of large 

wind farms subject to grid congestions. Emphasis is put on how 

different control strategies for handling congestion situations 

affect the operation and economics of the studied regional 

power system.

M Method to Perform Study 1 – Add wind energy

In the case study, two separated simulations are performed. 

First, the EMPS-model is run for the whole Nordic system to 

obtain the weekly planned hydropower production and weekly 

market prices. Then, the regional system is simulated from 

hour-to-hour with wind power where the hydropower production 

can be adjusted to take into account wind power variations.
IEA Wind Task 24 Final Report, Vol. 2

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S Simulation Model of 

Operation 

2 – Deterministic simulation for wind power impact (main 

simulation) 

4 – Stochastic simulation for hydro planning and power market 

prices (pre-simulation)

Simulation Detail

R Resolution of Time 1 week for hydro planning and power market prices (presimulation)

2 hours for impact of wind power (main simulation)

P Pricing Method 3 – Market actor simulation (pre-simulation)

Wind power influence on price is not considered

D Design of Remaining 

System 

1 – Constant remaining system

Uncertainty and Balancing

I Imbalance Calculation No imbalance calculation

B Balancing Location No imbalance calculation

U Uncertainty Treatment 2 – Hydro inflow uncertainty

Taken into account in Hydro planning and power market prices 

(pre-simulation)

Wind and load forecasting is not part of the analysis 

Power System Details

G Grid Limit on Transmission 2 – Constant MW limits

H Hydropower Modeling Hydro planning and power market prices (pre-simulation):

1 – Head height considered

2 – Hydrological coupling included (including reservoir capacity)

3 – Hydrological restrictions included

4 – Hydro optimization considered

5 – Consider most relevant aspects of affected hydro resources

Impact of wind power (main simulation)

3 – Hydrological restrictions included (reservoir level, stream 

flows)

5 – Consider most relevant aspects of affected hydro resources

HC Hydro Capacity Service 2 – Real-time integration with impact of wind variability on 

system managed with hydro

3 – Capacity service: addition of capacity value through 

redelivery of wind energy at a later time

T Thermal Power Modeling Hydro planning and power market prices (pre-simulation): 

4 – Heat production considered (thermal power in the Nordic 

system)

Impact of wind power (main simulation):

Thermal power is not considered, since there are no thermal 

power plants in the specific region

W Wind Power Modeling 1 – Few wind speed time series (three different locations)

3 – Time series smoothing considered
IEA Wind Task 24 Final Report, Vol. 2

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5.2.3.1 Assumptions and Limitations

A 30-year time series with hourly resolution has been constructed for wind speed at three 

locations by combining 1-year time series with a 30-year time series. The smoothed power 

output is simulated as the sum of the output from the three locations using a typical power curve.

The 30-year time series for load, hydro inflow, planned hydro generation, and market price are 

constructed by using the EMPS model. The influence of wind power on the market price is not 

considered.

5.2.3.2 Wind Power Characteristics 

In the case study system, the possible wind farm sites were spread over a large area. Since the 

instantaneous wind speed varies at different wind farm locations, it is important to consider the 

smoothing effect this has on the total wind power in-feed to the regional grid. A 1-year time

series for wind speed at three different sites was collected to assess the smoothing effect. The 

time resolution was 1 hour, and the distance between the measurement sites was more than 

100 km. The hourly wind farm power output at the three locations was calculated simply 

assuming a typical wind turbine power curve.

The installed wind power capacity varied from 0 MW to 1,000 MW. With the chosen wind speed 

time series and power curve, the number of full load hours for wind power generation in the area 

was 3,500 hours.

0 2 4 6 8 10

0.05

0.1

0.15

0.2

0.25

0.3

# of wind farms

Std of delta wind power (pu)

Estimate

Observation

0 0.2 0.4 0.6 0.8 1

0

0.2

0.4

0.6

0.8

1

Wind power (p.u.)

Pr(Wind power <= x)

1 site

3 sites

Figure 21. Left Graph: Standard deviation of the hourly wind power variations for individual wind 

farms and for the sum of generation for one to three wind farms; Right Graph: Cumulative 

probability distribution of wind power (i.e., the probability that the wind power is less than or 

equal to a specific value)

5.2.4 Hydro System Characteristics 

In the hourly simulations, the hydro system is modeled as an aggregated power plant with 

380-MW power capacity and 760-GWh reservoir capacity. The hydropower plant is operated 

according to the control strategies described in Section 5.2.2, Overview of Power System. 
IEA Wind Task 24 Final Report, Vol. 2

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0 2000 4000 6000 8000

0

20

40

60

80

100

Time (h)

Reservoir content (%)

0 5 10 15 20 25 30

0.7

0.8

0.9

1

1.1

1.2

1.3

Time (yr)

Normalised annual generation

max

avg

mean

hydro

wind

Figure 22. Left graph: Hydro reservoir level for the case with no wind power. Right graph: Annual 

variations in wind and hydro generation

5.2.5 Wind Power Penetration and System Flexibility 

The installed wind power varied from 0 MW to 1,000 MW, which corresponds to 0 TWh to 3.5 

TWh of annual generation. With a 350-MW peak load and 1.5-TWh annual load, the wind power 

penetration and wind energy penetration varies from 0–286% and 0–233%, respectively. In 

addition to serving the local load, it is possible to export up to 420 MW to the main transmission 

grid. 

5.2.6 Wind and Hydro Integration – Benefits and Impacts

The most conservative approach allows for only 115 MW of wind power in the constrained 

network with 420 MW of capacity, as this will not require any control actions even in the very 

unlikely case of maximum wind and hydro generation (115 MW + 380 MW) at the same hour as 

the historically lowest consumption (75 MW).

A somewhat less conservative approach is to calculate the hourly power export for the reference 

case (no wind power) for each hour and use the maximum value to determine the acceptable 

wind power installation. It is found from the 30-year time series data that the maximum power 

export without wind power is 237 MW, making it possible to install 183 MW of wind power 

(420 MW to 237 MW) without affecting the power system operation. However, the viable 

amount of wind power that can be installed is expected to be much higher, not only because of 

the smoothing effect of geographically dispersed wind farms, but also because the periods with 

highest wind generation typically occurs in winter, when the consumption also is at its highest. 

Since the hydro inflow occurs mostly during summer, this wind characteristic is beneficial for 

the system operation.

With the chosen input data, it is found that the maximum wind power installation that does not 

lead to any rescheduling of hydro generation or dissipation of wind energy is 200 MW. This 

result is valid for three equally sized wind farms (each at 66.67 MW). If the wind power 

installation exceeds this limit, control actions must be performed to prevent overloading of the 

132-kV lines.

The left graph in Figure 23 shows the cumulative distribution function of wind power for the two 

proposed control strategies (see Section 5.2.2, Overview of Power System), in addition to a noncongested case in which the system has been simulated with unlimited transmission capacity. 

This illustrates that the control wind strategy gives some dissipation of wind energy. Moreover, it 
IEA Wind Task 24 Final Report, Vol. 2

76

is evident that with the control hydro strategy, it is possible to utilize almost all the available 

wind energy. 

The right graph in Figure 23 shows the cumulative distribution function of hydropower. The 

curve for control wind represents the scheduled hydro generation, and the graph illustrates that 

the control hydro strategy causes the power output to fluctuate more, as expected. However, the

areas between the curves and the y-axis are almost the same, which means that the annual water 

spillage is insignificantly increased when coordinated wind/hydro control is introduced.

0 100 200 300 400 500 600

0

0.2

0.4

0.6

0.8

1

Pw (MW)

Pr(Pw <x)

control wind

control hydro

non-congested

0 100 200 300 400

0

0.2

0.4

0.6

0.8

1

Ph (MW)

Pr(Ph<x)

control wind

control hydro

Figure 23. Cumulative distribution function of wind power (Pw) and hydropower (Ph) for three 200-

MW wind farms

The annual income from the electricity market is simply calculated by multiplying the sum of 

hourly wind + hydro generation with the hourly electricity price (the hourly price is found by 

interpolation of the weekly values from the EMPS model). Reduction of power output due to the 

transmission constraint of 420 MW reduces the income compared to the ideal non-congested 

case. Figure 24 shows the relative reduction in income as the wind power installation is increased 

for the two control strategies. With control hydro and 600 MW of wind power, the average 

yearly reduction in income is only 1%, while the control wind strategy gives 3% reduction. It is 

observed from the upper graph that a wind power installation up to 400 MW gives no income 

reduction due to transmission constraints if allowance is made for coordinated wind/hydro 

operation. For 600 MW of wind power, the annual income will be in the range of 97% to 100%

and 95% to 99% of the non-congested case for control hydro and control wind, respectively.
IEA Wind Task 24 Final Report, Vol. 2

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0 100 200 300 400 500 600 700 800 900 1000

80

85

90

95

100

control hydro

0 100 200 300 400 500 600 700 800 900 1000

80

85

90

95

100

Installed wind power (MW)

Income (% of non-congested)

control wind

max

avg

min

Figure 24. Annual income (wind+hydro) from energy sales to electricity market relative to the noncongested case (max, min, and avg plots refer to the 30-year samples used in this study)

5.2.7 Conclusions

The study shows that for the specific system studied, up to 600 MW of wind power is possible—

without noticeable reduction in income from energy sales compared to an ideal non-congested 

case—by applying coordinated operation of the wind power and hydropower plants. The results 

emphasize that this is achieved for a hydropower system with a relatively small reservoir and a 

high share of non-storable water inflow (37% of the total storable plus non-storable inflow). 

Even if the local hydropower plant follows the generation schedule unaffected by wind power, 

the reduction in income due to discarded wind energy is as low as 1% to 5%, depending on the 

annual wind speed and water inflow.

Power system coordination allows for surprisingly large amounts of wind power. It is essential to 

take account for power system flexibility and the stochastic and dispersed nature of wind power. 

The presented methodology facilitates this and represents a rational approach for power system 

integration of wind farms in areas with limited transfer capacity.

5.3 SINTEF 2: Regional Hydro-based Power System with Weak Interconnections

5.3.1 Introduction to Study

This case study considers the impact of wind power on system resource adequacy. The impact is 

assessed using data from a real-life, regional, hydro-based power system, although data are 

simplified and fitted for the purpose of the work. The region has a predicted need for new 

generation and/or reinforcement of interconnections to meet future demand. Considering that the 

region has favourable wind resources, the study was conducted to determine whether or not 

adding wind power to the hydro-based system will be sufficient or if additional measures must 

be taken to secure system adequacy.
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System adequacy relates to the ability of the system to meet the load demand. In this paper, this 

is addressed considering (1) the system’s ability to supply the annual load and (2) the system’s 

ability to meet the peak demand. The system’s ability to supply the annual load is assessed using 

30 years of recorded data of hydro inflow and wind speed. The system operation is simulated to 

quantify annual energy balance within the region, including hydro, wind, and import/export 

through interconnections with neighbouring regions. 

The system ability to meet the peak demand is assessed by calculating the loss of load 

probability (LOLP) for the system. The calculation takes account for the installed generation and 

transmission capacity, the probability of outages, and the probability of wind power generation at 

the hour of peak demand. The case study was presented at Nordic Wind Power Conference 

(NWPC) 2006 as the following: Tande J O, Korpås M, 2006. Impact of large-scale wind power 

on system adequacy in a regional hydro-based power system with weak interconnections. 

NWPC, Espoo, Finland, 22–23 May 2006

5.3.2 Overview of Power System 

Table 23. First half of the Task 24 matrix that defines important power system characteristics and 

some basic parameters of the setup used in the second SINTEF (Norway) integration study

Geographic area of study: A generalized regional power system partly based on the 

characteristics of Mid-Norway. Years: 2006 and future (2010+)

Power system characteristics: 

Load Conventional 

Generation

InterConnection Wind Power

Peak (MW) Min (MW) TWh/a Capacity (MW) Capacity 

(MW) MW TWh/a

3,180 (now)

3,780 (future)

750 (now)

900 

(future)

18 (now)

21 (future)

Hydro: 2,250

Gas: 375 1,600

62 (now)

1,062 

(future)

0.2 

(now) 3 

(future)

Other relevant characteristics of power system: The load is expected to increase significantly 

in the future, and new investments in generation and/or transmission are required to meet the 

load demand. Today, the generation capacity consists almost entirely of hydropower, but there 

are plans for building new wind power plants and gas power plants.

Characteristics of system planning: Transmission capacity planning is done by transmission 

system operators in the Nordic power system. Energy companies invest in power plants.

Description of market: Most of the power in Norway is traded at NordPool. NordPool has a 

spot market and a financial market. The total production in the Nordic area (Norway, Sweden, 

Denmark, Finland) was 395 TWh, while the volume traded at the Elspot market was 176 TWh 

in 2005.

Integration time frames of importance: 

Yes/No Time Frame

No Frequency, system dynamics, grid stability, LVRT, PSS, V-Reg

No Regulation, AGC

No Load following; intra-hour ramping; economic dispatch

Yes Unit commitment and day-ahead scheduling; economic utilization of resources

Yes Resource and capacity planning; reliability
IEA Wind Task 24 Final Report, Vol. 2

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This case study uses data from a real-life, regional, hydro-based power system. The system is 

running on a tight balance, and measures must be taken for meeting a future load increase. This 

is assessed assuming three alternatives: new wind generation (Case A), new gas generation (Case 

B), and a combination of the two (Case C). 

18 TWh annual load / 3180 MW max load

Increasing to 21 TWh / 3780 MW

13 TWh hydro / 2250 MW (6x375 MW)

Total import capacity 

14 TWh / 1600 MW (4x400 MW) 

0,18 TWh wind / 62 MW (3 wind farms)

18 TWh annual load / 3180 MW max load

Increasing to 21 TWh / 3780 MW

13 TWh hydro / 2250 MW (6x375 MW)

Total import capacity 

14 TWh / 1600 MW (4x400 MW) 

0,18 TWh wind / 62 MW (3 wind farms)

Figure 25. Overview of the case study power system

Table 24. Case study annual load and generation

Base A B C

Annual load (GWh) 18,024 21,024 21,024 21,024

Hydro (GWh) 12,928 12,928 12,928 12,928

Wind (GWh) 186 3,186 186 3,186

Gas (GWh) 0 0 3,000 3,000

Import (GWh) 4,909 4,909 4,909 1,909

Wind energy penetration (%) 1.0 15.2 0.9 15.2

Table 25. Case study max load and generating capacity

Base A B C

Max load (MW) 3,180 3,780 3,780 3,780

Hydro (MW) 2,250 2,250 2,250 2,250

Wind (MW) 62 1,062 62 1,062

Gas (MW) 0 0 375 375

Import (MW) 1,600 1,600 1,600 1,600

Table 26. Unit sizes and availability factors

Wind Gas Hydro Lines

Unit size (MW) 3 375 375 400

Availability (%) 99 99.5 99.5 99.5
IEA Wind Task 24 Final Report, Vol. 2

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5.3.3 Study Methodology

5.3.3.1 Energy Balance Calculations

The system’s ability to supply the annual load was assessed using results from EMPS 

simulations. The EMPS-model includes data for the Western European power system divided 

into 34 interconnected areas. Norway is described in most detail, including about 800 hydro 

modules grouped into 12 areas, with one of these areas representing the regional power system of 

this case study. The system operation is simulated for the base case year on a week-by-week 

basis, and repeated 30 times for considering 30 years of recorded temperature, wind and hydro 

inflow data (1961–1990). For cases A, B and C, it is assumed (for simplicity) that the hydro 

generation and prices will remain in the same as the base case, so these cases are assessed 

without rerunning the EMPS model. The load and wind are simply scaled up based on data 

already included in the EMPS model, and gas is added by assuming constant generation (8,000 

full load hours)

5.3.3.2 Power Balance Calculations

The loss of load probability, LOLP = Pr (Pm < 0), is calculated by using standard statistical 

methods as briefly described below. The generating capacity margin, Pm, is the difference 

between the available conventional capacity, Pc, and the net load, Pn.

The generating capacity margin distribution is calculated as the convolution of the available 

conventional capacity distribution and the net load distribution (i.e., no correlation between the 

available conventional generating capacity and the net load in the peak hour is assumed). The net 

load distribution is calculated as the convolution of the wind power distribution and the 

consumers load distribution (i.e., no correlation between the wind power variations and the 

consumers’ load within the peak hour is assumed). 

The wind power distribution from each group is calculated by a two-step procedure. First, the 

wind power distribution from one 100% available wind turbine is calculated from a time series 

of the hour-to-hour wind speed variations and a typical wind turbine power curve. This approach 

makes it convenient to take into account the smoothing effect of geographically distributed wind 

power. Next, the wind power distribution from the number of wind turbines is calculated as the 

convolution of the wind power distribution of the “ideal” wind turbine and the binomial 

distribution of the available wind turbines.
IEA Wind Task 24 Final Report, Vol. 2

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Table 27. Second half of the Task 24 matrix that lists variations of underlying assumptions and 

modeling approaches used in the second (Norway) wind integration studies

Set Up

A Aim of Study

1 – What happens with 1,000 MW wind with respect to the 

system adequacy in a regional power system with limited 

import capacity

M Method to Perform Study

1 – Add wind energy

2 – Wind also replaces capacity (or reduces the need for future 

capacity expansion)

The system’s ability to supply the annual load is assessed using 

results from the EMPSmodel with 30 years of recorded data of 

hydro inflow and wind speed. The system operation is 

simulated to quantify annual energy balance within the region, 

including hydro, wind, and import/export through 

interconnections with neighboring regions. 

The system’s ability to meet the peak demand is assessed by 

calculating LOLP for the system. The calculation takes account 

for the installed generation and transmission capacity, the 

probability of outages, and the probability of wind power 

generation at the hour of peak demand.

S Simulation Model of 

Operation 

EMPS simulations:

3 Stochastic simulation several cases

Capacity credit calculations:

Probability density functions are created for load, wind speed 

and availability of wind generation, conventional generation, 

and transmission lines.

Simulation Detail

R Resolution of Time 1 week (energy balance)

2 hour (capacity credit)

P Pricing Method 3 – Perfect market simulation (Base case for energy balance 

calculations from EMPS simulations)

D Design of Remaining 

System 

1 – Constant remaining system

2 – Optimized remaining production capacity (avoided new 

conventional generation)

3 – Optimized remaining transmission (avoided grid 

reinforcements)

Uncertainty And Balancing

I Imbalance Calculation No imbalance calculation

B Balancing Location No imbalance calculation

U Uncertainty Treatment 

Uncertainty treatment in EMPS simulations:

2 – Hydro inflow uncertainty

Uncertainty treatment in capacity credit calculations:

6 – Thermal power outages considered: Availability of all 

generator types are considered

Uncertainty of wind power and maximum load are modeled by 

using probability density functions

Power System Details

G Grid Limit on Transmission 2 – Constant MW limits

4 N-1 criteria
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H Hydropower Modeling 

Hydropower modeling in EMPS model:

1 – Head height considered

2 – Hydrological coupling included (including reservoir capacity)

3 – Hydrological restrictions included (reservoir level, stream 

flows)

4 – Availability of water, capacity factor, dry/wet year

HC Hydro Capacity Service 1 – Interaction with hydro resources not significant (Hydro 

production is not changed due to wind power)

T Thermal Power Modeling Thermal power modeling in EMPS model:

4 – Heat production considered 

W Wind Power Modeling 1 – Few wind speed time series (3 different locations)

3 – Time series smoothing considered

5.3.3.3 Assumptions and Limitations

Energy balance calculations:

• A base case is defined from EMPS simulations (30 years with load and hydro inflow).

• Weekly wind (30 years time series) and gas power added to the base case. The gas power 

plant is assumed to operate at nominal power.

