Hong Chen, PhD, is a Senior Consultant at PJM Interconnection in Pennsylvania, USA, and was a Principal Analyst in electricity market design and development at ISO New England, USA. Dr. Chen received her PhD in electrical and computer engineering, ME in power systems, and BE in power systems.  She has more than 20 years of experience within the power industry and has been active author, editor and committee leader in IEEE activities. Dr. Chen is the current chair of Power System Operation, Planning and Economics committee in the Power and Energy Society of IEEE.

• FOREWORD ix PREFACE xiACKNOWLEDGMENT xiiiCONTRIBUTORS xvPART I INTEGRATED SYSTEM AND MARKET OPERATIONCHAPTER 1 BALANCE ECONOMIC EFFICIENCY AND OPERATION RISK MITIGATION 3Hong Chen and Jianwei Liu1.1 Power System Operation Risk Mitigation: The Physics 41.2 Integrated System and Market Operation: The Basics 111.3 Economic Efficiency Evaluation and Improvement: The Economics 201.4 Final Remarks 35Appendix 1.A Nomenclature 36Appendix 1.B Electricity Market Model 37References 39Disclaimer 41CHAPTER 2 MITIGATE MARKET POWER TO IMPROVE MARKET EFFICIENCY 4Ross Baldick2.1 Introduction 432.2 Price Formation in Electricity Markets 502.3 Price and Offer Caps 522.4 Ability and Incentive to Exercise Market Power 532.5 Market Power Mitigation Approaches 572.6 Conclusion 65Acknowledgments 65References 65PART II UNDER SMART GRID ERACHAPTER 3 MASS MARKET DEMAND RESPONSE MANAGEMENT FOR THE SMART GRID 69Alex D. Papalexopoulos3.1 Overview 693.2 Introduction 723.3 Distributed Computing-Based Demand Response Management Approach 743.4 The ColorPower Architecture and Control Algorithms 753.5 Integration with the Wholesale Energy Market 803.6 Equalizing Market Power Between Supply and Demand 833.7 Generalization Beyond Demand Response 843.8 A Numerical Example 873.9 Concluding Remarks 88Appendix 3.A Nomenclature 89References 89CHAPTER 4 IMPROVE SYSTEM PERFORMANCE WITH LARGE-SCALE VARIABLE GENERATION ADDITION 91Yuri V. Makarov, Pavel V. Etingov, and Pengwei Du4.1 Review of Regulation and Ancillary Services 924.2 Day-Ahead Regulation Forecast at CAISO 934.3 Ramping and Uncertainties Evaluation at CAISO 994.4 Quantifying the Regulation Service Requirements at ERCOT 1034.5 Conclusions 111Appendix 4.A Nomenclature 112References 113PART III STOCHASTIC APPLICATIONSCHAPTER 5 SECURITY-CONSTRAINED UNIT COMMITMENT WITH UNCERTAINTIES 117Lei Wu and Mohammad Shahidehpour5.1 Introduction 1185.2 SCUC 1195.3 Uncertainties in Emerging Power Systems 1255.4 Managing the Resource Uncertainty in SCUC 1345.5 Illustrative Results 1555.6 Conclusions 163Appendix 5.A Nomenclature 164Acknowledgments 166References 166CHAPTER 6 DAY-AHEAD SCHEDULING: RESERVE DETERMINATION AND VALUATION 16Ruiwei Jiang, Antonio J. Conejo, and Jianhui Wang6.1 The Need of Reserves for Power System Operation 1696.2 Reserve Determination via Stochastic Programming 1706.3 Reserve Determination via Adaptive Robust Optimization 1796.4 Stochastic Programming vs. Adaptive Robust Optimization 1826.5 Reserve Valuation 1856.6 Summary, Concluding Remarks, and Research Needs 191Appendix 6.A Nomenclature 192References 193PART IV HARNESS TRANSMISSION FLEXIBILITYCHAPTER 7 IMPROVED MARKET EFFICIENCY VIA TRANSMISSION SWITCHING AND OUTAGE EVALUATION IN SYSTEM OPERATIONS 197Kwok W. Cheung and Jun Wu7.1 Background 1977.2 Basic Dispatch Model for Market Clearing 1987.3 Economic Evaluation of Transmission Outage 2017.4 Optimal Transmission Switching 2037.5 Selection of Candidate Transmission Lines for Switching and Implementation of OTS 2067.6 Test Cases 2107.7 Final Remarks 216Appendix 7.A Nomenclature 216References 217CHAPTER 8 TOWARD VALUING FLEXIBILITY IN TRANSMISSION PLANNING 219Chin Yen Tee and Marija D. Ilíc8.1 Introduction 2198.2 Scale Economies of Transmission Technologies 2218.3 Disconnect of Current Power System Operational, Planning, and Market Mechanisms 2258.4 Impact of Operational and Market Practices on Investment Planning 2258.5 Information and Risk Sharing in the Face of Uncertainties 2308.6 Challenges in Designing Financial Rights for Flexibility 2348.7 Conclusions 235Appendix 8.A Nomenclature 236Appendix 8.B Mathematical Models Used for Case Studies 238Appendix 8.C Investment Cost 247References 248INDEX 251

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