Power System Resource Adequacy for Clean Energy E-Book

Renchang Dai

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Serie: IEEE Press Collection on Offshore Wind Energy
• Sprache: Englisch

Seitenzahl: 195

Veröffentlichungsjahr: 2025

Beschreibung

Comprehensive overview of how to ensure adequate power resources in a decarbonized world powered by renewable energy

Power System Resource Adequacy for Clean Energy explores and addresses the challenges and solutions associated with ensuring adequate power resources as power grids transition toward a decarbonized and renewable energy future, discussing assumptions, methodologies, modeling frameworks, detailed inputs, and result analysis. The book illustrates the methodology, approaches, and nuances of resource adequacy studies to determine seasonal planning reserve margins as well as resource peak capacity contributions to meet peak demand. The saturation effects of renewable resources and energy-limited resources are highlighted, and importance of resource adequacy verification is emphasized.

Written by an expert with a wealth of real-world experience in the field, Power System Resource Adequacy for Clean Energy includes information on:

• The impact of climate change, seasonal planning reserve margins, multi-metric criteria for resource adequacy
• Wind and solar generation profiles, energy storage and thermal generation modeling, and the flexibility of hydroelectric generation
• Operating and balancing reserve, effective load carrying capability, ­saturation effects, and marginal and average ELCC
• Resource adequacy verification, a critical concept in ensuring that the peak demands are truly met by built resources

Power System Resource Adequacy for Clean Energy is an excellent reference on the subject for power system planners, federal and state energy policy makers and commissioners, and professors, researchers, and graduate students in Electrical & Computer Engineering.

Leseprobe

Table of Contents

Cover

Table of Contents

Title Page

Copyright

About the Author

About the Editor

Preface

1 Introduction

1.1 Demand Forecast

1.2 Reliability Metrics

1.3 Resource Adequacy Evaluation Method

1.4 Regional and Local Resource Adequacy Coordination

1.5 Resource Adequacy Verification

References

2 Resource Adequacy Study Overview

2.1 Resource Adequacy Metrics

2.2 Regulatory Policy and Impact

References

3 Load Forecast

3.1 Methodology

3.2 Input Assumptions

3.3 Climate Change

3.4 Demand‐side Resources Impacts

3.5 Stochastic Scenarios

References

4 Planning Reserve Margin

4.1 Operating Reserves

4.2 Balancing Reserve

4.3 Reliability Target

4.4 Seasonal PRM

References

5 Resource Adequacy Modeling

5.1 Wind Power Generation

5.2 Solar Photovoltaic Power Generation

5.3 Energy Storage

5.4 Hydroelectric Generation

5.5 Thermal Generation

References

6 Resource Adequacy Methodology

6.1 Reliability Metric Evaluation

6.2 PRM Calculation

6.3 Effective Load Carrying Capability

References

7 Regional and Local Resource Adequacy Coordination

7.1 Regional Resource Adequacy

7.2 Regional Resource Sharing Program

7.3 Market Purchases

References

8 Resource Adequacy Verification

8.1 Long‐Term Capacity Expansion Model

8.2 Wind and Solar Resource Grouping

8.3 Resource Adequacy Verification

8.4 Resource Adequacy Adjustment

8.5 Numerical Case Study

References

9 Conclusions

Index

End User License Agreement

List of Tables

Chapter 2

Table 2.1 Resource Adequacy Study Inputs.

Table 2.2 Resource Adequacy Study Outputs.

Table 2.3 Resource Adequacy Metric Threshold.

Chapter 3

Table 3.1 Global Mean Surface Temperature Change.

Chapter 5

Table 5.1 Wind Power Model Parameters.

Table 5.2 Drivetrain Model Parameters.

Table 5.3 Pitch Control Model Parameters.

Table 5.4 Normalized Power Curves.

Table 5.5 Battery Energy Storage Parameters.

Table 5.6 Pumped Hydro Storage Parameters.

Table 5.7 Reservoir Elevation Limits.

Chapter 6

Table 6.1 Resource Adequacy Metric Thresholds.

Table 6.2 Net Load and Solar Generation.

Table 6.3 Net Load and Storage Generation.

Table 6.4 Storage Dispatch of Hybrid System.