Capacity credit calculations:

• Use 1-year time series from three locations with hourly resolution. The smoothed power 

output is simulated as the sum of the output from the three locations using a typical power 

curve. This time series is used for creating a probability density function for the wind power 

output in the maximum load hour (assuming that the average wind speed in the maximum 

load hour is equal to the average wind speed of the whole year)

5.3.4 Wind Power Characteristics 

The possible wind farm sites are spread over a large area. Since the instantaneous wind speed 

varies at different wind farm locations, it is important to consider the smoothing effect this has 

on the total wind power in-feed to the regional grid. One-year time series for wind speed at three 

different sites was collected to assess the smoothing effect. The time resolution was 1 hour, and 

the distance between the measurement sites was more than 100 km. The hourly wind farm power 

output at the three locations was calculated simply assuming a typical wind turbine power curve.

Figure 26 shows the cumulative distribution function of wind power with and without wind 

power smoothing. It is observed that the probability of zero power output is almost 20% for a 

single wind farm, while the corresponding value for the sum power output from three wind farms 

is less than 5%. Therefore, the smoothing effect could have a positive impact on the contribution 

of wind power to meet the maximum load. The smoothing effect is also evident for the periods 

with high wind speed; the three wind farms generate at full power at the same time less than 1%

of the year, compared to 10% of the year for a single wind farm.
IEA Wind Task 24 Final Report, Vol. 2

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0 200 400 600 800 1000

0

0.2

0.4

0.6

0.8

1

Wind power (MW)

CDF of wind power

Simple scaling of wind production

Summation of three wind farms

Figure 26. Cumulative distribution function of wind power for Case A

With the chosen wind speed time series and power curve, the number of full load hours for wind 

power generation in the area is 3,000 hours.

5.3.5 Hydro System Characteristics 

See Section 5.3.3, Study Methodology, for a description on how hydropower is modelled for the 

energy balance calculations. For the power balance calculations, hydropower is modelled as a 

conventional generator type with 99.5% availability and unit size of 375 MW. 

5.3.6 Wind Power Penetration and System Flexibility

The installed wind power is 62 MW (Case B) and 1,062 MW (Case A and Case C), which 

corresponds to 186 GWh and 3,186 GWh of annual generation. With a 3,780-MW peak load, the 

wind power penetration becomes 1.6% (Case B) and 28.1% (Case A and Case C). The annual 

load is 21,024 GWh, which gives wind energy penetration levels of 0.9% (Case B) and 15.2% 

(Case A and Case C).

5.3.7 Wind and Hydro Integration – Benefits and Impacts

As illustrated in the left graph in Figure 27, the average week-by-week wind and load follow the 

same pattern, which is opposite to the hydro inflow. This is beneficial, although variations from 

year to year may be significant. The year-to-year variations in load, wind, and hydro generation 

are provided in the right graph in Figure 27.

0

1

2

3

4

5

6

7

1 6 11 16 21 26 31 36 41 46 51

Week of year

% of annual

Load

Wind

Hydro

0

20

40

60

80

100

120

140

1961 1966 1971 1976 1981 1986

Year

Normalized data (%) 

Hydro

Wind

Load

0

1

2

3

4

5

6

7

1 6 11 16 21 26 31 36 41 46 51

Week of year

% of annual

Load

Wind

Hydro

0

20

40

60

80

100

120

140

1961 1966 1971 1976 1981 1986

Year

Normalized data (%) 

Hydro

Wind

Load
IEA Wind Task 24 Final Report, Vol. 2

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0

1

2

3

4

5

6

7

1 6 11 16 21 26 31 36 41 46 51

Week of year

% of annual

Load

Wind

Hydro

Figure 27. Left graph: Average seasonal pattern for load, wind, and hydro inflow

Right graph: Annual variations in load (due to temperature), wind, and hydro generation

For the power balance calculations within each week modelled by EMPS (which does take into 

account hydro constraints, availability, inflow, head height, etc.), hydropower is modelled as a 

conventional generator, and therefore no specific hydro characteristics are taken into account, 

such as reservoir and inflow. The remainder of this section provides a short summary of how 

wind power will impact system adequacy in the case study system.

Figure 28 shows the cumulative distribution of the capacity margin for the different cases (see 

Section 5.3.2, Overview of Power System, for an explanation of the cases). Wind power 

variations result in a higher variability of the capacity margin. By comparing Case B (gas) and 

Case C (gas plus wind), it is evident that wind power significantly improves the generating 

capacity margin.

-500 0 500 1000 1500

0

0.2

0.4

0.6

0.8

1

Generating capacity margin (MW)

CDF of generating capacity margin

Base

Case A

Case B

Case C

Figure 28. Cumulative distribution function of the generating capacity margin

The geographical distribution of wind power in the area was taken into account by adding up the 

production from three representative wind farms that are geographically separated by up to 

100 km. This has a positive effect on the ability of the system to meet the maximum load 

compared to a case with only one wind farm site and the same installed capacity. These cases are 

compared in the graphs in Figure 29, which clearly shows that the smoothing effect becomes 
IEA Wind Task 24 Final Report, Vol. 2

85

more important as wind penetration increases. Capacity value is defined here as the amount of 

wind power that can be counted upon to meet peak load. 

0 200 400 600 800 1000

0

50

100

150

Installed wind power (MW)

Capacity value (MW)

a)

0 200 400 600 800 1000

10

20

30

40

Installed wind power (MW)

Capacity value (%)

b)

0 2 4 6 8 10 12 14

10

20

30

40

Penetration level (%)

Capacity value (%)

c)

Simple scaling of wind production

Summation of three wind farms

Figure 29. Capacity value of wind power with and without geographical smoothing effect: (a) 

Capacity value in megawatts; (b) Capacity value in percentage of installed wind power; (c) 

Capacity value as a function of wind penetration level

5.3.8 Conclusions

Wind power will have a positive effect on system adequacy in a regional hydro-based power 

system. Wind power contributes to reducing the LOLP and to improving the energy balance.

Adding 3 TWh of wind or 3 TWh of gas generation are found to contribute equally to the energy 

balance, both on a weekly and annual basis. Both wind and gas improves the power balance. The 

capacity value of gas is found to be about 95% of rated, and the capacity value of wind about 

30% at low-wind energy penetration and about 14% at 15% penetration. The smoothing effect 

due to geographical distribution of wind power has a significant impact on the wind capacity 

value at high penetration. 

Indeed, similar results have been reported from various national studies. The significance of this 

study is therefore related to the real-life case studied, being a region rather than a national 

system, and demonstrating the relevance of applying system adequacy studies for generation 

expansion and transmission planning of regional systems. 
IEA Wind Task 24 Final Report, Vol. 2

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6 Sweden

6.1 Introduction

In Sweden, there is a large amount of hydropower installed, and there is also an intense 

discussion concerning large amounts of wind power. This means that there have been some 

detailed studies concerning the possibilities of balancing wind power in Sweden with the 

available hydropower resource. The current figures for Sweden (2008) are an installed amount of 

hydropower of 16,195 MW (47% average, all capacity) with a mean yearly production of 

68.4 TWh (47 % of all energy production) and a total storage capacity of 33.8 TWh. In general,

the Swedish hydropower comes from comparatively long rivers with many hydropower stations 

in the same river, which implies that hydrology has to be considered when operation is to be 

simulated/optimized. In 2008, the installed amount of wind was 1,021 MW (3.0% of all capacity) 

and the yearly production was 2.0 TWh (1.4 % of all energy production). There are currently 

discussions in Sweden concerning up to 12,000 MW of wind power corresponding to 

30 TWh/year. Sweden has rather strong interconnections to neighboring countries (Norway, 

Finland, Denmark, Poland, Germany), a total of around 9,000 MW. This is important for 

handling of dry years (import), wet years (export), and daily trading. Sweden is a part of the 

Nordpool electricity market, which means a high interaction concerning operation strategies with 

Denmark, Norway, and Finland—and the Nordpool countries together have 48,776 MW of 

hydropower; therefore, so in reality a large amount of wind power in Sweden will be balanced in 

the whole Nordic area.

Below two Swedish studies will be presented. The first one is a detailed study of one river, 

where the aim is to simulate how this river can balance wind power. The other study is about 

how the rivers in the North part of Sweden can balance wind power in the same region. 

6.2 Case Study for Balancing of Wind Power in One River

The following study description was extracted from the following more comprehensive report: 

Integration study of small amounts of wind power in the power system, by Lennart Söder, March 

1994 (in English), 206 pages. 

6.2.1 Introduction to Study

• Study description and goals: The possibility of balancing wind power with hydropower 

plants located along one certain river.

• Organizations involved: KTH conducted the study, with case data from a large hydrothermal 

producer in Sweden.

• Reason study was performed: The aim of the simulations is to study whether an increased 

amount of wind power might decrease the efficiency of the hydropower system along an 

interconnected river system.

• Published reports applicable to the case study: 

o Söder L., Modelling of wind power forecast uncertainty. Proceedings of 1993 European 

Community Wind Energy Conference held at Lubeck-Travemunde, Germany, 8-12 

March 1993.
IEA Wind Task 24 Final Report, Vol. 2

87

o Söder L., A mixed rule-based – linear programming method applied to daily operation 

planning of a hydropower system. Presented at Stockholm Power Tech, 1994. 

• Intended outcomes of the report relative to the objectives of Task 24: The study addresses 

possibilities of wind-hydro coordination.

6.2.2 Overview of Power System

The simulation model uses simulated realistic forecasts of the load and total wind power 

production, and these forecasts are updated each hour. The result from the simulation model 

includes the efficiency of the hydropower system measured as how many percent of the potential 

energy in the hydro system that is used for electricity production. The studied system includes 

seven hydropower stations with an installed capacity of 478 MW and a yearly energy production 

of 2.2 TWh. In this system 30-90 MW of wind power was installed. A summary of the main 

system characteristics is presented in Table 28.

Table 28. First half of the Task 24 matrix that defines important power system characteristics and 

some basic parameters of the setup for the first Swedish case studies

Geographic Area of Study: Sweden

Power System Characteristics: The study was made for an estimated system with wind and 

hydropower.

Load Hydropower

Generation Interconnection Wind Power

(base case)

Peak (MW) Min (MW) TWh/a Capacity (MW) Capacity (MW) MW GWh/a

450 100 2,2 478 0 30-–90 83

Other Relevant Characteristics of Power System: n/a

Characteristics of System Planning: planning updated hourly using new forecasts of wind and 

load

Description of Market: Day-ahead, intra-day, and regulation markets

Integration Time Frames of Importance: 

Yes/N

o

Time Frame

No Frequency, system dynamics, grid stability, LVRT, PSS, V-Reg

Yes Regulation 

Yes Load following; economic dispatch

Yes Unit commitment and day-ahead scheduling; economic utilization of resources

Yes Resource and capacity planning; reliability

6.2.3 Study Methodology

The method used was to (1) plan the hydropower system for a week (deterministic approach), 

(2) simulate changes in wind power production and load during the coming hour, (3) estimate 

how the power system was operated since it was not according to plan, (4) replan the rest of the 

week, and (5) go back to simulating changes in wind power until all hours during the week have 

been simulated. This means that forecast uncertainties concerning both wind power and load 

were considered, and the hydropower system operation was optimized and re-optimized when 

new information was available. But it was assumed that a certain amount of wind power was 

balanced using hydropower resources in a certain river. A summary of the study methodology in 

the context of the matrix is provided in Table 29.
IEA Wind Task 24 Final Report, Vol. 2

88

Table 29. Second half of the Task 24 matrix that lists variations of underlying assumptions and 

modeling approaches used in the first Swedish case study

Set Up

A Aim of Study 1 what happens with x GWh wind

M Method to Perform Study 1 add wind energy

S Simulation Model of 

Operation 

3 Stochastic simulation several cases

Simulation Detail

R Resolution of Time 2 hour

P Pricing Method 1 costs of fuels 

D Design of Remaining 

System 

2 optimized remaining production

4 perfect trading rules

Uncertainty and Balancing

I Imbalance Calculation 2 wind+load

B Balancing Location 2 from the same region

U Uncertainty Treatment 2 hydro inflow uncertainty

4 best possible wind forecasts

5 load forecasts considered

Power System Details

G Grid Limit on Transmission 1 no limits

H Hydropower Modeling 1 head height considered

2 hydrological coupling included (including reservoir capacity)

3 some hydrological restrictions included

4 hydro optimization considered

5 consider most relevant aspects of affected hydro resources

HC Hydro Capacity Service 2 real-time integration with impact of wind variability on system 

managed with hydro

T Thermal Power Modeling Not applicable – no thermal power plants considered in this 

study

W Wind Power Modeling 2 many wind power time series

3 time series smoothing considered

6.2.4 Wind Power Characteristics

Wind data from six sites in Skåne in southern part of Sweden are used. Two parallel wind speed 

series are used, one representative for winter (Jan 1 through March 31 and Oct 1 through Dec 31,

1980) and one for the summer (April 1 through Sept. 30, 1980). In addition to that, the wind 

speed forecast errors are assumed to be simulated according to estimated forecast errors. The 

interdependence of forecast errors in different sites with wind power plants are also considered. 

The wind power is obtained from 10 wind speed series and data from an assumed 2-MW wind 

power station for each wind speed series: Näsudden II.
IEA Wind Task 24 Final Report, Vol. 2

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6.2.5 Hydro System Characteristics

The studied hydropower system consists of seven hydropower stations and has a total capacity of 

478 MW. The hydropower stations are modeled with installed capacity, varying efficiency 

(marginal production equivalents) depending on discharge, reservoir capacity for each station,

and delay time between the different hydropower stations. An illustration of the system is 

provided in Figure 30.

Figure 30. An illustration of the seven hydro stations studied on the Ume River

6.2.6 Wind Power Penetration and System Flexibility

Wind power penetration was studied for three different scenarios: 30, 60, and 90 MW. The result 

was then extrapolated in order to draw conclusions for wind power integration in whole Sweden. 

The studied amount of wind power in the certain hydropower system corresponds to that the 

penetration in whole Sweden should be up to 6.5–7.5 TWh/year (i.e., 5 % of total energy

production per year). The whole balancing is assumed to be performed in Sweden and in the 

specific wind turbines.

6.2.7 Wind and Hydro Integration – Benefits and Impacts

The whole idea of this project is to simulate the impact of a certain amount of wind power on a 

certain hydropower system, assuming that only the generation in this hydropower system is used 

to provide the enhancement in balancing requirements caused by the addition of the wind power. 

Since there is a detailed model of the hydropower system, including efficiencies depending on 

discharge level, then it is possible to study whether there is a decreased efficiency depending on 

wind power integration. With more wind power in the system, the hydropower production will 

vary more, which may decrease the efficiency in the hydropower system. Table 30 shows 

simulation results of a the impact on overall hydro generator efficiency due to changes in 

operation caused by incorporation of 0–90 MW of wind power.
IEA Wind Task 24 Final Report, Vol. 2

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Table 30. Mean hydropower efficiencies with 0–90 MW of wind power

Wind

MW η' 1 η 2 η' 1 η 2 η' 1 η 2 η' 1 η 2 η' 1 η 2

0 87.09 90.89 87.02 90.57 87.81 92.06 87.20 91.10 87.28 91.15

30 87.10 90.89 87.05 90.60 87.74 92.09 87.23 91.27 87.28 91.22

60 87.01 90.90 86.96 90.57 87.74 91.12 87.15 91.29 87.21 91.22

90 86.94 90.87 86.81 90.45 87.68 92.05 87.14 91.32 87.14 91.17

Hydro-A Hydro-B Hydro-C Hydro-D Mean

Hydro periods A and B are assumed to be winter periods, while periods C and D are summer 

periods. The parameter η′1 = hydropower system efficiency with consideration of water flowing 

in the river. The parameter η2 = hydropower station efficiency in comparison with maximal local 

efficiency. These two different ways to calculate the hydropower system efficiency have then 

been used to see how much they change when the amount of wind power is increasing. In 

general, a lower efficiency means that more water have to be used in order to produce the same 

amount of hydropower (i.e., a measure of the energy loss). 

6.2.8 Summary and Conclusions

In the report the results from the study is scaled in order to get an estimation of the impact of 

small scale wind power on the efficiency in the hydropower system. The result from the 

simulations in the project is that Swedish wind power installations that generate about 

2–2.5 TWh/year do not affect the efficiency of the Swedish hydro system. At wind power levels 

of about 4–5 TWh/year, the installed amount of wind power has to be increased by about 1% to 

compensate for the decreased efficiency in the hydro system. At wind power levels of about 

6.5–7.5 TWh/year, the needed compensation is probably about 1.2%, but this figure has to be 

verified with more extended simulations.

The report presented here does not include effects on the losses and limitations in the 

transmission or distribution system. These effects can increase or decrease the value of wind 

power in the power system.

6.3 Case Study for Balancing of Wind Power in North Sweden

Report: Balancing of wind power and hydropower in Northern Sweden. By Mikael Amelin, 

Calle Englund and Andreas Fagerberg. Elforsk report 09:88 (in Swedish with an English 

summary). Available from www.elforsk.se/rapporter. 

6.3.1 Introduction to Study

• Study description and goals: The possibility of balancing wind power with the hydropower 

stations in northern Sweden

• Organizations involved: KTH conducted the study, with case data from a large hydrothermal 

producer in Sweden.

• Reason study was performed: The aim of the simulations is to study the possibility of 

balancing wind power in northern Sweden with hydropower in northern Sweden.

• Intended outcomes of the report relative to the objectives of Task 24: The study addresses 

possibilities of wind-hydro coordination.
IEA Wind Task 24 Final Report, Vol. 2

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6.3.2 Overview of Power System

The simulation model uses real hourly data for load, hydro inflow and thermal production. The 

wind power hourly data are simulated wind power series from 19 sites in northern Sweden. The 

simulated series have a total installed capacity of 795 MW, and the output has been scaled to 

1,000; 4,000; 8,000; and 12,000 MW. The hydropower system consists of 154 hydropower plants 

with a combined capacity of 13.2 GW, which corresponds to about 80% of the installed capacity 

of all hydropower in Sweden. This does not include any internal transmission limits within 

northern Sweden, but the local thermal production, load, and export capabilities from this region 

are considered with actual values.

Table 31. First half of the Task 24 matrix that defines important power system characteristics and 

some basic parameters of the setup for the second Swedish case studies

Geographic Area of Study: North Sweden

Power System Characteristics: The study was made for an estimated system with wind and 

hydropower.

Load Hydropower 

Generation Interconnection Wind Power

Peak (MW) Min (MW) TWh/a Capacity (MW) Capacity (MW) MW TWh/a

4000 3000 30 13200 8500 1000–

12000

2,5–30

Other Relevant Characteristics of Power System: There are 633 MW of local thermal 

production

Characteristics of System Planning: hourly simulation with assumption of perfect forecasts

Description of Market: Perfect market, but water movement could not be changed between

weeks

Integration Time Frames of Importance: 

Yes/No Time Frame

No Frequency, system dynamics, grid stability, LVRT, PSS, V-Reg

No Regulation 

Yes Load following; economic dispatch

Yes Unit commitment and day-ahead scheduling; economic utilization of resources

Yes Resource and capacity planning; reliability

6.3.3 Study Methodology

The method used was, for each week studied (12 different weeks per year were studied), and 

wind power level, to: (1) set up a certain wind power scenario, (2) define a level for each 

reservoir level at the end of the studied week including the level of flexibility for this level, (3) 

perform a deterministic optimization (linear programming approach) for how to use the available 

water as efficiently as possible (maximize the production) for the studied week considering: 

wind power production, hydrological constraints including juridical restrictions, export 

capability, local load, and thermal production. 

6.3.4 Wind Power Characteristics

The wind power hourly data are simulated wind power series from 19 sites in northern Sweden. 

Available data are from 1992–2001. The total wind power production is obtained by just 

summing up the data from the 19 sites since no transmission constraints within the studied are 
IEA Wind Task 24 Final Report, Vol. 2

92

considered. The simulated series have a total installed capacity of 795 MW and the output has 

been scaled to 1,000; 4,000; 8,000; and 12,000 MW.

6.3.5 Hydro System Characteristics

All hydropower stations larger than 10 MW in the studied area are considered (i.e., 154 stations). 

These stations are modeled with a variable efficiency at peak production and at a lower level 

(piece-wise linear marginal production curve). Reservoir volumes and constraints, juridical 

restriction concerning things such as minimum flow etc. and delay times between different 

stations are also considered. Inflows from 2007 (a rather “normal” inflow year) are applied. 

Twelve different simulations have been performed for different inflows, reservoir start, and end 

limits.

6.3.6 Wind Power Penetration and System Flexibility

Wind power penetration was studied for four different scenarios: 1,000; 4,000; 8,000; and 

12,000 MW. This corresponds to an energy penetration in the whole Sweden of 30 TWh/year 

(assuming 12,000 MW of wind), or 20 % of total energy production per year. The method used 

assumed that electric power exported to neighboring countries (Norway and Finland) or southern 

Sweden can be used in those areas. The study is based on the hydrological constraints and 

transmission system characteristics at the time the study was conducted (no expansions, etc.), 

with no balancing in local thermal power and no flexibility in the load.

6.3.7 Wind and Hydro Integration – Benefits and Impacts

The aim of this project was to simulate the impact of a certain amount of wind power on the 

hydro system in northern Sweden. Since there is a detailed model of the hydropower system, 

including efficiencies depending on discharge level, minimum discharge level, etc., it is possible 

to study whether there will be significant spillage depending wind power integration. In a 

situation with comparatively low load, high inflow and much wind, there may be a problem to 

export all the power produced. Figure 31 shows the export to Norway and Finland in relation to 

maximal possible export. The figure shows that there are only a few weeks when the export 

capacity is totally used. This is primarily because the yearly hydro planning is not assumed to be 

changed and because there is comparatively more wind power in the winter; therefore, all this 

wind power has to be exported—and this is a situation in which there is already a lot of 

hydropower exported from Northern to Southern Sweden.
IEA Wind Task 24 Final Report, Vol. 2

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Figure 31. Mean export to Norway and Finland per week in comparison with maximal export 

capability; Y-axis: “Mean delivery” in megawatt-hours, X-axis: “Week”

6.3.8 Summary and Conclusions

The report describes the results from a study of the capability of the hydropower to balance 

various amounts of wind power in Northern Sweden. To perform such an analysis, a model of 

the hydropower system north of cut two has been developed. The model includes 154 

hydropower plants with a combined capacity of 13.2 GW, which corresponds to about 80% of 

the installed capacity of all hydropower in Sweden. The model makes it possible to follow the 

interplay between hydropower, wind power, other power plants, and the load on an hourly basis. 

The hydropower model has been made as detailed as possible, and considers court decisions, 

water delay time between power plants, and other physical limitations. However, it has not been 

possible within this project to develop sufficiently detailed models of season and short-term 

planning. Also, the modeling of the electricity market is quite simplified. All in all, this results in 

a model showing what technical possibilities by hydropower can balance wind power variations, 

but more research is required to study how much of this balancing capability that will be made 

available to the electricity market under different regulatory frameworks.

The model has been used in a number of case studies to investigate the size of the balancing 

capability of the hydropower for a wind power expansion of 1,000; 4,000; 8,000; and 

12,000 MW, respectively. The spill that can be seen in the case studies is to an overwhelmingly 

extent a spill that can be avoided by using efficient tools for especially the season planning. Only 

in a few cases—and then in particular for a wind power expansion of 12,000 MW—will there be 

a spill that depends on insufficient balancing capability in the hydropower. The conclusion of the 

study is therefore that the existing hydropower in Northern Sweden has sufficient installed 

capacity and is fast enough to balance even large amounts of wind power. The challenge for a 

large-scale expansion of wind power is rather to find an outlet for all electricity generation. 

Improved planning tools can solve this challenge, but it could also be profitable to make 

investments in, for example, reinforced export capacity.
IEA Wind Task 24 Final Report, Vol. 2

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7 United States

7.1 Introduction

Several organizations in the United States originally expressed interest in contributing case 

studies to the work plan of Task 24. However, due to various circumstances, three case studies 

emerged with completed, documented studies that could be included as part of the U.S. 

contribution. The studies are from three different river systems and electrical balancing areas, as 

listed below:

• Missouri River and the Western Area Power Administration (WAPA)

• Upper American River and the Sacramento PUD

• Columbia River and the Grant County PUD

In these studies, each system differs in its basic characteristics related to wind integration: 

organizations involved and balancing area set-up and generation resources, hydro system 

characteristics, purpose of study, and constraining factors. However, each of these studies did 

seek to determine the basic impacts of wind integration in terms of the ancillary services required 

to handle the additional variability and uncertainty that wind power introduces into the balancing 

area net load. The third study also investigated the impact of wind integration on system flow 

constraints. 

7.2 Case Study: Western Area Power Administration, Missouri River

7.2.1 Introduction to Study

• Study description and goals: The purpose of this study was to determine the amount of wind 

generation that could be “accommodated” into the Upper Great Plains Region (UGPR) 

control area of WAPA. The effects of incrementally increasing levels of wind capacity of 80; 

100; 250; 500; and 1,000 MW were analyzed. The WAPA power system was kept in its 

current state with a goal of determining the levels of reserves that would be required with the 

addition of various wind generation capacities. The incremental regulation requirements for 

the control area will be computed with the well-accepted statistical methodologies developed 

by Kirby and Hirst (2000). Implications on load-following reserves will be determined 

through statistical analysis of the wind generation time series data and WAPA control area 

load data. Another primary goal of the study was to investigate the existence of any 

correlation between wind trends in the Dakotas and water runoff (and hence hydro resources) 

in the Missouri River Valley Basin.

• Organizations involved: This study was conducted by EnerNex Corporation and Wind on the 

Wires for NREL, in cooperation with WAPA and the U.S. Army Corps of Engineers

(USACE).

• Reason study was performed: The study was conducted to make an assessment regarding the 

magnitude of impact various levels of wind penetration would have on the system regulation 

(10-minute variations), load following (hour-to-hour), magnitude of ramping during the 

morning and evening load ramps, and the effect of wind forecast errors on the aggregate 

hour-ahead and day-ahead forecast error of load net wind versus load alone. These impacts 
IEA Wind Task 24 Final Report, Vol. 2

95

were deduced by comparing the relative statistics computed when considering load alone and 

load net wind.

• Case study chapter has been condensed from the following project report: EnerNex 

Corporation submitted a complete report of the study, titled “WAPA Wind Integration 

Study,” to U.S. National Renewable Energy Laboratory in August 2006 (available at the 

Task 24 site at www.ieawind.org) or from NREL

• Intended outcomes of the report relative to the objectives of Task 24: The intended outcomes 

of the study were to determine the impacts of integrating wind capacity into the given WAPA 

control area while maintaining the current restrictions and operations of the existing hydro 

resources. Specifically, an assessment was made of the wind generation impacts on 

regulation and load following. The long-term correlation between the availability of wind 

and hydropower was also investigated. Thus, this study falls into the “grid integration” case 

studies performed for the task. 

7.2.2 Overview of Power System

Table 32. First half of the Task 24 matrix that defines important power system characteristics and 

some basic parameters of the setup for U.S. WAPA Missouri River integration study

Geographic area of study: UGPR of WAPA, primarily covering North Dakota, South Dakota, 

and Montana in the United States: 378,000 square miles

Power system characteristics: 

Load Conventional 

Generation Interconnection Wind Power

Peak (MW) Min (MW) TWh/a Capacity (MW) Capacity (MW) MW TWh/a

2700 1200 ~ 12 Hydropower 

supplemented 

by Coal

Not relevant in 

this statistical 

study

80

100

250

500

1,000

0.30

0.37

0.92

1.80

3.80

Other relevant characteristics of power system:

• UGPR of WAPA includes Iowa, Minnesota, Montana, Nebraska, North Dakota, and 

South Dakota 

• 12 billion kWh per year produced in service area

• Hydro generation comes from six hydropower plants/dams on the main stem of the 

Missouri River 

• Hydropower resources are located in the Missouri River Valley Basin

Characteristics of system planning:

• WAPA’s power is planned and dispatched from an operations center in Watertown, 

South Dakota, to serve the load in its balancing area.

• Use of the hydropower adheres to the guidelines (restrictions) defined in USACE’s 

Master Manual.

• WAPA supplements its hydropower with purchases from other generation sources, as 

required to meet its load.
IEA Wind Task 24 Final Report, Vol. 2

96

• For the time frames of interest in this study, WAPA functions with a day-ahead capacity 

and energy plan for each hour of the day; the plan for any given hour can be updated up 

to an hour ahead, and within hour adjustments are possible. 

Description of market: Power is delivered to WAPA’s public power customers, many of which 

contract with WAPA as the balancing area operator. Some of these customers do not actively 

participate in a market and have their load served entirely by WAPA. Other customers do 

engage in bi-lateral transactions with other power generators or utilities, and function as a 

vertically integrated utility. Other customers may participate in a market, such as that operated 

by the Midwest Independent System Operator. However, for the purpose of this study, the 

market impacts and costs are not considered. The purpose is simply to understand the impacts of 

wind within the WAPA control area on its indigenous ancillary services requirements 

(requirement to regulation and load following). The analysis is statistical in nature, and is meant 

to assess the increase in variability experienced by WAPA in its balancing area, due to wind 

generation used to serve a portion of its load.

Integration time frames of importance: 

Yes/No Time Frame

No Frequency, system dynamics, grid stability, LVRT, PSS, V-Reg

Yes Regulation, AGC

Yes Load following, intra-hour ramping, economic dispatch

No Unit commitment and day-ahead scheduling, economic utilization of resources

No Resource and capacity planning, reliability

7.2.3 Study Methodology

The objective of this case study was to perform a statistical study of the impact of wind 

variability on load variability in the WAPA balancing area. Table 33 provides an overall 

summary of the study technique and assumptions that were employed. A list of key assumptions 

and limitations follow the table.

Table 33. Second half of the Task 24 matrix that lists variations of underlying assumptions and 

modeling approaches used in the U.S. WAPA Missouri River wind integration study

Set Up

A Aim of Study 1 what happens with a certain number of GWh of wind 

2 how much wind is possible 

M Method to Perform Study 1 add wind energy

S Simulation Model of 

Operation 

2 deterministic simulation several cases (note: the power 

system was not simulated in this study; rather, a statistical 

study was conducted using time-synchronized wind and load 

data)

Simulation Detail

R Resolution of Time 3 minute/second

P Pricing Method Not applicable to this study

D Design of Remaining 

System 

1 constant remaining system

Uncertainty and Balancing
IEA Wind Task 24 Final Report, Vol. 2

97

I Imbalance Calculation 2 wind + load

B Balancing Location 2 from the same region 

U Uncertainty Treatment 2 hydro inflow uncertainty 

4 best possible wind forecasts 

5 load forecasts considered 

Power System Details

G Grid Limit on Transmission Not applicable to this study

H Hydropower Modeling Not applicable to this study

HC Hydro Capacity Service 2 real-time integration with impact of wind variability on system 

managed with hydro

T Thermal Power Modeling Not applicable to this study

W Wind Power Modeling 3 time series smoothing considered (wind power time series 

was created using the MM5 mesoscale modeling computer 

program, with the output wind speed time series transformed 

into a power production time series)

7.2.3.1 Assumptions

• This was a statistical study of the influence of wind power on the WAPA control area load to 

deduce the approximate impact on regulation and load following.

• Interconnection considerations were neglected for this study (because this is a statistical 

study of the impact of wind variability when combined with load variability, interconnections 

do not come into play). 

• All theoretical wind turbines were either General Electric (GE) 1.5S or 1.5SL, 1500-kW 

models. Wind power time series were generated at a 10-minute time resolution for the model

year using wind speed data generated by a mesoscale model (MM5).

• Load and simulated wind power data were used from the year 2003.

• Wind power forecasting errors were considered alongside load forecast errors to assess the 

aggregate error in the load plus wind.

7.2.3.2 Limitations

• Ten-minute load and wind data were used for most of the analyses. This causes an 

underestimate of the minute-to-minute “regulation” variations experience by the load and the 

load net wind. This data captures the broader within-hour and hour-to-hour variability well, 

but the “regulation” value reported underestimates that actual amount since the fast 1-minute 

changes in load and wind are not taken into account.

• All hours of statistical simulations are treated the same, which may create problems when 

wind production exceeds 30% of the system load. That is, the statistical study shows there 

are a limited number of instances when wind power causes large swings on the system; 

however, when these swings occur during low load hours, the consequences on system 

operation can be significant and should be investigated in detail. No detailed investigation 

was part of this study. 
IEA Wind Task 24 Final Report, Vol. 2

98

• The data and analyses in this report do not address how the control area demand with 

significant wind generation would affect WAPA control area operations or how specific 

generating resources would be impacted.

• The statistical analysis of the long-term meteorological dataset located in the centroid of the 

proposed wind farm sites of the Dakotas, with runoff into a watershed that extends hundreds 

of miles, as far as western Montana, is problematic. There are inherent problems such as 

precipitation runoff lag for snowmelt in the Rocky Mountains. Other considerations include, 

for example, a particular meteorological pattern might favor greater-than-normal 

precipitation over the southern and central Dakotas but could also support drier-than-normal 

conditions over the westernmost part of the watershed.

• Long-term runoff data was available only as bulk for the entire section upstream of Sioux 

City, Iowa, but not from individual tributaries or points.

7.2.4 Wind Power Characteristics

The study included five potential wind plant locations spread throughout North and South 

Dakota, which provided substantial geographic diversity. A total of 50 points simulating proxy 

meteorological towers at 80-meters hub height were dispersed within these five regions. Wind 

speed and power data were simulated at 10-minute intervals for the year 2003. A nested grid was 

employed within the MM5 of 1–4 km resolution and used over North and South Dakota for 

power simulations, with a larger resolution meteorological model covering the central United 

States and parts of Canada. Wind speed data were used to generate power production at an 80-m 

hub height for GE 1.5S and 1.5Sl, 1,500-kW turbines. Figures illustrating the locations of the 

wind power plants within North Dakota and South Dakota are provided below. Figure 32 shows 

the general location of the wind power resources relative to the high-voltage transmission 

system, and Figure 33 shows the MM5 1-km grid covering the region, along with the proxy 

towers. The black dots of the proxy towers are located in five general areas, and these areas 

correspond to the five wind power plant sites considered in the statistical determination of the 

wind impacts on regulation and load following. 
IEA Wind Task 24 Final Report, Vol. 2

99

Figure 32. The general location of regions and sites for the MM5 meteorological simulation

Figure 33. The inner grid layout deployed for the MM5 meteorological simulation 

(Numbered black dots correspond to the “proxy” meteorological towers, and the large, magenta 

dot labeled “RNL PT” indicates the location where long-term climatological data was extracted for 

comparison of wind and hydropower resources)

Hub-height data was stored at 10-minute intervals for the entire year at each proxy tower. From 

this chronological data, a number of wind resource characterizations were generated. These 

10-minute wind speed time series were then transformed into wind power time series by 
IEA Wind Task 24 Final Report, Vol. 2

100

processing them through a GE 1.5-MW power curve. The power time series were then used in a 

long-term, climatological comparison of wind speed and water runoff, and in assessing wind 

integration impacts on the WAPA balancing area load. Hypothetical wind power plants proposed 

at five sites for the different wind scenarios are summarized in Table 34.

Table 34. The study cases and wind-generation scenarios

Wind Capacity

Approximate 

Wind 

Penetration 

(wind/peak load) Mission

Ft. 

Thompson

Buffalo 

Ridge Edgeley Max

80 MW 3.0% 40 MW 40 MW

100 MW 3.7% 20 MW 20 MW 20 MW 20 MW 20 MW

250 MW 9.3% 50 MW 50 MW 50 MW 50 MW 50 MW

500 MW 18.5% 100 MW 100 MW 100 MW 100 MW 100 MW

1000 MW 37.0% 200 MW 200 MW 200 MW 200 MW 200 MW

Sample output of the wind power time series are shown in Figure 34. The WAPA balancing area 

system load is plotted together with the wind power time series (each time series corresponds to 

a different scale) for the 2003 model year. 

Figure 34. The 2003 WAPA system load plotted with the output from a 500-MW wind power plant

Annual capacity factors for the five cases ranged from 42–44%. By season, the capacity factors 

were lowest in the summer months and greatest in the spring and fall. The average daily profiles 

were generally flat with a decline late in the day, as can be seen in Figure 35.

 

0

500

1000

1500

2000

2500

3000

J

F

MAM

JJ

ASOND

J

System Load (MW)

0

100

200

300

400

500

600

700

800

900

1000

Wind Generation (MW)

System Load Wind
IEA Wind Task 24 Final Report, Vol. 2

101

Figure 35. The average daily profile for summer months for the 500-MW scenario

7.2.5 Hydro System Characteristics

The six hydropower facilities being considered in this study are located at the dams within 

WAPA’s UGPR are run by USACE, managed within its North-Western Division, and based on 

written guidelines in the Missouri River Master Manual (www.nwdmr.usace.army.mil/mmanual/mast-man.htm). USACE updates this manual each year; provides 

guidance to the operators at each dam concerning water releases; and incorporates recent and 

projected hydrological conditions, environmental requirements (e.g., accounting for bird and fish 

survival), desired lake levels, etc. The dams are all located on the main stem of the Missouri 

River in the Northern-Midwest United States, as shown in Figure 36; they collect all of the 

runoff in the Missouri River drainage basin and feed into the Mississippi River at the most 

Eastern extent of the basin. 

Figure 36. The Missouri River drainage basin and the six dams and hydropower facilities operated 

by USACE (highlighted in red)

The names of the six dams are highlighted in Figure 36—they can store a combined 75 million 

acre-feet of water (about three times average annual runoff) and have hydropower facilities 

totaling a combined peak capacity of approximately 2,400 MW (Fort Peck: 185 MW; Garrison: 
IEA Wind Task 24 Final Report, Vol. 2

102

517 MW; Oahe: 714 MW; Big Bend: 494 MW; Fort Randall: 320 MW; Gavins Point: 132 MW; 

based on data from the U.S. Department of Energy, Energy Information Administration, 2002). 

WAPA schedules and markets power generated by these facilities and delivers it to public power 

customers throughout the Midwest. 

The hydro resources available to this control area are in the Missouri River Valley Basin. The 

dams are situated on the main stem of the Missouri River and therefore all downstream dams are 

dependent on the water releases from the upstream facilities. However, since the combined water 

storage capacity in these reservoirs is three times the annual runoff, there is some flexibility in 

timing of water releases. As with any federally funded hydropower facility in the United States, 

there are multiple uses for these facilities, and power generation is typically the lowest priority. 

These dams were created with flood control as their primary purpose, and they are also used for 

navigation, recreation, irrigation, and power generation. Furthermore, there are numerous 

environmental regulations regarding water releases from the dams pertaining to bird and fish 

survival. Combined, these priorities and constraints restrict the use of the indigenous flexibility 

available in the hydro generators. Therefore, capacity and energy schedules are set subservient to 

the other priority uses and environmental constraints.

Overlaid on the physical capabilities and constraints of the hydropower facilities are numerous 

organizations and stakeholders. USACE operates the dams; WAPA markets the power; and there 

are numerous public power and water customers. Taken together, the collective stakeholders and 

institutions create a formidable bureaucracy to negotiate. At present, the only practical method of 

wind integration is for customers of the Federal hydropower (e.g., the customers in WAPA’s 

balancing area) to integrate wind in with their load obligation and utilize the Federal hydropower 

within its current set of constraints, regulations, and laws. Because of the organizational and 

legal complexities involved, it was decided to focus this effort on determining the statistical 

impacts in the regulation and load-following time frames, considering only the balancing area 

load and proposed wind power generation.

7.2.6 Wind Power Penetration and System Flexibility

The study includes hypothetical installed wind capacities of 80; 100; 250; 500; and 1,000 MW. 