Table 6.5 Generation or Charging Load of Hybrid System.

Chapter 7

Table 7.1 Winter Peak Load and Corresponding Capacity Saving.

Table 7.2 Summer Peak Load and Corresponding Capacity Saving.

Chapter 8

Table 8.1 Resource Cost Category.

Table 8.2 Generic Resource Technology.

Table 8.3 Generic Resource Capital Cost ($/kW).

Table 8.4 Generic Resource Cost and Operation Characteristics.

Table 8.5 Resource ELCC at the first Tranche Nameplate Capacity in ...

Table 8.6 Wind and Solar ELCC Saturation.

Table 8.7 Energy Storage ELCC Saturation.

Table 8.8 Resource Adequacy Study Result – First Iteration.

Table 8.9 Long‐term Capacity Expansion Result – First Iteration.

Table 8.10 Resource Adequacy Verification – First Iteration.

Table 8.11 Resource ELCC at the First Tranche Nameplate Capacity i...

Table 8.12 Long‐term Capacity Expansion Result – RA Verification....

Table 8.13 Long‐term Capacity Expansion Result – Second Iteration....

Table 8.14 Resource Adequacy and Long‐term Capacity Expansion Iter...

List of Illustrations

Chapter 1

Figure 1.1 Reliability Analysis Hierarchical Levels.

Chapter 2

Figure 2.1 Example of System Hourly Available Capacity for a Simul...

Figure 2.2 Example of System Hourly Load for a Simulation Year.

Figure 2.3 System Hourly Surplus/Deficit for the Simulation Year....

Figure 2.4 Example of Reliability Metric Convergence Over Simulati...

Figure 2.5 Resource Adequacy Evaluation Flow Chart.

Figure 2.6 Load Curtailment.

Figure 2.7 Highest Curtailment Curve.

Figure 2.8 ELCC Calculation by Load Approach.

Figure 2.9 ELCC Calculation by Generation Approach.

Figure 2.10 Distribution of Loss of Load in MW.

Figure 2.11 Distribution of Unserved Energy in MWh.

Figure 2.12 Optimal Metric Threshold.

Chapter 3

Figure 3.1 Illustration of Feedforward Network.

Figure 3.2 Neuron Operation.

Figure 3.3 Architecture of RNN for Load Forecast.

Figure 3.4 Annual Maximum Temperature.

Figure 3.5 Annual and One‐in‐two Peak Temperature.

Chapter 4

Figure 4.1 Illustration of Planning Reserve Margin.

Figure 4.2 Contingency Reserve Based on 3% of Load and Generation ...

Figure 4.3 Contingency Reserve Based on 3% of Load and Generation ...

Figure 4.4 Net Load 5‐second Variations.

Figure 4.5 Regulation Up and Regulation Down.

Figure 4.6 Histogram of Regulation Samples in the Given Hour.

Figure 4.7 Net Load Variation.

Figure 4.8 Monthly Peak Load.

Figure 4.9 Example of Monthly Average Wind Power.

Figure 4.10 Example of Monthly Average Solar Power.

Figure 4.11 Hourly Peak Load in August.

Figure 4.12 Hourly Peak Load in December.

Figure 4.13 Example of Hourly Average Wind Power in August.

Figure 4.14 Example of Hourly Average Wind Power in December.

Figure 4.15 Example of Hourly Average Solar Power in August.

Figure 4.16 Example of Hourly Average Solar Power in December.

Figure 4.17 Load Curtailment (MW) by Hour and by Month.

Figure 4.18 Winter Planning Reserve Margin.

Figure 4.19 Summer Planning Reserve Margin.

Chapter 5

Figure 5.1 Wind Turbine Model.

Figure 5.2 Ideal Disk.

Figure 5.3 Air Mass.

Figure 5.4 Wind Turbine Performance Coefficient Curves.

Figure 5.5 Drive Train System.

Figure 5.6 Two‐mass Rotor System.

Figure 5.7 Two‐mass Rotor Model Block Diagram.

Figure 5.8 Pitch Control Block Diagram.

Figure 5.9 Wind Turbine Simulink Model.

Figure 5.10 Wind Power Simulink Model.