Peak load and the penetration rates that these capacities would correspond to are not mentioned 

explicitly in the project report. However, the peak system load during the period reported (2003) 

was approximately 2,700 MW. Therefore, the wind penetration computed as a percentage of 

peak system load, for each wind capacity scenario, is 3.0%; 3.7%; 9.3%; 18.5%; and 37.0%, 

respectively. Since this study only considers a statistical analysis of wind power variability on 

system load (i.e., the flexibility of the generators to meet the load is not considered), reporting 

the wind penetration as a percentage of peak load is appropriate.

7.2.7 Wind and Hydro Integration – Benefits and Impacts

Because this study focused on determining the impact of wind variability and uncertainty on the 

load following and regulation statistics, the benefits to or impacts on the hydropower system was 

not assessed. However, an investigation was made pertaining to the question of whether or not 

the timing of the wind power and hydropower resources was complementary, coincident, or 

uncorrelated. 
IEA Wind Task 24 Final Report, Vol. 2

103

In addressing this question, the long-term climatological relationship between the wind speed 

and water runoff (which is related to the hydropower) was conducted in the region of interest. To 

understand the statistical relationship between the Missouri River Valley Basin and the Dakotas 

wind, 40 years of overlapping data from 1964–2003 were selected for analyses. Data on the 

long-term wind climatology for the Dakotas was obtained from the National Centers for 

Environmental Prediction / National Centers for Atmospheric Research Global Reanalysis

(RNL) dataset (Kalnay, et al. 1996; Kistler, et al. 2001). A representative RNL grid point was 

selected to represent the wind characteristics of the Dakotas. As shown in Figure 33 displaying 

the MM5 grid, this RNL grid point is located close to the approximate centroid of wind power 

generation for the ensemble of proxy wind farm locations. By extracting the approximate hubheight level wind data from the RNL dataset, monthly time series data for the 40-year 

examination period were produced for the representative grid point. USACE provided long-term 

runoff data for the Missouri River, above Sioux City, Iowa, in monthly increments for the period 

of 1964–2003. USACE normalized the runoff data to the 1949 level of development. 

Data for runoff and wind speed departures are plotted in Figure 37. The blue line shows the 

monthly runoff departures for this period (the departure is defined as the difference between the 

monthly average and the long-term, 40-year average), and the magenta line represents the 

monthly wind speed departures. However, this graph shows no obvious correlation between the 

data. Numerous statistical tests were conducted to determine if a correlation exists between the 

data sets, considering the monthly data, yearly averaged data, and rolling averages of the data.

Time shifts of the data sets were also considered in order to uncover any time shifting of the data 

(i.e., one data set leading or lagging the other). Of the many statistical tests conducted, most did 

not yield any correlation between the data sets. The only trials that yielded small-to-modest 

inverse correlations were those for the 2- and 3-year running averages (R = -0.25 and -0.26, 

respectively) and a trial that involved leading (lagging) the wind speed departure series (runoff 

departure series) by 1 year (R = -0.4). In the latter case, the time series analysis indicated that 

67% of the comparison points for the wind speed and runoff departures were of opposite sign. In 

summary, the most coherent statistical signal resulted from the experiment in which wind speed 

departure was allowed to lead the runoff departure by 1 year. In physical terms, this would 

indicate at least some tendency for good wind years to lead (by just 1 year) poor runoff years.
IEA Wind Task 24 Final Report, Vol. 2

104

Figure 37. Time series of monthly runoff and wind speed departures, 1964–2003

7.2.7.1 Regulation and Load Following Statistics

The relative impacts of wind generation on real-time control area operations and day-ahead 

planning can be estimated by examining various aspects of load and wind generation time series 

data. In general, the effects are assessed by comparing various attributes of the load alone to the 

load net of wind generation. For evaluating real-time operating impacts, the variability of the 

control area demand is the major issue. Generating resources must be deployed and controlled to 

match demand continuously. This overall objective can be further broken down by time frames 

over which the adjustments to generation take place. A typical breakdown would include:

• Regulation: tens of seconds to minutes

• Load following: tens of minutes to an hour

• Ramping: one to several hours

While these terms can take on somewhat different definitions in various control areas, and the 

division between them may be hard to quantify, in general, they cover the range of generation 

control that takes place over the course of a day. The effect of wind generation on the behavior 

of the WAPA control area demand in these operational time frames was investigated in this 

study, the results of which are described below.

An initial measure of the wind generation impact on the control area demand variability is 

created by calculating the value of the control area demand changes from one 10-minute interval 

to the next. Figure 38 shows the statistical distributions of these variations for the existing load 

alone and the load net 500-MW wind generation. Using the standard deviation of the distribution 

as the metric, wind generation impacts begin to become significant at the 500-MW level. This 

measure, however, is relatively course and does not account for the faster 1-minute fluctuations 

experienced by the system. 

Runoff and Wind Speed Monthly Departures 

-5500

-4500

-3500

-2500

-1500

-500

500

1500

2500

3500

4500

5500

1964196519661967196819691970197119721973197419751976197719781979198019811982198319841985198619871988198919901991199219931994199519961997199819992000200120022003

Year

Runoff Departure (Acre Ft x 1000)

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

Wind Speed Departure (m/s)

Runoff

Wind Speed
IEA Wind Task 24 Final Report, Vol. 2

105

Figure 38. Statistical distribution of 10-minute regulation for the 500-MW scenario

Another method for processing wind and load data is illustrated in Figure 39. A trend 

characteristic (based on a 2-hour running average) and an hourly average value have been

created from the 10-minute resolution load data. Variations of the control area demand were then 

computed as the differences between the actual load and the trend or the actual load and the 

average value for the hour.

Figure 39. A sample of the actual WAPA load, load trend, and average hourly load

The amount of “10-minute regulation” capacity that would be required to compensate for the

additional fluctuations wind adds to the system due to variations of the 10-minute load data 

about the 2-hour tend are summarized in Table 35. Since the distribution of variations is 

essentially normal (Gaussian), 99.7% of all variations are accounted for by multiplying the 

standard deviation by 3 (3σ) and 99.9999% by computing 5σ. The “incremental regulation” is 

then computed by computing the difference in these measures between the load only and the load 

net wind. The value of the incremental regulation that the system operator would be required to 

provide for the system is expected to be in the range of three times the change in the standard 

deviation (load and load net wind), 3∆σ, to 5∆σ.

4792 4795 4798 4801 4804

1292

1599

1906

2213

2520

Load Trend

Hourly Average Load

Actual Load

Hour

Load (MW)

 

0

1000

2000

3000

4000

5000

6000

7000

-200-175-150-125-100

-75-50-25

0

255075

100125150175200

Load deviation from trend (MW)

Occurrences

Net Load σ = 31.2 MW

Load Only σ = 28.4 MW
IEA Wind Task 24 Final Report, Vol. 2

106

Table 35. Incremental 10-minute regulation requirements for wind generation

Scenario

Incremental 

Regulation

(3 x ∆σ)

Incremental 

Regulation

(5 x ∆σ)

100-MW Wind 

Generation 0.6 MW 1.0 MW

250-MW Wind 

Generation 1.2 MW 2.0 MW

500-MW Wind 

Generation 3.9 MW 6.5 MW

1,000-MW Wind 

Generation 12.9 MW 21.5 MW

A second component of the variation consists of the difference between the trend (i.e., the 2-hour 

rolling average) and the average hourly value, which would impact generation that is being 

adjusted to track slower changes in control area demand. The statistics of these variations are 

summarized in Table 36. The right-most column in the table shows a value of 2 multiplied by the 

change in standard deviation between the existing load and the load net wind in each scenario. 

Two times ∆σ would account for 95.4% of the additional variations in the longer-term (load 

following type) trend brought about by the wind in each scenario. The actual increment in this 

trend can be computed by multiplying the change in standard deviation by between 2 and 4, 

dependent on the preferences of the system operators and planners. However, the load following 

trend is typically multiplied by a smaller factor than the regulation characteristic, simply because 

the system operator typically has time to deal with the changes in trend through economic 

dispatch of units or via the market, whereas the regulation characteristic must be dealt with in 

real time with resources available on the system. 

Table 36. Summary of statistics: variation of 2-hour rolling average from average hourly value

Scenario σ – Deviations from 

Hourly Average 2 x σ 2 x ∆σ

Existing Load 31.3 MW 62.6 MW --

100-MW Wind 

Generation 31.7 MW 63.4 MW 0.8 MW

250-MW Wind 

Generation 32.5 MW 65.0 MW 2.4 MW

500-MW Wind 

Generation 34.3 MW 68.6 MW 6.0 MW

7.2.7.2 Morning and Evening Ramping

As shown previously, spatial and geographic dispersion of large numbers of individual wind 

turbines have a significant beneficial effect on short-term variability of wind energy production, 
IEA Wind Task 24 Final Report, Vol. 2

107

since short-term changes between wind power plants are essentially uncorrelated. Over longer 

time periods, however, correlation in behavior between even well-separated wind plants will 

increase. In real-time operations, multi-hour periods when the load is changing substantially can 

be challenging for operators. With significant wind generation in the control area, there exists a 

possibility that these ramping periods can be made even more challenging if wind production 

moves in a direction opposite to that of the load. Therefore, peak ramping load hours in the 

morning and evening, which are of special interest to operators, were analyzed in greater detail. 

The effect of wind generation on changes during these important operational periods can be 

illustrated from the annual record of wind and load data for the study year. The two graphs in 

Figure 40 show the hourly load changes during the morning and evening ramping periods for 

load and the various wind scenarios. Perhaps what is most striking about these distributions is 

how little wind generation effects the hourly changes, even at 500 MW.

Figure 40. Hourly load changes during ramp periods for 500-MW wind scenario, morning (left) and 

evening (right)

The impact of changes/ramps in wind energy on the morning and evening ramps become more 

pronounced if one considers a 4-hour period of time in the morning or evening. Figure 41

illustrates the net control area demand change over a multi-hour period in the morning (from 

6 a.m. to 10 a.m.) for the 500-MW scenario. Here, the effect of wind generation shows up more 

clearly. At 500 MW, there are 4 days with morning ramps higher that what was seen in 2003 

with load alone, and a substantially higher number of very high ramps.

Figure 41. Distribution of morning ramps for load and load net 500-MW wind generation

 

0

50

100

150

200

250

300

-250-200-150-100

-50

0

50

100150200

250300

MW Change

Occurrences

Delta Actual Load

Delta Net Load

 

0

50

100

150

200

250

300

350

400

-375-325-275-225-175-125

-75-25

25

75

125175

MW Change

Occurrences

Delta Actual Load

Delta Net Load

250 0 250 500 750

0

8

16

24

32

40

Load

Load net Wind

M W

Count
IEA Wind Task 24 Final Report, Vol. 2

108

7.2.7.3 Wind Generation Forecast Error

Wind generation forecasting is a relatively new science. At present, day-ahead forecast accuracy 

of 15–25% mean absolute error of the plant rating over 1 year has been achieved. Even with this

level of effectiveness, there will still be hours and days in which the wind generation forecast 

error can be very high. Mis-timing of frontal passages, for example, can lead to very large hourly 

errors, even if the overall pattern and daily energy are reasonably close to actual. Day-ahead 

forecasts of energy delivery can be more accurate since they are not as dependent on the exact 

timing of meteorological systems. Accuracies of 15–20% of actual energy delivered are possible. 

For this study, a forecast time series for the 2003 wind scenarios was created, based on a forecast 

study conducted for Xcel Energy. 

The overall impact of day-ahead wind generation forecast errors depends on the accuracy of dayahead load forecasts. If load forecasting was perfect, then any costs due to sub-optimal plans or 

day-ahead transaction decisions could be attributed to wind generation. Since load forecasting is 

not perfect, the effect of wind forecast errors can be muted somewhat, given that there are days 

or hours in which the sign of the individual errors is opposite. Of course, there will also be times 

where the errors add mathematically. If the individual error series are uncorrelated, however, the 

combined error over time would be significantly less than the arithmetic sum. Figure 42 contains

a time series plot of the individual day-ahead forecast errors for wind generation and load for the 

hourly data for the year 2003. 

Figure 42. Time series plot of the load forecast error and the wind forecast error for the 

500-MW scenario

Combining these forecast errors shown in these time series, Figure 43 provides a distribution of 

the errors for the load forecast and the forecast for load net wind for the 500-MW scenario. Due 

to the existing load forecast errors in the WAPA system, there is little noticeable impact of wind 

generation on the day-ahead, hour-by-hour forecast until the penetration reaches 500 MW (as 

shown). 

 

500 M W Sce ne rio

-400

-300

-200

-100

0

100

200

300

400

1 1001 2001 3001 4001 5001 6001 7001 8001

Hour

M W

L o a d Fo re ca s t E rro r

W in d Fo re ca s t E rro r
IEA Wind Task 24 Final Report, Vol. 2

109

Figure 43. Distribution of load and load net wind day-ahead forecast errors for the 500-MW 

scenario

In conclusion, the statistical study presented has indicated that in the WAPA system, significant 

operational impacts from wind energy—those that must be dealt with in planning and 

operation—will likely arise when the wind penetration approaches 500 MW (about 18% of the 

peak system load). Below 10% wind penetration, the impacts on 10-minute regulation—load 

following, morning and evening ramping, and the load net wind forecast error—are somewhat 

modest.

7.2.8 Conclusions

This study considered integrating five levels of wind power (80 MW, 100 MW, 250 MW, 

500 MW, and 1,000 MW) into the WAPA control area with its peak load of 2,700 MW. Thus the 

wind penetration levels (defined by wind power capacity divided by peak load) for these cases 

were 3%, 3.7%, 9.3%, 18.6%, and 37%, respectively. A statistical study was performed to 

determine the impacts of the wind integration on the system regulation (minute-to-minute 

variations), load following (hour-to-hour), magnitude of ramping during the morning and 

evening load ramps, and the effect of wind forecast errors on the aggregate hour-ahead and dayahead forecast error of load net wind versus load alone. The following conclusions were drawn:

• The amount of “10-minute regulation” capacity that would be required to compensate for the 

additional fluctuations wind adds to the system due to variations of the 10-minute load data 

about the 2-hour tend was found to be minimal for wind penetration levels up to 250 MW, 

noticeable at 500 MW, and significant at 1,000 MW of wind power. 

• The load following trend, computed via changes in hourly load and compared to changes in 

hourly load net wind, showed a similar result to the regulation in that the influence of the 

wind power did not become significant until 500 MW of wind was absorbed into the system.

• Of importance in any electrical system is the ability of the system operator to use available 

generation resources to effectively and economically meet the morning and evening ramping 

requirements. The statistical study demonstrated that the load changes during the morning 

and evening ramping periods were very similar for the load alone and the load net wind even 

 

0

200

400

600

800

1000

1200

1400

1600

-375-325-275-225-175-125

-75

-25

2575

125175225

275325375

Hourly Error in MW

Occurrences

No Wind

With Wind
IEA Wind Task 24 Final Report, Vol. 2

110

up to 500 MW of wind power (18.6% penetration). After this level, the wind does impact the 

ramping requirements, increasing the number of larger ramps that occur and increasing the 

maximum level of ramping needed during the year.

• In investigating the effect of wind forecast errors, the error in the wind forecast was 

combined with load forecast errors and then compared to the load forecast errors alone. Due 

to the existing load forecast errors in the WAPA system, there is little noticeable impact of 

wind generation on the day-ahead, hour-by-hour forecast until the penetration reaches 

500 MW. 

In conclusion, the statistical study presented has indicated that in the WAPA system, significant 

operational impacts from wind energy—those that must be dealt with in planning and 

operation—will likely arise when the wind penetration approaches 500 MW (about 18% of the 

peak system load). Below 10% wind penetration, the impacts on 10-minute regulation—load 

following, morning and evening ramping, and the load net wind forecast error—are somewhat 

modest.

7.3 Case Study: Sacramento PUD, Upper American River

7.3.1 Introduction to Study

• Study description and goals: The study focused on the impacts to the Sacramento Municipal 

Utility District (SMUD) that four proposed penetration levels of wind generation would have

on regulation requirements, equivalent capacity values, and integration costs. The goals of 

the study are grouped into seven defined tasks. These tasks include such components as 

creating models of the SMUD system operations by including wind, estimating the capacity 

value of wind in the system, and determining the reserve requirements and unit commitment 

practices required for incremental penetration levels. Additionally, educational resources for 

system operators are considered, as well as interconnection issues with the existing grid 

system. 

• Organizations involved: EnerNex Corporation conducted the study for the Public Interest 

Energy Research Program of the California Energy Commission in collaboration with

Sacramento PUD. WindLogics was also involved to assist with the development of the wind 

generation models.

• Reason study was performed: The study was conducted to analyze the impacts that increased 

intermittent generation will have on system operations, reliability, reserve requirements, and 

overall integration costs.

• Case study chapter has been condensed from the following project report: Zavadil, Robert. 

2008. Wind Integration Study for the Sacramento Municipal Utility District. California 

Energy Commission, Public Interest Energy Research Renewable Energy Technologies 

Program. CEC-500-00-034.

• Intended outcomes of the report relative to the objectives of Task 24: This study fits into the 

grid integration case study category of Task 24. The primary objective was to assess the 

stochastic nature of the power produced from additional wind energy plants and the impacts 

they cause on the need for additional fast-ramping regulation and load following reserves in 
IEA Wind Task 24 Final Report, Vol. 2

111

the SMUD balancing area. Also investigated was the ability to provide “regulation” from the 

hypothetical Iowa Hill pumped storage facility.

7.3.2 Overview of Power System

Table 37. First half of the Task 24 matrix that defines important power system characteristics and 

some basic parameters of the setup for U.S. SMUD wind integration study

Study conducted by: EnerNex Corporation for Public Interest Energy Research Program of 

California Energy Commission (WindLogics subcontracted for wind generation modeling)

Geographic area of study:

- County of Sacramento

- 900 mi2 (source: SMUD website)

- Population of area served: 1.4 million (source: SMUD website)

- Five wind plants located outside of the SMUD balancing area

- Hydropower on the Upper American River

Power System Characteristics:

Load Conventional 

Generation Interconnection Wind Power

Peak (MW) Min (MW) TWh/a Capacity (MW) Capacity (MW) MW TWh/a

3,721 (2011) 924 

(2011)

13.65

(2011) 

972 thermal

(including 500 

Combined 

Cycle Gas 

Turbine)

230 additional 

contract 

thermal

688 hydro

10 MW PV

2000 MW 

(currently and 

2011)

Case 1: 

102 

(current)

Case 2:

252 

Case 3:

453 

Case 4:

855 

Case 1: 

102 MW 

= 0.292

Case 2: 

252 MW 

= 0.703

Case 3: 

453 MW 

= 1.291

Case 4: 

855 MW 

= 2.400

Other relevant characteristics of power system:

• SMUD peak load exceeds generating capacity, and excess generation is scheduled through 

the California Independent System Operator (CAISO) in the form of short-, medium-, and 

long-term contracts.

• Iowa Hill is a proposed 400-MW pumped storage hydro facility

Characteristics of system planning:

• Day-ahead scheduling: Schedule 6 a.m. day-ahead for unit commitment

• Regulation: Includes minute-to-minute up to a couple of hours

• Real-time desk conducts hour-ahead adjustments when needed

• Hydro is run on ACG for fast regulation, but is used sparingly to conserve for summer

Description of market:

• SMUD is vertically integrated, and tied to CAISO with many long- and medium-term, 

bilateral contracts. 

• The integration study relied on the upcoming CAISO Hourly Ahead Scheduling Process 

market for sales of excess wind. SMUD is unable to purchase energy in the Hourly Ahead 
IEA Wind Task 24 Final Report, Vol. 2

112

Scheduling Process market.

Integration time frames of importance: 

Yes/No Time Frame

Yes Frequency, system dynamics, grid stability, LVRT, PSS, V-Reg

Yes Regulation, AGC 

Yes Load following; intra-hour ramping; economic dispatch

Yes Unit commitment and day-ahead scheduling; economic utilization of resources

Yes Resource and capacity planning; reliability

7.3.3 Study Methodology

The study was broken into seven separate tasks (of which six were completed) to assess the 

impacts of integrating wind into the SMUD system. Additionally, a series of four cases were run 

with different wind penetration levels, and each of these were assessed with and without 

consideration of the Participating Intermittent Resources Program (PIRP), as well as with and

without the proposed Iowa Hill pumped storage facility. The first two cases involve smaller 

penetration levels, which are also less geographically diversified. The second two cases involve 

higher and more geographically diversified penetrations. The simulations were conducted by 

using load data from 2003–2004 as well as physics-based Numerical Weather Prediction wind 

models using meteorological data from 2003–2004.

Underlying assumptions and the modeling approach are summarized in Table 38.

Table 38. Second half of the Task 24 matrix that lists variations of underlying assumptions and 

modeling approaches used in the U.S. SMUD wind integration study

Set Up

A Aim of Study 1 – what happens with a certain number of GWh of wind –

analyze impacts of various levels of wind generation on: 

ancillary service amounts and costs, performance and 

reliability, reserve requirements

M Method to Perform Study 1 – add wind energy to SMUD

(also consider scheduling new wind through ISO)

S Simulation Model of 

Operation 

2 – deterministic simulation several cases (various levels of 

wind integrated)

Simulation Detail

R Resolution of Time 2 – hour scale for analysis of load following generation and 

needed reserves; 

3 – sub-minute scale for analysis of fast regulation analysis and 

necessary reserves

P Pricing Method 1 – costs of fuels 

2 – prices for trading with neighbors

4 – market dynamics included

D Design of Remaining 

System 

1 – constant remaining system

2 – optimized remaining production

Uncertainty and Balancing

I Imbalance Calculation 2 – wind+load
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B Balancing Location 2 – from the same region 

3 – also outside region 

U Uncertainty Treatment 3 – no wind forecasts (persistence for < 2 hours ahead, rough 

error patterns and mean absolute error used otherwise)

5 – load forecasts considered 

6 – thermal power outages considered

Power System Details

G Grid Limit on Transmission 2 – constant MW limits – 2000-MW import limit

3 – consider voltage 

H Hydropower Modeling 5 – consider most relevant aspects of affected hydro resources

HC Hydro Capacity Service 2 – real-time integration with impact of wind variability on 

system managed with hydro

T Thermal Power Modeling 1 – ramp rates considered 

2 – start/stop costs considered 

3 – efficiency variation considered 

4 – heat production considered 

W Wind Power Modeling 3 – time series smoothing considered

7.3.3.1 Assumptions

• For the purpose of the study, the short-, medium-, and long-term power contracts that SMUD 

schedules through CAISO were treated as if they were tangible power produced by 

conventional sources. Appropriate loss of load and forced outage rates were incorporated. 

• The wind and hydro projects involved are out of the SMUD control area, but all are hardwired to the control area.

• Wind power production values were formulated using Vestas V82 1.65-MW turbines. The 

peak power rating was scaled to 3 MW for the purpose of achieving 12 MW of generating 

capacity at each proxy tower site.

• All generating units can exist only at full capacity (up) or zero capacity (down) states, and 

forced outage, failure, and repair rates are subsequently determined. 

• Fuel costs were assumed fixed throughout the year. 

• The reference case treats wind as a perfectly predictable source, therefore requiring no 

change in reserve requirements. The actual or additional cases require additional reserves.

Penetration levels and geographic dispersion of the seven cases are shown in Table 5 of the 

report.

7.3.3.2 Limitations

• Meteorological weather simulations were used to determine wind availability at some of the 

key sites due to a lack of historical weather data.

• There are some inconsistencies between the simulated cases, mostly in the form of different 

reserve amounts for the cases that included Iowa Hill. These discrepancies were not 

discovered until later in the study, and there was not sufficient time to re-run all cases to be 

consistent.
IEA Wind Task 24 Final Report, Vol. 2

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7.3.3.3 More Details About the Simulation

Many wind integration studies to date have employed an analytical methodology based on 

chronological simulation of power system operation at the hourly level. This hourly granularity 

is very common in conventional production costing analysis, and allows for long-term (i.e.,

annual or longer) simulation of the power system. Because this study applied a novel technique 

to determine the fast regulation requirements (minute-to-minute), it bears further explanation. 

Due to temporal resolution, the real-time control of generation for support of system frequency 

and balancing of the control area cannot be represented explicitly in an hourly simulation. 

Instead, the amount of capacity type required to perform this function is deployed indirectly to 

meet various reserve constraints that are placed on the unit commitment optimization and 

economic dispatch of units in the simulation. While this has worked quite well for conventional 

production costing studies, there is some concern that it may be inadequate in some instances for 

wind generation studies. This is primarily due to the fact that increased reserve requirements and 

cost is of primary interest in wind integration, whereas the reserve questions are generally much 

less important for other studies based on production costing simulations. Therefore, one 

objective of this wind integration study was to employ detailed, high-resolution simulations of 

the SMUD control area to ascertain the impacts of wind generation on real-time operation of 

SMUD resources. The platform used for these simulations is the e-terra-simulator from Areva 

Transmission & Distribution. The simulator is a component of Areva’s Energy Management 

System product line for utility control centers. Its primary purpose is to provide a realistic 

environment for training of system operators, which is why the product has also been known as 

the Dispatcher Training Simulator. The Dispatcher Training Simulator uses all the core 

algorithms and processes used to manage a control area, but operates on a simulation model 

rather than from Supervisory Control and Data Acquisition signals as inputs and generation 

control pulses as outputs. Areva’s Dispatcher Training Simulator has been modified to allow 

wind generation to be represented as a point-by-point generation value, so that the type of wind 

data generated for this study can be used directly to define wind generation.

7.3.3.4 Hourly Simulation

The hourly cases were completed for at least 1 full year of data for four wind generation 

penetration levels. Wind generation data for the all cases was synthesized from the WindLogics 

MM5 meteorological simulation data for the historical year 2003. Integration cost in this study is 

defined as the difference between the actual production cost incurred to serve the net of actual 

load and actual wind generation and the production cost from the reference case, where wind is 

perfectly known and adds no variability to the control area, and where next-day load is the only 

uncertainty.

The method for determining the costs at the hourly level proceeds as follows:

1. Run the unit commitment program (Areva e-terra Commit) in Unit Commitment mode to 

develop a plan for serving the forecast load. Wind generation for the day is known 

perfectly, and is delivered in equal amounts each hour through the day. The results are 

saved as basis for the next step.

2. Run a second unit commitment simulating a morning-of re-commit with the day-ahead 

transactions fixed from Step 1, and allow the thermal units to recommit if necessary. This 
IEA Wind Task 24 Final Report, Vol. 2

115

run uses fixed-block wind energy and assumes perfect load knowledge. The results are 

saved as a basis for the next step.

3. If Iowa Hill is included in the case, a third unit commitment run is made to simulate hourahead commitment decisions on its operation. This run assumes perfect knowledge of 

load. The day-ahead transactions and unit commitment schedule from Step 2 are fixed,

but dispatch is allowed to vary based on the commitment.

4. Using the unit commitment from Steps 2 or 3 above, re-run the day with forecast load 

replaced by actual load. The program runs an economic dispatch based on the 

commitments and allows re-dispatched, available units to meet the actual load. 

Generation is manually committed to meet load that cannot be served from the early 

same-day commitment. From these results, total production cost for the period is 

calculated and defined it as the “reference production cost.”

5. Repeat Step 1 with the load forecast and the day-ahead wind energy forecast calculated 

from the actual wind pattern. Again, the results are saved with the day-ahead energy 

transactions.

6. Run a second unit commitment simulating a morning-of re-commit with the day-ahead 

transactions fixed from Step 5, and allow the thermal units to re-commit if necessary. 

This run assumes perfect load knowledge. Wind forecast uses the same error pattern as 

Step 5 but reduces the mean absolute error of the forecast to 10%, which is consistent 

with typical 2–10-hour-out forecasts. The results are saved as a basis for the next step.

7. If Iowa Hill is included in the case, a third unit commitment run is made to simulate hourahead commitment decisions on its operation. This run assumes perfect knowledge of 

load and uses a two-hour persistence forecast for wind. The day-ahead transactions and 

unit commitment schedule from Step 6 are fixed but dispatch is allowed to vary based on 

the commitment.

8. Finally, using the unit commitment from Steps 6 or 7 above, re-run the day with forecast 

load replaced by actual load. The program runs an economic dispatch based on the 

commitments and allows re-dispatch available units to meet the actual load. Any 

unserved load left at the end of the dispatch is met using a generic thermal resource at 

10.45 heat rate and the prevailing gas cost. From these results, total production cost for 

the period is calculated and defined it as the “actual production cost.”

9. Integration cost is then calculated as the difference between the “actual production cost” 

and “reference production cost” normalized to the total annual wind energy for the case 

and is expressed in $ USD/MWh of wind energy.

Certain aspects of the methodology enumerated above merit additional emphasis:

• Load megawatt-hours and wind megawatt-hours in “reference” and “actual” cases is 

identical. If wind generation is assumed to be a “must take” resource, the payment from 

SMUD to the wind generators is identical in both the “reference” and “actual” cases. 

Therefore, the cost per megawatt-hour of wind energy is not relevant to the analysis (i.e., it 

“subtracts out”).
IEA Wind Task 24 Final Report, Vol. 2

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• Reference cases are run with base reserve requirements not modified by the wind. Actual 

cases are run with reserve requirement that are modified by the wind values, as documented 

elsewhere in this report. The exception to this rule is the PIRP case in which the base reserve 

requirements are used also for the “actual” cases. This in effect says that in PIRP cases,

SMUD bears no burden for extra reserve requirements due to the wind.

• Consumes combined-cycle plant was set as must-run in all cases. Without this condition, 

there were a large number of overnight hours that did not meet energy and reserve 

requirements since no units under AGC were committed based on economics.

Finally, there is the issue of the wind generation attributes defined for the “reference” case. In 

this method, wind energy delivery is allowed to vary day-by-day, but the delivery within in a day

is assumed to have the characteristics of a baseload resource. The argument for such treatment is 

that baseload resources impose no incremental burden on daily operations (except for decisions 

to de-commit large baseload resources). They neither assist with nor detract from the ramping or 

regulation requirements imposed by the load. In some respects, they are nearly invisible to the 

system operators.

The reference resource for wind assumed here is equivalent to an “as-available” energy contract 

with a third-party, where the terms of the contract allow the constant delivery to be scheduled a 

day in advance. 

In some circumstances, defining the reference resource to be some type of conventional unit may 

be appropriate. Care must be taken, however, to operate this unit per the terms of the contract 

and within the capabilities of the actual proxy unit. As an example, if the reference resource were 

defined to be a simple cycle-gas turbine, it would not be appropriate to allow that unit to be 

dispatched to provide load following or other ancillary services unless the terms of the power 

purchase agreement were to explicitly include consideration of and compensation for this 

capability.

7.3.4 Wind Power Characteristics

The study analyzed the impacts of SMUD’s existing 102-MW Solano wind plant and a 

subsequent expansion to 250 MW. Additionally, the study looked at a third case with 453 MW 

and a fourth case of 855 MW, using wind at geographically dispersed sites ranging from northern 

California to southern Oregon. Wind power simulations were divided into seven cases to 

incorporate the various penetration levels, geographic diversity, and scenarios, including Iowa 

Hill pumped storage. Each of these included a PIRP and non-PIRP run to assess the impacts of 

variability. The four most relevant cases are shown in Table 39. Wind and load data from 2003–

2004 were used in the simulations. Numerical Weather Prediction simulations MM5 were 

conducted with a resolution of 20–40 km for entire area, with nested grids of a few kilometers 

used for wind sites of major interest.
IEA Wind Task 24 Final Report, Vol. 2

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Table 39. Wind turbine population and capacity by case

Site Solano Abert Rim Stinkingwater 

Mountains

Shasta 

County

Fredonyer 

Peak

Case 1

MW 102

Turbines 34

Towers 9

Case 2

MW 250

Turbines 84

Towers 21

Case 3

MW 250 200

Turbines 84 67

Towers 21 17

Case 4

MW 250 200 200 50 150

Turbines 84 67 67 17 50

Towers 21 17 17 5 13

7.3.5 Hydro System Characteristics

The hydro component of SMUD comes from both the Upper American River Project and

WAPA. All existing hydro resources are outside of the SMUD balancing area. The hydro 

capacity of 688 MW is generated from 11 units, and the study incorporates forced outage, 

failure, and repair rates for each of these. The average monthly load served by each of these 

entities was used to produce the power profiles for hydro component of the system. The study 

also includes a proposed 400-MW pumped storage hydro facility called Iowa Hill.

7.3.6 Wind Power Penetration and System Flexibility

The cases with higher penetration levels correspond to greater geographic diversity of the wind 

resources. Flexibility is calculated using 10-minute wind and load data. In the full report for the 

project, a step-by-step procedure is outlined to determine the required flexible generation that 

will be needed each hour to account for differences between the scheduled resources and the 

real-time load.

PIRP is a state initiative to assist in the integration of variable renewable resources. The PIRP 

and non-PIRP cases address levels of variability that will be absorbed by CAISO or left to 

SMUD alone. CAISO will pick up the deviation from forecast on an hourly basis. There is an 

associated $0.10 USD/MWh forecasting fee to join this program and pay for the AWS Truewind

forecast, with the balance paid for the cumulative net error at the end of the month.

7.3.7 Wind and Hydro Integration – Benefits and Impacts

There were several outcomes of the project related to the impacts of wind integration and the 

benefits of hydropower in addressing these impacts. To compensate for almost all of the load 

fluctuations, fast responding regulation capacity equal to some multiple of the standard 

deviations would be necessary. Integration studies with other control areas have found this factor 

to range up to 5 (i.e., 5σ). The average of 5 times the standard deviation (5σ) of the SMUD load 
IEA Wind Task 24 Final Report, Vol. 2

118

regulation characteristic for the 14 days analyzed is approximately 18 MW. The average as a 

percentage of peak load is 1.15%. Therefore, the conclusion of this analysis is that the minimum 

up-regulation and down-regulation capacity for the SMUD control area is up to 18 MW. Upregulation in the morning will be higher to account for two-thirds of the expected load pick up 

over the hour, with down-regulation set to 18 MW. In the evening as the load ramps down, 

down-regulation will again be two-thirds of the expected hourly decrease, with up-regulation at 

18 MW. 

Applying the findings from the NREL measurement program and subsequent analysis, the 

standard deviation of the regulation characteristic for each of the scenarios would be 1 MW, 

1.6 MW, 2.12 MW, and 2.91 MW for the four cases, respectively. If the regulation 

characteristics of the individual subsets are truly uncorrelated, the regulation characteristic of the 

combination can be calculated from the statistics of the individual characteristics. Using this 

method, the effect on the system fast requirement can be determined for each of the four cases. 

The results, shown in Table 40, show that the fast regulation requirement increases to as much as 

5.1 MW with 850 MW of wind generation. Additional requirements for hourly variability and 

schedule deviation can raise the operating reserve requirements as high as 200 MW for 850 MW 

of wind, as shown in Table 41.

Table 40. Wind generation increase figures

Wind Generation System Fast Regulation 

Requirement

Increase due to Wind 

Generation

0 MW (Load Only) 18.0 MW -

100 MW 18.7 MW 0.7 MW

250 MW 19.7 MW 1.7 MW

450 MW 20.9 MW 2.9 MW

850 MW 23.1 MW 5.1 MW

Table 41. Operating reserve requirement hourly variability

Case Average Hourly Flexibility 

for Variability (+/-)

Average Hourly Flexibility 

for Variability and 

Schedule Deviation (+/-)

0 MW (Load only) 14.6 MW 26.6 MW

100 MW 18.4 MW 47.8 MW

250 MW 26.4 MW 112.9 MW

450 MW 39.8 MW 146.2 MW

850 MW 46.0 MW 201.1 MW

The study also estimated the integration costs for the different cases under several scenarios. The 

results show that all case sets except the Iowa Hill base show a peak in integration costs (in 

$ USD/MWh) at Case 2, which is 250 MW, all at Solano. This case represents the least diverse 

scenario of the four cases, which partially accounts for its higher costs. The results for each of 

the cases are shown in Table 42. They are also shown in Figure 44 and Figure 45, which provide 

useful ways to visualize the results for comparison.
IEA Wind Task 24 Final Report, Vol. 2

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Table 42. Integration costs ($ USD/MWh) for the four cases

Case Set Case 1 Case 2 Case 3 Case 4

Base 4.48 7.41 5.12 3.96

Base Discounted Forecast 7.80 9.16 4.87 0.97

Base PIRP w/ Discounted Forecast 7.38 7.67 4.49 0.71

Iowa Hill Base 5.12 3.08 2.22 1.67

Iowa Hill JIT Commit 4.94 5.77 0.20 -0.12

The results show a very substantial reduction in operating cost and integration costs with Iowa 

Hill operating. The results also show that integration costs decrease with increasing diversity of 

wind generation assets. 

Summary of Integration Cost by Case Set

-2.00

0.00

2.00

4.00

6.00

8.00

10.00

Base Base Discounted

Forecast

Base PIRP w /

Discounted

Forecast

Iow a Hill Base Iow a Hill JIT

Commit

Integration Cost ($/MWh)

Case 1

Case 2

Case 3

Case 4

Figure 44. Integration cost summary by case set
IEA Wind Task 24 Final Report, Vol. 2

120

Summary of Integration Cost by Case (Wind Penetration Level)

-2.00

0.00

2.00

4.00

6.00

8.00

10.00

Case 1 Case 2 Case 3 Case 4

Integration Cost ($/MWh)

Base

Base Discounted Forecast

Base PIRP w / Discounted Forecast

Iow a Hill Base

Iow a Hill JIT Commit

Figure 45. Integration cost summary by case (wind penetration level)

7.3.8 Conclusions 

The study investigated the affect on SMUD’s control area of four levels of wind generation: 

102 MW, 250 MW, 450 MW, and 850 MW. Specifically, the project sought to investigate the 

effect on the fast regulation requirement and integration costs of wind energy for the different 

cases. The study found lower penetrations of wind generation have only a small impact on fast 

regulation requirements, but begin to dominate as the penetration increases. The results show a 

very substantial reduction in operating cost and integration costs with Iowa Hill operating (as 

much as $5 USD/MWh). Furthermore, the results also show that integration costs decrease with

increasing diversity of wind generation assets. 

The authors determined that the integration cost drops significantly with the wind penetration 

level. At first, this seems counterintuitive since it would seem likely that more wind would 

require less efficient commitment to handle the uncertainty and variability in the wind energy 

delivery. One aspect of lowering the effect of higher wind penetrations is increased geographic 

diversity. Cases 1 and 2 are concentrated scenarios with all of the turbines in a relatively small 

area, and are affected by essentially the same meteorology at the same time. In Cases 3 and 4, the 

wind plants are scattered over a much greater geographic area. This tends to smooth the wind 

because while one site may have low wind another may have high wind.

The modeling conducted showed that unit commitment and dispatch become difficult at 

penetration levels of 850 MW without the Hour Ahead Scheduling Process, yet work very well at 

the 450-MW level. Although the cases that include involvement in the PIRP will require fewer 

reserves to be provided by SMUD, there are only very small decreases in the integration costs 

that result from these cases. Changes in the Hourly Ahead Scheduling Process market structure 

could significantly affect integration costs. A more detailed treatment of error analysis will yield 
IEA Wind Task 24 Final Report, Vol. 2

121

more accurate results. Wind forecasting error, load forecasting error, and any relation between 

the two should be studied. 

7.4 Case Study: Grant County PUD, Columbia River

7.4.1 Introduction to Study

• Study description and goals: The Grant County PUD No. 2 (Grant PUD) was interested in 

studying ways to expand its wind energy generation through effective integration with its 

hydropower operations. Grant PUD owns and operates the two-dam Priest Rapids Project on 

the Columbia River in central Washington, one of the largest hydropower developments in 

the United States. Grant PUD also purchases a share the 63.7-MW Nine Canyon Wind 

Project. The two primary goals of the study were: (1) to understand the impacts of Grant 

PUD’s current efforts at integrating wind and hydropower, and (2) to study the potential for 

future expansion of wind integration. In addressing these goals, the Grant PUD sought to 

understand the impacts of wind integration on its hydro operations, including effects on spill; 

approximate an economic value for the wind energy; and, most importantly, identify the 

frequency and magnitude of surpassing generation limitation or dropping below minimum 

flow requirement (for fish survival). 