Figure 5.11 Drivetrain Simulink Model.

Figure 5.12 Pitch Control Simulink Model.

Figure 5.13 Simulated Normalized Power Curve.

Figure 5.14 Generic Normalized Power Curve.

Figure 5.15 Wind Speed Temporal Variability at a Single Coordinat...

Figure 5.16 Wind Speed Spatial Variability at a Single Time Point...

Figure 5.17 Wind Speed Weibull Distribution.

Figure 5.18 Winter Daytime Wind Speed Distribution.

Figure 5.19 Winter Nighttime Wind Speed Distribution.

Figure 5.20 Summer Daytime Wind Speed Distribution.

Figure 5.21 Summer Nighttime Wind Speed Distribution.

Figure 5.22 Photovoltaic Cell Simplified Equivalent Circuit.

Figure 5.23 Simplified Photovoltaic Cell Current–Voltage Curve.

Figure 5.24 Photovoltaic Cell Practical Equivalent Circuit.

Figure 5.25 Photovoltaic Cell Current–Voltage Curve.

Figure 5.26 Photovoltaic I–V Curve under Various Cell Temperature...

Figure 5.27 Photovoltaic Power–Current Curve under Various Cell T...

Figure 5.28 Photovoltaic Power–Voltage Curve under Various Cell T...

Figure 5.29 Solar Radiation.

Figure 5.30 Ambient Temperature.

Figure 5.31 Electrical Circuit of Lithium‐ion Battery.

Figure 5.32 Conceptual Representation of a Basic CAES System.

Figure 5.33 Thermodynamic System.

Figure 5.34 Compression Process.

Figure 5.35 Expansion Process.

Figure 5.36 River Discharge.

Figure 5.37 River Discharge and Hydro Power Generation.

Figure 5.38 Reservoir Elevation Limits.

Figure 5.39 Reservoir Storage.

Figure 5.40 Water Flow and Hydro Power Generation.

Figure 5.41 Reservoir Elevation.

Chapter 6

Figure 6.1 Reliability Metric Evaluation Flowchart.

Figure 6.2 Net Load and Solar Generation.

Figure 6.3 Net Load with Accumulative Solar Installation.

Figure 6.4 Solar Photovoltaic Effective Load Carrying Capability S...

Figure 6.5 Net Load and Storage Dispatch.

Figure 6.6 Net Load with Incremental Storage Installation.

Figure 6.7 Storage System ELCC Saturation.

Figure 6.8 Net Load with Incremental Hybrid System Installation.

Figure 6.9 Hybrid System ELCC Saturation.

Figure 6.10 Effective Load Carrying Capability With and Without ...

Chapter 7

Figure 7.1 Utility and Region Winter Peak Load.

Chapter 8

Figure 8.1 Resource Adequacy Verification Flow Chart.

Figure 8.2 Wind and Solar Marginal ELCC Saturation.

Figure 8.3 Wind and Solar Average ELCC Saturation.

Figure 8.4 Energy Storage Marginal ELCC Saturation.

Figure 8.5 Energy Storage Average ELCC Saturation.

Figure 8.6 Energy Storage Levelized Peak Capacity Cost.

Figure 8.7 Non‐energy Storage Resources Levelized Peak Capacity Co...

Figure 8.8 Wind and Solar Average ELCC Saturation.

Figure 8.9 Energy Storage Average ELCC Saturation.

Guide

Cover

Table of Contents

Title Page

Copyright

About the Author

Preface

Begin Reading

Index

End User License Agreement

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Power System Resource Adequacy for Clean Energy

Renchang DaiPuget Sound EnergyBellevue, United States

IEEE Press Offshore Wind Energy CollectionPeng Zhang, Collection Editor

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Library of Congress Cataloging‐in‐Publication Data Applied for:

Hardback ISBN: 9781394318193

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IEEE Press Offshore Wind Energy Collection

Series Editor: Peng Zhang, Professor, Electrical and Computer Engineering, Stony Brook University