• Study results focus: The study focused on three primary interest areas: (1) wind power effects 

in the regulation and load following time frames; (2) impacts associated with system 

planning in the unit commitment time frame, including the influence of wind energy 

forecasts, and impacts on the hydrological operations; and (3) application of study results to 

actually extending the district’s wind integration, and mitigating any undesirable effects of a 

non-negligible penetration level over current management requirements. The objectives 

inherent of this three-part process include determining current operating behavior and 

requirements (regulation and load following), developing metrics with which to objectively 

compare operation variations, selecting a finite number of key scenarios to examine, and 

assessing the impact on flow constraints and generation limits. Statistical summaries of the 

magnitude and frequency of the metrics’ variations could then be used to evaluate impacts of 

an actual penetration expansion and propose a few leading scenarios.

• Organizations involved: Three main organizational entities were involved. Northern Arizona 

University completed original analyses and generated summaries for Grant PUD and NREL.

Grant PUD provided operations and management reference and expertise, and guided efforts 

to ensure Northern Arizona University’s modeled actual operation. Detailed wind forecast 

histories for the Nine Canyon Wind Project were provided by 3TIER.

• Reason study was performed: The study was conducted to analyze the impacts that increased 

intermittent generation will have on system operations, reliability, reserve requirements, and 

overall integration costs. The study was conducted to analyze the impact that increased 

intermittent generation will have on the existing operating structure of Grant PUD, including 

the impacts on reliability and reserve requirements.
IEA Wind Task 24 Final Report, Vol. 2

122

• Case study chapter has been condensed from the following reports: 

o Acker, T.L., Buechler, J., Knitter, K., and Conway, K. (2007).“Impacts of Integrating 

Wind Power into the Grant County PUD Balancing Area,” proceedings of the 2007 

AWEA Windpower Conference, Los Angeles, CA.

o Acker, T. L., Knitter, K., Conway, K., and Buechler J. (2006). Wind and Hydropower 

Integration in the Grant County Public Utility District, Washington, Proceedings of the 

2006 Hydrovision Conference, Portland, Oregon, U.S. Acker, T. L. and Buechler, J.,

Final Report: Grant County Public Utility District

o Wind Integration Study, project report submitted to the U.S. National Renewable Energy 

Laboratory, 2008.

• Intended outcomes of the report relative to the objectives of Task 24: Goals of the study were 

to determine the impacts of wind integration on the system regulation and load following, 

and to determine if how wind integration within this predominantly hydro utility effect 

compliance with generation and flow constraints.

7.4.2 Overview of Power System

Table 43. First half of the Task 24 matrix that defines important power system characteristics and 

some basic parameters of the setup for the U.S. Grant PUD wind integration study

Study conducted by: Grant County Public Utility District No. 2, of Grant County, Washington, 

U.S., the National Renewable Energy Laboratory, and Northern Arizona University, Flagstaff, 

AZ

Geographic area of study: Grant County, Washington, USA

Area: 7,229 km2 (2,791 mi2) (source: Wikipedia)

Population: 75,000

Relatively small control/balancing area 

Power system characteristics: 

Load Conventional 

generation Interconnection Wind power

Peak (MW) Min (MW) TWh/a Capacity (MW) Capacity (MW) MW TWh/a

610 load

(809 including 

purch & sales)

89 load

(9 including 

purch & 

sales) 

4.4 879 MW hydro

17 MW diesel

n/a in this study 12, 63, 

150

0.15, 

0.80, 

1.90

Other relevant characteristics of power system: dual peak (winter and summer); average load 

about 250 MW; generation resources almost entirely hydroelectric; several limiting flow 
IEA Wind Task 24 Final Report, Vol. 2

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restrictions through dams located on Mid-Columbia River. Interconnected with Bonneville 

Power Administration balancing area.

Characteristics of system planning: 

Three basic time frames: Term (1-2 years down to 1 week), Preschedule (1 week to 1 day), Real 

time (during operators shift). Utility is vertically integrated (responsible for generation, 

transmission and distribution). Several dams are on the Columbia River upstream of Grant PUD, 

and their operation affects Grant PUD’s two dam Priest Rapids project. Thus the mid-Columbia 

dams cooperate in optimizing the hydro system through the Mid-Columbia Coordination 

Agreement. Electrical dispatch is done via this agreement.

In terms of Grant PUD’s load and resource planning and operation, allocating excess or securing 

additional generation resources in the monthly to annual time frame is characterized as a “term” 

transaction. These term transactions ensure a balanced load and resource portfolio under 

changing river conditions and needs, and are typically bi-lateral transactions negotiated between 

trading partners, and not secured though an open market transaction. In the day-ahead to weekahead time frame, the load and resource planning transactions are scheduled during the “preschedule” period, and transactions occurring during the day of operation occur in “real time.” 

The marketer’s make decisions related to pre-schedule and term transactions, whereas the 

system operator is responsible for the real time transactions. The primary considerations that 

govern planning in the pre-schedule time frame are the Pacific Northwest Coordination 

Agreement (PNCA) elevation targets defined for Grand Coulee, the NPCs, the actual hydrology 

and weather, the anticipated system load, how Grand Coulee is dispatched, the flow estimates 

provided by USACE, the market conditions for electricity, and the sales and purchase 

obligations made prior to the preschedule (i.e., term transactions). As with the term transactions, 

most of the pre-schedule and real-time transactions are bilateral between Grant PUD and a 

willing partner. 

Description of market: Purchases and sales made at the Mid-Columbia hub; market is not very 

liquid and cannot always be depended upon. Preference of utility is therefore to only rely on 

their hydro resources to account for effects of wind variability. 

Integration time frames of importance: 

Yes/No Time Frame

No Frequency, system dynamics, grid stability, LVRT, PSS, V-Reg

Yes Regulation, AGC

Yes Load following; intra-hour ramping; economic dispatch

Yes Unit commitment and day-ahead scheduling; economic utilization of resources

No Resource and capacity planning; reliability

7.4.3 Study Methodology

The objective of this case study was to perform a statistical study of system regulation, load 

following, and ramping characteristics, and to also perform a simple hourly simulation to 

determine the impact of wind forecast error on system generation limits and minimum flow 

constraints. Table 44 provides an overall summary of the study technique and assumptions that 

were employed. A list of key assumptions and limitations follow the table. All data used for the 

study were from actual operation during the year 2006.
IEA Wind Task 24 Final Report, Vol. 2

124

Table 44. Second half of the Task 24 matrix that lists variations of underlying assumptions and 

modeling approaches used in the U.S. Grant PUD wind integration study

Set Up

A Aim of Study 1 – what happens with a certain number of GWh of wind 

(primary objective of study)

2 – how much wind is possible (secondary objective of study)

M Method to Perform Study 1 – add wind energy

S Simulation Model of 

Operation 

2 – deterministic simulation several cases

Simulation Detail

R Resolution of Time 3 – minute/second

P Pricing Method 1 – costs of fuels etc (1st attempt)

2 – prices for trading with neighbors (2nd attempt)

D Design of Remaining 

System 

1 – constant remaining system

Uncertainty and Balancing

I Imbalance Calculation 3 – wind+load+ production

B Balancing Location 1 – dedicated source (Grant PUD’s hydro facilities)

U Uncertainty Treatment 2 – hydro inflow uncertainty

3 – no wind forecasts (assume persistence)

4 – best possible wind forecasts

Power System Details

G Grid Limit on Transmission 1 – no limits

H Hydropower Modeling 1 – head height considered

2 – hydrological coupling included (including reservoir capacity)

3 – hydrological restrictions included

5 – consider most relevant aspects of affected hydro resources

HC Hydro Capacity Service 2 – real-time integration with impact of wind variability on 

system managed with hydro

T Thermal Power Modeling No significant thermal facilities in Grant’s balancing area.

W Wind Power Modeling 3 – time series smoothing considered

- Wind speed measurements from anemometers in county

- Actual wind output from nearby wind plant

7.4.3.1 Assumptions

• Hydro, wind, and load data all time synchronized, and 1-minute data from the actual hydro 

and wind power production and load were employed for the study year. Short periods of 

missing data could be linearly interpolated at the discretion of researchers.

• The following wind capacity values were used: 0 MW, 12 MW, 63.7 MW, and 150 MW, and

the actual wind production data used are from the Nine Canyon Wind Farm in south-central 

Washington State.
IEA Wind Task 24 Final Report, Vol. 2

125

• Wind forecast data being used was the actual professional forecasts available for wind 

project during study year. Load, generation, and forecast data are exactly what Grant PUD 

had to work with at the time the data was first available.

• Market prices being used were the actual recorded hourly market prices at Mid-Columbia 

hub. Transactions made at this price were assumed not to affect the market (the megawatthour size of the transactions were all quite small compared to the volume traded daily).

• Forecasted wind data was available each hour of the study year for the following 140 hours.

• Load and generation data were available by minute, while forecast data was only available 

by-hour.

• System flow and generation constraints were available on an hourly basis for the study year.

• Grant PUD’s generation requests cannot exceed 95% of its generating capacity. The 

remaining 5% of capacity is set aside for “contingency reserves.”

7.4.3.2 Limitations

• Forecasts only considered when planning day-ahead.

• No intra-day adjustments were made to the schedule once it was set in the day-ahead 

planning. The purpose was to see how frequently problems arise when using considering 

wind and planning for it day-ahead. The goal in doing this was to identify the magnitude and 

frequency of constraint exceedences would occur and need to be dealt with hour ahead or in 

real time.

• All pre-schedule planning was conducted the day before operation. In reality, there is some 

2-day-ahead planning conducted on weekends, holidays, etc.

• There is only about 1.5 months during the year when no fish flow restriction are in place 

affecting generation.

7.4.3.3 Wind Power Characteristics

Wind power generation levels of 0 MW (for comparison purposes), 12 MW (actual), 63.7 MW

(full output from Nine Canyon Wind Power Plant), and 150 MW (scaled from 63.7 with no 

smoothing) were considered. Because Grant PUD was interested in how the wind power 

impacted its minute-to-minute regulation and hour-to-hour load following, it was of interest to 

tabulate the variation of the wind power plant on these time scales. Figure 46 shows the 1-minute 

changes in output out of the 63.7-MW Nine Canyon wind power plant for the 2006 data being 

considered. As can be seen, about 95% of the 1-minute changes are 1 MW or less (below 1% of 

capacity). The hourly changes in generation, tabulated as the change between the wind power 

generation at the beginning of each hour, are shown in Figure 47. The preponderance of 

generation changes were less than 10% of plant output, and all hourly changes were less than 

about +/- 50% of the total wind power capacity. Note that the variations shown here become 

small as a percent of capacity if multiple, geographically diverse wind plants are employed.
IEA Wind Task 24 Final Report, Vol. 2

126

Figure 46. Frequency of 1-minute wind power generation changes for data year 2006, as computed 

by finding the difference between the actual generation and a 10-minute rolling average

Figure 47. Frequency of hourly generation changes found by comparing the wind generation at 

the top of each hour for data year 2006
IEA Wind Task 24 Final Report, Vol. 2

127

Figure 48. Illustration of the method to identify the magnitude and duration of wind power ramps

Figure 49. Cumulative exceedence curve showing the proportion of wind power ramps that 

surpass a given ramp rate

7.4.4 Hydro System Characteristics

Grant PUD’s Priest Rapids Project (including both Wanapum and Priest Rapids dams) is located 

in central Washington in what is called the mid-Columbia portion of the Columbia River (see 
IEA Wind Task 24 Final Report, Vol. 2

128

Figure 50. Map of dams in the Columbia River 

Basin 

(Source: USACE, 

www.nwd.usace.army.mil/ps/home.asp)

Figure 50). The two dams have a nameplate capacity of just under 2,000 MW, of which nearly 

880 MW are devoted to Grant PUDs load and balancing area. With many hydroelectric facilities 

located along the river, hydro operators realized early on that there were important benefits to 

coordinating their operation. In 1964, this cooperation was formalized in the form of the PNCA. 

A main goal of this agreement was to try to optimize benefits of the hydro facilities all along the 

river by emulating the coordinated operation that could be achieved if operated by a single 

owner. Priorities of this agreement include producing firm energy based on very low historical 

stream flows, refilling reservoirs, and producing surplus energy with higher stream flows. 

Energy production, however, is subsidiary to meeting NPCs. This agreement sets flow 

requirements from the Priest Rapids dam needed for the spawning of Columbia Fall Chinook 

salmon. General priorities in planning due to NPCs and power generation (order of priority 

changes based upon time of year, system operational characteristics, etc.): 

1. Fish migration 

2. Flood control (note flood control will take precedence over fish migration when dam 

structures are threatened)

3. Recreation and lake levels

4. Navigation

5. Power production

Priest Rapids Dam is subject to significant 

NPCs. The area of the Columbia River 

below the dam is referred to as the

Hanford Reach, which extends 82 km

downstream of the dam to Richland, 

Washington. The Hanford Reach supports 

the larger of two remaining healthy 

naturally spawning fall chinook salmon 

populations in the Columbia River 

System, and is a important component of 

the Pacific Salmon Treaty between the 

United States and Canada. River flows for 

this section of the Columbia River are 

controlled by discharge from Priest Rapids 

Dam, which can fluctuate due to changes 

in hydroelectric power output, or for 

reasons related to irrigation, water storage, 

and flood control. These fluctuations have 

impacted the spawning, incubation, 

rearing and transportation of fall Chinook 

salmon in the Hanford Reach. In order to minimize this impact and to encourage a healthy 

salmon population, Priest Rapids must conform to NPCs that substantially affect energy 

production and operational flexibility. These NPCs effect the operation and energy production of 

the projects, and most are reflected in the capacity and energy numbers computed in the PNCA 

MidColumbia 
IEA Wind Task 24 Final Report, Vol. 2

129

process. Some of these NPCs also affect Wanapum due to its close proximity to Priest Rapids.

The main NPCs due to fish protection are summarized below: 

• Modified Spawning Operations: Beginning in mid-October and continuing for about 

5 weeks, the daytime capacity at Priest Rapids is severely restricted, and Priest Rapids is 

forced to generate at high levels during the nighttime hours. In prior years, this mode of 

operation was opposite to what is generally desired and was, therefore, called Reverse Load 

Factoring. However, in 2006 this practice was modified such that flows were generally 

higher at night, pulsed to full capacity discharge for a short (~ 1 hour) duration towards the 

beginning and end of the high load hours. In order to maximize the survivability of Fall 

Chinook redds in the Hanford Reach, which are believed that Fall Chinook salmon to 

primarily spawn during the daylight hours, these measures are taken to encourage salmon to 

spawn at lower elevations in order to maximize the probability of having enough flow during 

the incubation period to protect the redds. 

• Protection Level Flows: Following the Reverse Load Factoring Operation, Priest Rapids has 

high minimum flows during the incubation period, which generally lasts until mid-April. 

• Rearing Period Operations: Once the salmon hatch on the Hanford Reach, Priest Rapids 

conducts rearing period operations from mid-March until June. During the rearing period, the 

discharge flow band from Priest Rapids is substantially limited in order to prevent temporary 

pools of water from forming along the banks of the river, which cause small salmon to be 

stranded and possibly die from lack of oxygen. 

• Fish Spill: From April 16 to August 31, both Wanapum and Priest Rapids spill substantial 

amounts of water past the turbines, varying for the purpose of fish passage. This spill has 

recently varied from 39% to 61% of the total discharge. 

These flow constraints exert a governing influence on the generation capacity available at the 

Priest Rapids Project, along with the PNCA plan. The upper and lower limits on capacity vary 

seasonally, daily, and even change within the day (e.g., during the modified spawning 

operations). A plot depicting the variation of the maximum and minimum capacity in megawatts 

is available to Grant PUD at the Priest Rapids and Wanapum dams is shown in Figure 51. This 

plot was made using 1-minute data, and reflects all constraints on generation. From the plot, one 

can see the tight band on generation (high minimum capacity) during the rearing period fish 

operations, and the unusual looking mix of minimum/maximum capacities during the modified 

spawning operation. There is only about 1.5 months during the year when no fish flow restriction 

is in place that affects generation.
IEA Wind Task 24 Final Report, Vol. 2

130

Maximum 

Hydro 

Capacity

Minimum 

Hydro 

Capacity

Protection 

Level Flows

Rearing Period

Fish Spill Modified Spawning

Figure 51. Variation of the maximum and minimum capacity (MW) available to Grant PUD at the

Priest Rapids and Wanapum dams

7.4.5 Wind Power Penetration and System Flexibility

The penetration of wind power penetration considered in the project is 12 MW (1.8%), 63.7 MW 

(7.8%), and 150 MW (18.6%), computed as a percentage of peak load (including sales of 

energy). The hydropower generators themselves are quite flexible, primarily being 100-MW 

units. Use of the hydro is constrained by its limited hydro impoundment (on the order of 

1–2 days of river flow); the fact that it is at the end of an interconnected system of seven dams 

(see Figure 50, with Grand Coulee dam standing at the head of the mid-Columbia and essentially 

dictating flow that will come to Grant PUD facilities); and the significant fish flow constraints. 

Even with these fairly onerous constraints, there is still substantial flexibility in use of the hydro 

resource as demonstrated by the graphs shown in Figure 52. On the left of this figure is the 

monthly averaged diurnal profile of the system load, and on the right side is a plot showing the 

average diurnal profile of the generation request to cover the system load plus purchases and 

sales. As can be seen, Grant PUD uses the flexibility available to maximize energy sales during 

the high load hours of the day.
IEA Wind Task 24 Final Report, Vol. 2

131

Hour of day

Average System Load diurnal profile by month

Power (MW)

Hour of day

Average System Request diurnal profile by month

Power (MW)

Figure 52. Average diurnal profile of Grant PUD system load (left) and generation request (right), 

for the study year 2006

7.4.6 Wind and Hydro Integration – Benefits and Impacts

This wind integration impact described in this section will focus on the impacts of integrating 

wind power into the Grant PUD balancing area. Specifically, the changes in regulation 

requirement, load following (in this case, hourly changes in generation), and impact on 

complying to generation limits and flow constraints (collectively referred to as “exceedances” 

here). The specific details of the calculations required to demonstrate these impacts is left to the 

reference documents listed at the beginning of this case study section. 

The changes in 1-minute regulation values of the system net load (load minus wind generation) 

due to the various levels of wind power being considered is shown in Figure 53. Note that the 

histogram (i.e., bar chart) looks very similar regardless of the wind penetration level, and that the 

standard deviation of one-minute deviations from the 10-minute rolling average does not change 

much. This result that wind power does not substantially affect the regulation is consistent with 

other studies.

Moving to the hourly time frame, Figure 54 shows a bar chart demonstrating the 1-hour changes 

in generation request (load net wind + purchases – sales) for the wind penetration rates being 

considered. The general shape of the distribution is normal, centered about a mean near 0. The 

light blue bars correspond to the hourly changes in the actual generation requests, including 

12 MW of wind (1.8% penetration). The standard deviation was 43.8 MW, essentially identical 

to the case where no wind energy is included in the system. The yellow bars shown in the figure 

represent the hourly variability if the full 63.7 MW of Nine Canyon wind were incorporated into 

the generation request (7.8% penetration). This produces a noticeable effect in the distribution, 

causing a significant dip in the distribution near 0 and increased incidence of higher hourly 

changes. The standard deviation in hourly changes is 44.6 MW. The difference in the standard 

deviation between including 0 and 63.7 MW of wind is 0.8 MW. If one were to simply scale up 

the output from Nine Canyon to 150 MW (which will over predict its variability), 
IEA Wind Task 24 Final Report, Vol. 2

132

Figure 53. Bar chart showing frequency of one-minute “regulation” values (MW) for the Grant PUD 

net load (load + purchases – sales – wind power)

Figure 54. Bar chart showing frequency of hourly load following values (MW) for the Grant PUD 

net load (load + purchases – sales – wind power)

as shown by the red bars, the standard deviation increases only modestly to 48.8 MW. The more 

significant impact from the increased wind penetration, both at 63.7 MW and 150 MW, however, 

Wind Reg. 

Wind Penetration Std. Dev.

(MW) (%) (MW)

0 0.0% 3.47

12 1.8% 3.47

63.7 7.8% 3.51

150 18.6% 3.72

2006Data; 480,960 min

GCPD LimitedRequest

10 minute rolling ave

1

1 2

2

−

+ = − ∑

+

= −

n t

n i t

t t i x

n

R x

Wind Reg. 

Wind Penetration Std. Dev.

(MW) (%) (MW)

0 0.0% 43.80

12 1.8% 43.77

63.7 7.8% 44.62

150 18.6% 48.75

2006Data, 8016 hours

GCPD LimitedRequest

10 minute rolling ave

1 if

1 if

( )

( ) (min req) (max req)

min max

max min

−







− >

+ > =

= −

t t

t t

t

LF t t hour hour

δ

δ
IEA Wind Task 24 Final Report, Vol. 2

133

is the altered shape of the histogram. The number of incidences of 0–10 MW hourly generation 

changes (either positive or negative) is decreased, with a consequential increase of hourly 

generation changes primarily in the 10–40 MW range. Because Grant PUD’s system operator 

frequently runs the system within a couple percent of its maximum permissible generation 

(allowing for reserves), an unexpected (i.e., unplanned or not forecasted) change in generation 

could incur high costs to manage, cause violation of North American Electricity Reliability 

Corporation (NERC) reliability requirements, and/or violate a non-power flow constraint (i.e., 

fish flow requirement). Thus, two important conclusions from the load following results are that 

the system already deals with a significant amount of variability, and that the ability to handle 

the increase in overall variability must be dealt with in the day-ahead and hour-ahead planning 

operations to avoid exceeding generation limits.

In order to determine the effect of wind on exceeding the limiting capacity values, it was first 

necessary to devise an algorithm for planning wind in the pre-schedule. The method chosen here 

was to use the actual wind forecasts for the Nine Canyon project to estimate the amount of high 

load hour (HLH) and low load hour (LLH) wind energy would arrive during the day of

operation. The amount of energy expected is then sold day-ahead as a flat block of HLH or LHL 

energy and represents additional capacity in Grant PUD’s system. This transaction is carried out 

with two simplifying assumptions: (1) the flat block is sold in the amount estimated and not as a 

25-MW block (which is done for a typical transaction); and (2) only consider pre-schedule 

planning 1-day ahead (i.e., the weekend days are planned 1-day ahead similar to the weekdays).

The actual system request that occurred during each day of the year is then modified by adding 

the flat block capacity to the actual system request during either the HLH and LLH hours, as 

appropriate. Then, during the day of operation, the wind energy comes into the system with its 

actual profile (different than the flat block), and is used to meet the increased system request. If 

the wind were to happen to come into the system as a flat block, then it would decrease the 

system request and return it to the profile that actually occurred. However, since this never 

occurs in practice, the system request will be higher during some minutes and lower during 

others compared to what actually occurred. The modified request is then compared to the 

minimum and maximum capacities and the exceedences are tabulated. In performing this 

calculation, the maximum available capacity of the system is reduced because the overall load 

serving obligation of Grant PUD has increased, and it is now responsible for setting aside 5% of 

the increased obligation as a contingency reserve. For example, if the maximum capacity during 

a given HLH happened to be 800 MW, and 40 MW of wind power was sold in the pre-schedule 

(day ahead) as a flat block for this HLH period, then the maximum available capacity is reduced

by 5% of 40 MW, or 2 MW. Thus, the new maximum capacity during the HLH is 798 MW, and 

this value is used in computing the percent of maximum capacity consumed by the modified 

system request. Implementing this algorithm yields the results indicated by the blue bars in 

Figure 55 and Figure 56, which plot on a bar chart the number of exceedences during the year 

versus duration. As shown in Figure 55, some of the shorter duration HLH exceedences of the 

95% capacity threshold are shifted to longer durations, and there is an overall increase, though 

modest, in the total number of exceedences. Figure 56 shows the effect of day-ahead wind sales 

on the LLH minimum capacity exceedences (dropping below the minimum capacity, and 

therefore dipping below the minimum flow permitted from Priest Rapids Dam). With respect to 

this limit, the effect is not as pronounced, with an increase only in the short-duration violations. 

Note that no attempt has been made in this analysis to see if the increased violations could be 

averted by real-time or hour-ahead transactions. Rather, the intent was to assess the impact on 
IEA Wind Task 24 Final Report, Vol. 2

134

the number of exceedences under a reasonable planning algorithm. These results do show that an 

increased number of exceedences do occur, but that the increase is only modest. It is also 

possible that many of these exceedences could be handled during the day of operation, and at 

some cost. Addressing this latter point would be the next logical step for Grant PUD in 

continuing this analysis.

Figure 55. Number of exceedences of the 95% available capacity limit during the 2006 model year 

for the system as run and with 63.7 MW of wind planned into the system in the pre-schedule

Figure 56. Number of exceedences (dropping below) the minimum allowable capacity due to a 

flow constraint for the system as run and with 63.7 MW of wind planned into the system in the 

preschedule
IEA Wind Task 24 Final Report, Vol. 2

135

7.4.7 Conclusions

The goals of this project were to understand the impacts of Grant PUD’s current efforts at 

integrating wind and hydropower into their electrical system operation, and in particular to 

understand the impacts on exceeding system constraints. These constraints are the minimum and 

maximum capacity constraints for the Priest Rapids Project, due to reliability and NPCs (fish 

flows). Study results for the 2006 data year suggest that the overall impact on system statistics 

for regulation and load following is quite modest, even at a wind energy penetration of 150 MW 

(~19% wind penetration by capacity). This small statistical impact suggests that, absent other 

constraints, the physical generation resources are sufficient to handle wind variability at this 

level. However, due to changes in the distribution of load following hourly changes, there are 

some potentially significant operational challenges in scheduling the resources without infringing 

upon system constraints. To assess the impact on system constraints, a pre-schedule planning 

simulation was devised and conducted. Results of this simulation indicate that when using a 

professional quality forecast, that an increased incidence of constraint exceedences can occur, 

though at a modest level. The new instances of exceedences would need to be handled in the 

hour-ahead and real-time operations, which appears may be a reasonable approach. Assessing the 

significance of these constraint exceedences and testing other pre-schedule planning algorithms 

would be an appropriate next step in furthering this analysis.
IEA Wind Task 24 Final Report, Vol. 2

136

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Adequacy Planning; pp36-42, April 2009.

RETSCREEN Wind Energy Project Case 

Study, www.retscreen.net/download.php/fi/841/3/WIND08-C.pdf (accessed December 2009).

Smith, J.C, Parsons, B., Acker, T., Milligan, M., Zavadil, R., Schuerger, M., and DeMeo, E., 

(2007) “Best Practices in Grid Integration of Variable Wind Power: Summary of Recent US 

Case Study Results and Mitigation Measures,” Proceedings of the 2007 European Wind Energy 

Conference, Milan, Italy, May 2007.

Söder, L. and Holttinen, H., (2008). “On methodology for modeling wind power impact on 

power systems,” Int. J. Global Energy Issues, Vol. 29, Nos. 1/2, pp.181-198.
IEA Wind Task 24 Final Report, Vol. 2

139

Appendix A: Hydro Quebec Case Study

The information in this document is being provided as a supplement to the Task 24 report 

entitled:

Final Technical Report:

Results of IEA Wind Task 24 on the Integration of Wind and 

Hydropower Systems

Volume 2: Participant Case Studies

Hydro-Québec was an active participant in Task 24, but was not able to contribute this report to 

the final report mentioned above until the related work was approved by Hydro-Québec 

management for public dissemination. It was known during the task that this date of release was 

after the date of publication of the final report, so this document is being provided as a 

supplement to Volume 2. Since Volume 2 finished with Chapter 7, this supplement is included as 

Appendix A.

Canada – Hydro-Québec – Preliminary Impacts

Introduction

By developing Québec's hydropower potential, Hydro-Québec has already demonstrated its 

commitment to sustainability with a green and renewable energy and now is turning increasingly 

to wind power as a complementary source of renewable energy. Starting in 1999, with a few 

wind projects reaching 500 MW, the Québec government has decided by order that its electricity 

portfolio must contain an additional 3500 MW of wind power by year 2015. The total wind 

power will be around 5% of its annual energy supply or 10% of its installed capacity. That is the 

only scenario to be studied until now.

Except for a bloc of 500 MW reserved for small wind projects developed by communities and 

First Nations, for which the call for tenders selection process will terminate at the end of 2010, 

the wind project locations are already known. Characteristics and power time series are well 

defined for around 3500 MW. This report gives the results of wind power integration impacts 

that the Hydro-Québec will have to manage. For the purpose of the Task 24 research, three 

studies were contributed:

1. The first study defines the impacts of wind power on operational reserves, specifically on

AGC and load following reserve.

2. The second study analyses the impacts on the balancing reserve in order to mitigate the 

consequences of inherent prediction errors over the time horizon of 1–48 hours. The 

methodology requires as input the statistical characteristics of load and wind generation 

forecast errors and of generation outages.

3. Finally, the last study considers the impacts on the system capacity adequacy taking into 

account the Nordic weather conditions on the wind turbines availability (stopping when 

the temperature is lower than -30o).
IEA Wind Task 24 Final Report, Vol. 2

140

Before presenting these studies, Section 2 describes the characteristics of the Hydro-Québec 

power system.

Overview of the Power System

Table 45. First half of the Task 24 matrix that defines important power system characteristics and 

some basic parameters of the setup for the Hydro-Québec case studies

Geographic area of study: Province of Québec, Canada

Area: 1 542 000 km2

Around 2 000 km from south to north and 1 500 km 

from east to west

Population: 7,6 millions

Very large control / balancing area, load 

concentrated in the South of the province and power 

generation in the North.

Power system characteristics: 

• Synchronous network covers the Province of Québec and Labrador (Newfoundland)

• Québec Government defines the wind energy policy with a target of 4000 MW for 2015 

and maintains 10% of peak penetration the following years

Load Conventional 

Generation Interconnection Wind power

Peak (MW) Min (MW) TWh/an Capacity (MW) Capacity (MW) MW TWh/an

37 000 14 000 195 32300 Hydro

5400 other hydro 

(Labrador long term 

agreement)

1600 Thermal

700 Nuclear

7,100 import

9,575 export

642 MW 

in 2010

4000 MW 

in 2015

~10 

in 2015

Other relevant characteristics of the power system:

• Most interconnections are AC-DC-AC, others are radial and so all asynchronous. These 

interconnections allow Hydro-Québec to import as much as 8350 MW of electric power or 

export as much as 10 800 MW to neighboring regions.

• Large scale hydro plants equipped with large storage capability (175 TWh)

• Most power plants are far from the load and also far from the first wind plants

• AGC on most of hydro plants. High level of operational efficiency.

• Min / max load ≈ 40% (average out-of-peak and peak daily load)

• Load factor = 55% at peak load
IEA Wind Task 24 Final Report, Vol. 2

141

Characteristics of system planning:

Utilization of in-house optimization and simulation models for different horizons: planning 

(1–2 years), scheduling (1–10 days) and real time.

Description of energy market: 

Hydro-Québec generates (Hydro-Québec Production), transmits (Hydro-Québec TransÉnergie) 

and distributes (Hydro-Québec Distribution) almost all the electricity in Québec. Distribution 

and Transmission activities are regulated. Internal opened market not established. HydroQuébec Production exports/imports to/from the United States and the neighboring Canadian 

provinces under open market rules, mostly on hourly and day-ahead markets. 

Most of the wind power is procured through long-term (~20 years) power purchase agreements 

between Hydro-Québec Distribution and private producers through a request for proposal 

process. Some are directly acquired by Hydro-Québec Production. There is a wind balancing 

service agreement between Hydro-Québec Distribution and Hydro-Québec Production (covering 

a first bloc of 1000 MW of wind power under contract with Hydro-Québec Distribution), which 

involve constant deliveries equal to the average yearly wind power load factor (35%) including 

a capacity guaranty. The agreement should be updated in 2011 and will integrate all of wind 

power projects under contract with Hydro-Québec Distribution.

Wind forecasting is centralized and processed by Hydro-Québec Distribution.

Basic parameters of the study analysis are provided in Table 46.

Table 46. Second half of the Task 24 matrix that lists variations of underlying assumptions and 

modeling approaches used in the first Hydro-Quebec (Canada) wind integration studies

Set Up

A Aim of Study Evaluation of additional balancing and planning reserves 

required by the integration of 3000-MW wind power, while 

maintaining the same system reliability.

M Method to Perform Study Simulate the uncertainties resulting from the addition of 

3000 MW of wind power to existing uncertainties, use these to 

estimate additional risk and balancing reserve requirements

S Simulation Model of 

Operation 

Probabilistic approach and grid simulation model

Simulation Detail

R Resolution of Time Minutes for frequency regulation reserve 

10 minutes for load following reserve 

Hourly for balancing reserve with horizon of 1–48 hours

Hourly data for evaluation of planning reserve requirements for 

capacity adequacy

P Pricing Method No price estimation

D Design of Remaining 

System 

Remaining system unchanged
IEA Wind Task 24 Final Report, Vol. 2

142

Uncertainty and Balancing

I Imbalance Calculation For regulation and load following reserve: wind + load 

For balancing reserve: Wind + load + unavailability due to 

outages

B Balancing Location Only Hydro-Québec area

U Uncertainty Treatment For regulation and load following reserve: load forecast and 

assuming persistence for wind forecast

For balancing reserve: load and wind forecast 

Power System Details

G Grid Limit on Transmission Use dynamic simulation only for regulation and load following 

reserve

H Hydropower Modeling Use dynamic simulation only for regulation and load following 

reserve; consider only operational reserve and efficiency losses 

without adjustment on hydro conditions

HC Hydro Capacity Service Complete integration of hydro resources with wind resources

T Thermal Power Modeling NA

W Wind Power Modeling For regulation and load following reserve: reconstituted hourly 

wind power data and interpolation for 1-minute data

For balancing reserve: wind forecast experience on 1 year

For capacity value: 36 years of hourly time series 

(reconstituted)

Case Study 1: Frequency Regulation Reserves for Integrating 3,000 MW of Wind 

Generation at Hydro-Quebec

I. Kamwa, A. Heniche, R. Mailhot, A. Robitaille

Introduction 

This study analyses the impacts of 3000 MW of wind power on operational reserves, specifically 

on AGC and load following reserve in a context of a predominant hydro generation system and

with asynchronous interconnections.

Giving the nature of its power grid which consists of a large domestic load (37,230 MW in 

January 16, 2009) linked asynchronously to the rest of the NPCC through HVDC lines (with 

7,600 MW and 5,100 MW of export and import capabilities), Hydro-Quebec TransÉnergie (the 

regional Independent System Operator [ISO]) has some very specific reserves requirements

which fall broadly in the following categories:

1. Stability (spinning) reserve: At typically 1,000 MW, it represents about 60% of biggest 

single lost of generation. At any time, spinning reserve must not fall below 250 MW.

2. 10-minute operations reserve: At 1000MW, it consists of non-firm sales, interruptible 

load and a large portion of stability reserve. The ISO has 90 minutes to offset any 

violation.

3. 30-minute operations reserve: About 500 MW, this represents 50% of the second-most 

severe single loss of generation. The ISO has 4 hours to offset any violation.
IEA Wind Task 24 Final Report, Vol. 2

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4. Frequency regulation reserves: AGC with 500-MW (minimum) modulation range are 

used.

5. Load following: No strictly defined standard exists because the large hydro-generation 

base (43,000 MW in 2009) allows for a load following without any practical constraint. 

However, the largest observed load-following requirement is about 3,000 MW during the 

winter mornings.

To prepare large-scale wind integration into its system operations processes, TransEnergy 

undertook a preliminary analysis of the impact of wind variability and uncertainty on operational 

time frame reserves requirements, essentially within the 1-hour horizon. It was rapidly clear that 

the first three reserves categories are not sensitive to wind energy integration essentially because 

wind plants are limited in size (less than 200 MW) and geographically spread over relatively 

large areas (1,000-km stretch). In this context, the most relevant quantities requiring further 

investigation were the AGC and load following reserve capacities. Both are required to ensure 

the reliability of the grid at a level specified by NERC standards CPS1 and CPS2 (BPA 2008, 

Kamwa et al. 2009) in face of short-term demand and wind generation uncertainties.

Figure 57. Load and wind scheduling during a summer morning with high wind generation
IEA Wind Task 24 Final Report, Vol. 2

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For the purpose of this study, the regulation and load following definitions follow Kirby’s 

principle of temporal decoupling (Hirst and Kirby 1999), while assuming that the movingaverage window for separating the load following and the regulation is set at 10 minutes since 

the NERC (CPS2) performance index is evaluated every 10 minutes in AGC systems. Figure 57

summarizes our conventions using a 6-hour observation window at the morning peak on August 

22, 2006. For extra realism, the load data (and corresponding forecasts) are actual historical data 

pertaining to Québec requirements that were provided by the Energy Management System. On 

the other hand, the wind generation data is minute/minute data simulated using the procedure 

described in Kamwa et al. 2009. Regulation (AGC) is simply the difference between the purple 

and blue curves while the intra-hourly imbalance is equal to the difference between the green and 

black curves.

Long-Term Variability of Wind Generation 

The demand data (BRD) is based on forecasts of the regular requirements of the load serving

entity for the 2016 horizon, based on the April 2009 revision of the load forecast. Meanwhile, the 

hourly wind generation data comes from historical reconstitutions of the 3,000-MW hourly 

generation in the first two tenders submitted by Hélimax11

Table 47

. The minute-by-minute data demand, 

wind generation, and forecasts were derived according to Kamwa et al. 2009. 

summarizes some of the typical features of the minute/minute data of the long-term demand and 

wind generation time series. Generally speaking, wind generation has more variability but less 

real-time predictability than the load. Following BPA and CAISO studies, real-time wind 

forecasts are based on a simple 2-hour persistence model. 

 11 Hélimax has published two reports, one on the contracts totalling 990 MW related to the first call for tenders 

(December 2008, 61 pages), the second on the 15 contracts totalling 2,000 MW, signed at the time of the second 

call for tenders (June 2009, 74 pages).
IEA Wind Task 24 Final Report, Vol. 2

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Table 47. Summary statistics of minute/minute data from: 

November 1, 1995 through October 31, 2006

Wind: (Average=1 099, Max= 2 801) MW; BRD: (Average=21 204, Max.= 41 774) MW

An illustration of the data set is provided in Figure 58 where the daily maximum and minimum 

values of the hourly penetration rates are shown on the top curve (i.e.,365 values per year for 

each curves). The bottom curve presents the daily maximum and minimum hourly ramping of 

the wind generation. 

Wind Generation Impacts on AGC and Imbalance

The calculation algorithm was first applied to all 11 years without differentiating between the 

seasons. Thus, for each variable, we had 5,785,920 points per minute (i.e., 241,080 points for 

each hour of the daily cycle). Sample results, positive (increase) are presented in Figure 59. Each 

value is normalized with respect to the installed wind capacity (2,995 MW). The Hydro-Québec 

results are compared with those shown in the BPA report according to (BPA 2009), for a wind 

capacity of 3,155 MW.