Offshore wind systems represent one of the most complex frontiers of modern power engineering, combining large‐scale inverter‐based resources (IBRs), high‐voltage AC/DC transmission, grid‐forming control architectures, and advanced cyber‐physical resilience. The IEEE Press Offshore Wind Energy Collection brings together concise, technically rigorous volumes addressing these challenges across weak‐grid integration, electromagnetic transients, HVDC/HVAC transmission, grid‐forming control, dynamics and stability, reliability, cybersecurity, market operation, and energy management. This series is uniquely designed to support workforce development and professional training. Each book serves as a practical learning module for engineers, graduate students, and industry professionals seeking to strengthen their expertise in IBR‐rich power grid engineering. Developed in collaboration with leading experts from academia, utilities, and manufacturers, the collection bridges theory, simulation, and industrial practice, offering compact yet comprehensive treatments of critical modern power grid technologies. It reflects IEEE Press's enduring commitment to advancing next‐generation power and energy systems through impactful, technically grounded publications.

About the Author

Renchang Dai, PhD, is a principal engineer and project manager at Puget Sound Energy, focusing on integrated system planning and resource adequacy studies. He received his PhD degree in electrical engineering from Tsinghua University, China, in 2001. Dr. Dai has worked on a variety of power system problems, including power system planning, operations, and control. He was the principal engineer and group manager for Global Energy Interconnection Research Institute North America, where he led a team of engineers in researching and developing graph database and graph computing technologies for power system planning and operations.

Dr. Dai was a team leader for GE Energy. At GE Energy, he designed, developed, and implemented energy management system. He was also a founding member of the GE Energy Consulting Smart Grid Center of Excellence, where he consulted on smart grid deployment and renewable energy grid integration projects. In 2005, while he was a lead scientist in GE Global Research, he received the GE Global Technical Award for his contributions to the development of wind turbine generator fault ride through technology.

Dr. Dai is a senior member of the IEEE. He has worked intensively on power system analysis and has published over 100 papers in international journals and conferences.

About the Editor

Peng Zhang, Ph.D., is Professor of Electrical and Computer Engineering and SUNY Empire Innovation Professor at Stony Brook University, New York, where he directs the Stony Brook Power Engineering Laboratory. He is an affiliated Professor of Computer Science and Applied Mathematics and Statistics at Stony Brook University. Dr. Zhang's research focuses on quantum‐engineered power grids, AI‐enabled grid operations, cybersecurity, formal methods, power system stability and control, and electromagnetic transients. He has pioneered quantum computing, quantum security, quantum networking, and quantum AI algorithms that have been successfully implemented on noisy intermediate‐scale quantum (NISQ) platforms to solve challenging power‐system problems. He has authored or co‐authored over 200 refereed publications and four books, including Microgrids: Theory and Practice (Wiley‐IEEE Press, 2024) and Networked Microgrids (Cambridge University Press, 2021). His upcoming volume, Quantum Grids (Cambridge University Press, 2026), introduces the foundations of quantum‐enabled energy systems. Dr. Zhang has been a long‐serving editor for the IEEE Transactions on Power Systems, IEEE Transactions on Sustainable Energy, IEEE Power & Energy Letters, and IEEE Journal of Oceanic Engineering. His honors include the IEEE Region 1 Technological Innovation Award, multiple Best Paper Awards from IEEE PES and CIGRÉ, and several Dean's Awards for Excellence in Research and Innovation.

Preface

The global transition to a decarbonized and renewable energy future necessitates a new approach for power system resource adequacy study. Ensuring a reliable electricity supply to meet customer demand is paramount, and this book addresses the evolving challenges and solutions in resource adequacy planning within this dynamic landscape. It is intended for a broad audience, including power system planners, policymakers, regulators, researchers, and graduate students.

This book provides a comprehensive overview of resource adequacy studies, encompassing key assumptions, methodologies, modeling frameworks, detailed inputs, and result analysis. It covers the entire spectrum of the resource adequacy process, from demand forecasting and capacity requirements to effective load‐carrying capability and resource adequacy verification.

Climate change and clean energy policies are fundamentally reshaping resource adequacy. Traditional studies, focused on dispatchable and firm generation like thermal and hydroelectric plants, are now insufficient. The increasing reliance on undispatchable renewables and energy‐limited storage to meet peak demand, coupled with climate‐driven shifts in load patterns and hydro generation, requires a new approach.

This book introduces the concept of seasonal resource adequacy evaluation, highlighting its critical importance. It details the methodologies and approaches necessary to determine seasonal planning reserve margins, assess resource peak capacity contributions, and understand their saturation effects in meeting seasonal peak demands. Furthermore, the book discusses the complexities of regional and local resource adequacy coordination. Finally, recognizing the importance of implementation, it explores resource adequacy verification, a crucial process to ensure that planned capacity additions effectively meet seasonal peak demand.

Achieving power system adequacy in a clean energy future demands a holistic and adaptable strategy. Policymakers, system planners, and researchers must collaborate to refine adequacy metrics, invest in flexible resources, and develop robust planning models capable of navigating the evolving energy landscape. As the power grid decarbonizes, resource adequacy methodologies must advance to maintain both reliability and affordability. This book aims to contribute to that evolution, ultimately supporting the development of a resilient, cost‐effective, and sustainable power system that facilitates the global transition to clean energy.

Renchang Dai

1Introduction

Power system is the largest manmade infrastructure in human history, playing a critical role in modern society. Electricity is indispensable for industrial, commercial, and residential activities. It is crucial to supply power to customers to satisfy their needs reliably. Keeping the lights on is essentially a top priority in power system planning and operation. However, achieving absolute reliability for power supply is impractical and prohibitively expensive. Thus, utilities and independent system operators (ISOs) undertake long‐term resource planning to balance cost and risk. This process aims to build adequate resources to meet established system reliability standards. The most commonly used metrics to measure power system reliability are loss‐of‐load probability (LOLP), loss‐of‐load expectation (LOLE), and expected unserved energy (EUE).

Traditionally, resource adequacy studies have focused on planning dispatchable and firm generators, such as thermal units, hydroelectric plants, and nuclear reactors, to reliably meet peak load demands. The maintenance schedules of these generation systems are typically planned during off‐peak periods to minimize disruption. Historically, uncertainties were mainly driven by load variability on the demand side and generation unit forced outage rate (FOR) on the supply side. Consequently, to meet the peak load needs, the peak hours are the focus and are carefully examined traditionally. As the resource adequacy evaluation results, the planning reserve margin (PRM) and necessary resource additions are calculated based on the availability of these dispatchable and firm generators during peak hours. Given the relatively stable load patterns and predictable generator characteristics of the past, annual resource adequacy studies were sufficient and effective.

However, today's electrical power systems are facing rapid and significant transformations due to climate, policy, demand, and resource changes. These challenges, coupled with the need for decarbonization and electrification, introduce new challenges and risks to power system resource adequacy study.

On the generation side, driven by global clean energy goals, wind turbine and solar photovoltaic panel installation are growing dramatically. These renewable resources, while crucial for reducing carbon emissions, are intermittent and non‐dispatchable, meaning their availability does not always coincide with peak load periods. This necessitates a reevaluation of how renewable resources are accredited for capacity contribution. Moreover, the effects of global warming are becoming increasingly evident, with higher summer temperatures and more frequent extreme heat events. The high ambient temperature depresses thermal unit capability when it is most needed on hot days. Additionally, warmer winters result in earlier snowmelt, leading to less precipitation and reduced hydroelectric power generation during the summer months.

On the demand side, climate change adds greater uncertainties. High variabilities are observed on loads under extreme weather conditions in both winter and summer. The global warming trend is shifting peak loads from winter to summer for traditional winter peaking utilities. The rapid proliferation of behind‐the‐meter photovoltaic panel installation is also changing the net demand profile, particularly in summer afternoons, which used to be typically peak load hours for summer peaking utilities. Critical hours for capacity needs are shifting from early afternoons to late afternoons or even evenings, making the traditional peak hours no longer critical from the perspective of capacity needs.

Climate change is causing changes in load patterns and hydro generation shapes in different seasons, while clean energy goals are reshaping the generation mix and availabilities. The shifts on both the supply and demand sides challenge the effectiveness of the current annual resource adequacy approach. As power systems worldwide transition to a decarbonized energy future and confront the impacts of climate change, there is an urgent need to develop and implement seasonal resource adequacy evaluations to ensure an adequate supply of electricity to meet customer demand reliably for all seasons.