Demand

(MW)

In % with 

respect to the 

mean BRD

Standard deviation of variability 1 minute 38.5 0.2%

Standard deviation of variability 1 hour 83.7 4%

Average real-time forecasting error 522 2,5%

Standard deviation of real-time 

forecasting errors 686 3,2%

Wind 

generati

on (MW)

In % with 

respect to 

mean wind 

generation 

Standard deviation of variability 1 minute 7.5 0.7%

Standard deviation of variability 1 hour 171 15.6%

Average real-time forecasting error 202 18.4%

Standard deviation of real-time 

forecasting errors 227 21%
IEA Wind Task 24 Final Report, Vol. 2

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Figure 58. Daily maximum and minimum of hourly penetration and ramping rates
IEA Wind Task 24 Final Report, Vol. 2

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Figure 59. Comparison of Hydro-Quebec and BPA supplemental reserves at 3,000-MW wind 

generation (up-regulation)

In a second step, we analyzed the same load and wind generation data per year, considering the 

different seasons of the year separately. The yearly values are obtained by applying the algorithm 

to the entire year (525,000 points for non-leap years) while the seasonal values are based on data 

for the months of the season. In each case, we first obtain the 24 values related with the daily 

cycle of the load, the wind, and the net demand. These values are then reduced to a single 

optimum value for the year, the winter, the summer, and the out-of-season periods using the 

weighting formulas for the maximum requirements of the net demand. The sample results in 

Figure 60 show inter-annual variations of the reserves requirements at 3,000-MW penetration.

0

2

4

6

8

10

0 2 4 6 8 10 12 14 16 18 20 22

% Rated Wind Generation

Perfect Load-Following UP 5

10

15

20

25

30

35

0 2 4 6 8 10 12 14 16 18 20 22

% Rated Wind Generation

Forecasted Load-Following UP

BPA(3155MW)

HQ(2995MW)

0

1

2

0 2 4 6 8 10 12 14 16 18 20 22

% Rated Wind Generation

Hour of the day

AGC UP

BPA(3155MW)

HQ(2995MW)

5

10

15

20

25

30

0 2 4 6 8 10 12 14 16 18 20 22

% Rated Wind Generation

Hour of the day

Imbalance UP
IEA Wind Task 24 Final Report, Vol. 2

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Figure 60. Annual values of supplemental AGC and imbalance based on the BPA approach
IEA Wind Task 24 Final Report, Vol. 2

149

Figure 61. Seasonal assessment of the n-sigma increase of frequency regulation reserves in 

percent of rated installed wind generation

For comparisons purposes, Figure 61 presents the results obtained by applying the n-sigma 

method of reserve assessment to the same data, with n=4 for AGC and n=2 for load following. 

Although very different from the BPA covariance allocation-based method, these results are 

broadly in line with recent publications. To simplify decision making, the expected value was 

estimated by averaging for the BPA and n-sigma approaches, the 10 annual values (see Figure 

59) and tabulated them in Table 48. The column “Mean” simply designates the average of the 

three values, “winter,” “summer,” and “out-of-season,” which themselves are the averages of the 

10 annual values.
IEA Wind Task 24 Final Report, Vol. 2

150

Table 48. Frequency regulation reserves requirements for 3,000-MW wind generation integration in 

the Quebec interconnection

(a) BPA weighted maximum allocation method (BPA 2008)

MW (Max Up or Down ) Mean Winter Summer Out of Season

AGC 54 49 65 48

Load Following

• Perfect schedule 191 170 229 174

• Forecasted schedule 663 513 890 768

% of wind capacity (Max Up or 

Down ) Mean Winter Summer Out of Season

AGC 1.8 1.6 2.2 1.6

Load Following

• Perfect schedule 6.4 5.7 7.6 5.8

• Forecast schedule 22.1 17.1 29.7 25.6

(b) ONRL-Holttinen n-sigma allocation method [4]

MW (Max Up or Down ) Mean Winter Summer Out of Season

AGC 13 10 17 11

Load Following

• Perfect schedule 17 11 23 15

• Forecasted schedule 203 153 285 170

% of wind capacity 

(max up or down ) Mean Winter Summer Out of Season

AGC 0.4 0.3 0.6 0.4

Load Following

• Perfect schedule 0.6 0.4 0.8 0.5

• Forecasted schedule 6.8 5.1 9.5 5.7

Conclusions

According to the BPA method, the supplementary AGC and load following reserves to 

accommodate 3,000 MW of installed wind capacity will amount to 1.8–22.1%, respectively, on a 

yearly average basis. However, there is a considerable disparity between the winter months and 

the other months of the year: thus, the additional load following requirements increase from 17% 

of the installed wind capacity in winter to 29.7% during the summer. These numbers can be 

compared favorably with the results obtained by BPA (28%) BPA 2008) and Manitoba Hydro 

(26%) (Molinski 2008), which are essentially hydroelectric utilities operating huge dams and 

having electricity import-export strategies that are not too different from our own. On the other 

hand, the n-sigma criterion resulted in much lower incremental reserves requirements with only 

0.4% and 6.8% increase of the AGC (n=4) and load following (n=2), respectively.

It is generally accepted that such impact evaluation based on a statistical approach is not as 

accurate as a method based on simulation, which is founded upon far more realistic system 

operation assumptions. l'Institut de recherche d'Hydro-Québec has been working on such a 

simulator since 2006, but the initial results on the 3,000-MW-integration will be published 

separately (de Montigny et al. 2010). The simulator is the proper tool for refining these paper’s 

estimations and, in particular, relating them to the terms of the agreement currently covering the 

frequency regulation service in Quebec interconnection. 
IEA Wind Task 24 Final Report, Vol. 2

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Calculation of Balancing Reserves Incorporating Wind Power into the HydroQuébec System over the Time Horizon of 1–48 Hours 

Nickie Menemenlis, Maurice Hunault, Jacques Bourret, and Andre Robitaille

Introduction

Balancing reserves ensure the short-term reliability of a power system over a time horizon of 

1 hour to 2–3 days ahead. They consist of available generating capacity that could be deployed 

when needed to offset discrepancies in supply caused by errors on current forecasts. 

Traditionally, these reserves have covered uncertainties on load forecasts and forced outages.

More recently, several studies in the literature have proposed increasing these reserve levels to 

counter the presence of uncertainties contributed by wind generation.

Methodology

One methodology to compute balancing reserves, integrating several sources of uncertainties, is 

power system reliability theory. It defines the LOLP as the probability that the available 

generation, including reserves, will not completely meet the demand. It is obtained from capacity 

outage probability tables considering the possible spread on the variables, including wind 

generation, through a set of discrete probable states. A specific LOLP target, or what we will call 

here a risk, is associated with reserves that are implicitly included in these tables.

The methodology adopted here borrows from reliability theory but has some novel features 

specific to the problem at hand. In its final formulation, the balancing reserve requirement is a 

function of the statistical characteristics of the forecast errors rather than the forecasts 

themselves. Hence, we developed distributions of forecast errors on all variables displaying 

uncertainties over the lead times from 1 to 48 hours. This was done by comparing past forecasts 

to the corresponding measurements. 

Replacing discrete states in the reliability computation by these distributions, more powerful 

probabilistic tools could be applied to the analysis. This facilitated both the aggregation of the 

individual forecast errors into a net forecast error and the graphical representation of results. The 

distribution of the net forecast error represents the reliability (i.e., the probability of satisfying 

the entire load). The anticipated risk, given as 1 minus the reliability, was then computed at each 

forecast lead time. It is the value of a function of the net forecast error distribution corresponding 

to a predetermined level of balancing reserves. Alternatively, given a target level of risk, the 

associated balancing reserve requirements can be quantified. Repeating this computation for each 

lead time over a given time horizon, it reveals the temporal evolution of risk or of balancing 

reserve requirements.

This methodology was used to evaluate additional balancing reserves required to integrate 

3,000 MW of wind power capacity into the Hydro-Québec system, corresponding to a

penetration level of wind power of approximately 10%. This was done by comparing the 

balancing reserves required to maintain the same level of risk before and after the integration of 

wind generation over numerous system conditions. Several results from this study are illustrated 

in the following figures and tables.
IEA Wind Task 24 Final Report, Vol. 2

152

Results

Figure 62 illustrates typical load, wind, and generation unavailability error distributions and the 

resulting net error distribution at a given instant. The curve Fd+u represents the distribution of the 

aggregation of conventional forecast errors, shown individually with dotted curves, while the 

curve Fd+u-w represents the distribution of the net forecast error including wind generation.

Values on the curves represent reliability. Including wind generation, without any balancing 

reserve ( BR MW = 0 on the x-axis), reliability is quite low ( ≈ 0.37). By adding balancing reserves, 

for example, BR MW = 500 , the reliability increases significantly ( ≈ 0.75), indicating that a risk of 

25% still persists. Balancing reserves of 1,250 MW result in very high reliability ( ≈ 0.98), and 

greater balancing reserves contribute little additional reliability. 

The risk in Figure 63 is obtained from the relation Risk = 1 - Reliability. 

It illustrates the risk, 0 R , corresponding to some nominal level of balancing reserves, nom BR , the 

additional risk incurred, ∆R , and the additional reserves ∆BRs required following the integration 

of two different wind generation capacities, with wind forecast error uncertainties modeled as 

zero-mean low and large variance Gaussian processes. With the given nominal balancing 

reserves of 500 nom BR MW = , the risk, 0 R , without wind generation is 17% (obtained by reading on

curve, d u R + ). Adding a certain amount of wind generation into the system, and keeping the same 

amount of balancing reserves, increases the system risk by an amount of ∆R . The full and dotted 

curves, duw R + − and d uW R + − , corresponding to small and large wind generation respectively. In order 

to maintain the same risk before and after the additions of wind generation, it is necessary to 

provide the system with additional balancing reserves of the amount of ∆BRs . For the case 

examined, the additional reserves are 50 MW and 140 MW respectively.

-1000 -500 0 500 1000 1500

0

0.2

0.4

0.6

0.8

1

B.R. [MW]

Distributions Fx(x)

Distributions : Fd

, Fu, Fw, Fd+u, Fd+u-w

F0

R0 = 1-F0

(B.R.)

Fd

F

u

F

w

Fd+u

Fd+u-w

Figure 62. Qualitative illustration of the various components used in the risk calculation
IEA Wind Task 24 Final Report, Vol. 2

153

300 400 500 600 700 800 900 1000

0

5

10

15

20

25

30

B.R. [MW]

Risk %

Risk : Rd+u, Rd+u-w

Rd+u

Rd+u-w

∆BR

∆R

BR

nom

Rd+u-W

Rd+u

Rd+u-w

∆BR

Figure 63. Qualitative illustration of the risk and additional balancing reserves for two different 

wind generation penetration levels

We note that at each instant the original risk without wind generation, 0 R , presented to the 

system depends on:

• the statistical characteristics of the uncertainties on the load forecast and the forecast of

unavailable power, and

• the nominal balancing reserves level, nom BR

In addition, looking at the time evolution of the variables, since the forecast uncertainties may 

vary over time, the hour during the day and the season, the following is true:

• The risk 0 R incurred with the nominal balancing reserves also varies over time. This is 

illustrated in Figure 63 over a horizon covering the next day using typical winter data from 

the Hydro-Québec system. The two curves in each pair represent risk with and without wind 

generation. The bump in the curves around 16:00 hours reflects the particular signature of

load forecast errors. 

• The balancing reserves BRs required to maintain a given risk level also varies over time. This 

is illustrated in Figure 60.

• The additional risk, ∆R , sustained by the system when integrating wind generation, and 

therefore the additional balancing reserves, ∆BRs , depend on the original risk, 0 R ,
IEA Wind Task 24 Final Report, Vol. 2

154

corresponding to the given level of reserves, nom BR , and on the statistical characteristics of the 

added wind generation forecast error. The two quantities ∆R and ∆BRs also vary over time. 

Figure 65 shows the risk encountered with and without wind generation, and the required 

∆BRs beyond the predetermined balancing reserves to maintain risk over a horizon covering 

the next day.

Figure 64. Risk encountered as a function of lead time for various levels of balancing reserves

Figure 65. Balancing reserves required as a function of lead time for various levels of risk

-4 6 16 26 36

2%

5%

10%

15%

20%

Risk

BRs vs lead time for day ahead horizon, January

Time of day ahead

BRs [MW]

-4 6 16 26 36

500 MW

700 MW

1000 MW

1200 MW

1500 MW

BRs

Risk vs lead time for day ahead horizon, January

Time of day ahead [h]

Risk [%]
IEA Wind Task 24 Final Report, Vol. 2

155

Figure 66. Risk with and without wind generation, and added balancing reserves to maintain the 

same risk

Using Hydro-Québec data, risk levels encountered in balancing reserves reach up to 5% more 

than the day-ahead horizon. This may seem unusually high, but contrary to the regulating 

reserves, acting in the intra-hour time horizon, utilities have the leisure to accept larger risk 

levels here because looking forward, they can still call on uncommitted yet available resources to 

remedy undesirable occurrences. Since the remedies are implemented at extra cost, the choice of 

risk level is essentially an economic consideration associated with the deployment of resources 

committed at the last minute.

We have used this measure of risk as a tool to evaluate the risk associated with the use of the 

present nominal balancing reserves and to compute the extra requirements in balancing reserves 

brought on by the presence of wind generation while maintaining the present risk.

Looking over the span of 1 year, maximum values over the day-ahead horizon were compiled for 

risk with and without wind generation, as well as additional risk encountered and additional 

balancing reserves required with the addition of 3,000 MW of wind capacity. These are 

summarized in Table 49. Risk [%]

with wind

without wind

-4 6 16 26 36 ∆ BRs [MW]

Time of day ahead [h]

Risk and ∆BR vs lead time, January
IEA Wind Task 24 Final Report, Vol. 2

156

Table 49. Results for every month over the day-ahead horizon

Maximum risk w/wo wind, ∆R and ∆BRs at maximum risk

Day ahead horizon - 3000 MW of wind generating capacity

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Rmax wo/w [%] 4,0 3,7 3,7 1,7 1,6 1,4 1,6 1,6 1,1 3,0 2,2 5,0

Rmax w/w [%] 4,5 4,1 4,1 2,0 1,8 2,0 1,9 2,1 1,4 3,8 2,7 5,5

∆R(Rmax) [%] 0,4 0,4 0,4 0,3 0,3 0,6 0,4 0,5 0,3 0,7 0,4 0,5

∆BR(Rmax) [MW] 38 40 41 49 47 82 61 67 68 57 52 34

Rmax wo/w: Maximum risk encountered over day-ahead horizon without wind

Rmax w/w: Maximum risk encountered over day-ahead horizon with wind

∆R(Rmax): Variation of risk occurring at maximum risk due to addition of wind power

∆BR(Rsémx): ∆PAs required at maximum risk due to addition of wind power

The last column of the table shows that the maximum risk in winter, when nominal balancing 

reserves are set to 1,500 MW, is 5% in December. If balancing reserves are not adjusted, this risk 

climbs to 5.5%. However, the increase in balancing reserves required to maintain a risk of 5% is 

only 34 MW. In summer, the nominal balancing reserves are set to 1,200 MW, and the system 

load and its associated uncertainties are lower. The contribution of the wind power uncertainties 

to the total distribution is therefore augmented. In October, the maximum risk encountered on the 

day-ahead horizon is 3% without wind power and 3.8% with wind power. The increase in 

balancing reserves required to maintain a risk of 3% is only 57 MW. We note also that in other 

months, to maintain much lower levels of risk, higher DBRs are required. However, if the

December risk level of 5% is deemed acceptable all year long, then the balancing reserves levels 

need not be increased beyond the 34 MW required in December.

Conclusions

In summary with this study, we quantified balancing reserve requirements, with and without 

wind generation, based on a risk criterion. With the same procedure we have also determined the 

added reserve requirements to maintain a specified level of risk before and after the integration 

of 3,000 MW of wind power capacity.

The conclusion to be drawn is that with current Hydro-Québec balancing reserves being 

relatively high and risk levels relatively low, little additional balancing reserves are required to 

integrate 3,000 MW of wind power capacity. The 5% maximum risk level revealed in our 

simulations was not predetermined, but rather was revealed by the present study. It seems to be 

acceptable, since current practice in operations planning seems satisfactory.
IEA Wind Task 24 Final Report, Vol. 2

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Wind Power Capacity Credit in Quebec 

L. Bernier and A. Forcione

Introduction

Climate and load

As is the case for a few northern countries and Canadian provinces, the Quebec annual peak 

electricity demand occurs during the winter, and is well correlated to actual air temperature and 

wind at major load centers

Climate and Wind Generation

. The peak usually happens during cold spells where minimum 

temperature reaches around -30o

C or less during two or more days in a row.

The winter season is also generally favorable to a good wind power production, on average. 

However, in order to protect the turbines against structural damage, the wind production is halted 

when actual temperatures at the turbine site

Scenario

reach a limit set by design. The limit is chosen by 

manufacturers mainly by comparing the value of expected lost energy over the life of the turbine 

with the cost of lowering this limit. Based on the climates in which wind capacity is actually 

deployed, today’s turbines are usually available either with a standard operational limit of -20o

C, 

or with a “cold package” limit of -30o

C. With the Quebec climate, turbines in the control zone 

are of the latter type, but might still face periods of low temperature induced forced stoppages. 

However, due to the geographic dispersion of wind power plants and their varying distances 

from load centers, these stoppages are not necessarily or systematically coincident with system 

peak load events.

In such a context, an appropriate evaluation of capacity contribution is crucial to ensure system 

security and reliability at minimum capacity supply cost. Accordingly, the capacity contribution 

of wind power in the Quebec control zone as been studied in detail for a “3000MW in 2016” 

wind scenario12. A few articles give in depth description of the study 00 for which a brief 

summary is provided here. 

Methodology

A custom-made Monte-Carlo simulation model was used. The model relied on wind and load 

data series that were matched on an hourly time-step, over a 36 years period using real weather 

data combined with seven different weekday pattern. The model takes into account forecasting 

errors and conventional generation outages through Monte-Carlo simulator. 

 12 At the time of the study, the “3000MW in 2016” scenario corresponded to the Hydro-Quebec Distribution firm 

engagements, with known wind plants locations and some available wind data.
IEA Wind Task 24 Final Report, Vol. 2

158

Results

The capacity contribution results from the comparison of two simulations leading to the same 

reliability target with the loss of load expectation (expectation of not having enough resources to 

meet the demand) equaling to one day per ten years: 

• A first simulation includes the 3000 MW wind power scenario

• In the second simulation, the wind power is replaced by conventional generation resources 

having a 0% outage rate. 

The amount of conventional generation added in the second scenario is then use as a benchmark 

for the capacity contribution of wind power. 

Obviously, such simulations rely on availability and realism of data over the full 36 years period. 

Accordingly, hourly load data was provided by highly reliable demand models and based on 

historical hourly weather time series. However, in absence of real historical wind generation and 

with the complex spatial and temporal correlations between weather, wind generation and load 

plus meteorologically triggered stoppages, care had to be given to the evaluation of the 

underlying long term wind power time series. These were obtained using historical 

meteorological data available from weather stations that were extrapolated at the power plants 

sites using a physics based diagnostic model. 

Thus, additional evaluations were performed in order to evaluate the sensitivity of wind power 

capacity contribution estimations to wind power data. Such evaluations indicated that the results 

were sensitive to wind power hourly data during a limited number of very cold events occurring 

along the 36 years period. That was not surprising, due to the correlation between extreme cold 

events and high risk periods of not having enough resources to meet the demand. 

Following the results of sensitivity analysis, the time series were then supplemented by in depth 

analysis of fourteen critical extreme cold weather events, using high resolution numeric weather 

“hindcasting” models and weather reanalysis data. 

After the inclusion of this new dataset, the capacity contribution of 3000 MW of wind power was 

found to be equivalent to 900 MW of conventional generation.

Limitations

Results were found to be very sensitive to wind data during a limited number of extreme cold 

events over the 36 years period. That finding also suggests that such evaluations are improved by 

long time series and by better on site weather data covering critical historical events. 

Conclusions

The capacity credit was established at 30% of total wind nameplate capacity, which amounts, for 

the studied 3000 MW scenario, to an equivalent of about 900 MW of firm conventional 

generation capacity.