Nojavan / Zare Electricity Markets

New Players and Pricing Uncertainties
1. Auflage 2020
ISBN: 978-3-030-36979-8
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 274 Seiten

This book analyzes new electricity pricing models that consider uncertainties in the power market due to the changing behavior of market players and the implementation of renewable distributed generation and responsive loads. In-depth chapters examine the different types of market players including the generation, transmission, and distribution companies, virtual power plants, demand response aggregators, and energy hubs and microgrids. Expert authors propose optimal operational models for short-term performance and scheduling and present readers with solutions for pricing challenges in uncertain environments. This book is useful for engineers, researchers and students involved in integrating demand response programs into smart grids and for electricity market operation and planning.Proposes optimal operation models;Discusses the various players in today's electricity markets;Describes the effects of demand response programs in smart grids.

Dr. Sayyad Nojavan is an Assistant Professor with the Department of Electrical Engineering at University of Bonab in Bonab, Iran.
Dr. Kazem Zare is an Associate Professor with the Department of Electrical and Computer Engineering at University of Tabriz, in Iran.

Autoren/Hrsg.

Nojavan, Sayyad

Zare, Kazem

Inhaltsverzeichnis

1;Preface;5
2;Contents;6
3;About the Editors;8
4;1 Energy Harvesting Technologies and Market Opportunities;9
4.1;Nomenclature;9
4.2;1.1 Introduction;10
4.3;1.2 Energy Harvesting Technologies and Challenges;10
4.4;1.3 Energy Harvesting Markets and Key Players;11
4.5;1.4 Intelligent Mechanisms for Energy Harvesting;14
4.6;1.5 Conclusions and Suggested Readings;21
4.7; References;23
5;2 Electricity Market Pricing: Uniform Pricing vs. Pay-as-BidPricing;27
5.1;2.1 Introduction;27
5.2;2.2 Contributions;29
5.3;2.3 Electricity Market Pricing;29
5.3.1;2.3.1 Vickrey–Clarke–Groves (VCG);29
5.3.2;2.3.2 Uniform Price Auction;30
5.3.2.1;2.3.2.1 The Advantages with the UPA;31
5.3.2.2;2.3.2.2 The Disadvantages with the UPA;32
5.3.3;2.3.3 Pay-as-Bid Auction;32
5.3.3.1;2.3.3.1 The Advantages with PABA;35
5.3.3.2;2.3.3.2 The Disadvantages with PABA;35
5.4;2.4 Switching from UPA to PABA?;36
5.5;2.5 Conclusion;40
5.6;References;41
6;3 Integrated Gas and Power Networks;44
6.1;Nomenclature;44
6.2;Sets;44
6.3;Parameters;44
6.4;Variables;45
6.5;3.1 Introduction;47
6.6;3.2 Expansion Co-planning of Electricity and Gas Networks;49
6.7;3.3 Operational Co-planning of Electricity and Gas Networks;59
6.8;3.4 Conclusion;65
6.9;References;66
7;4 Transmission Pricing: Right Insights for Suitable Cost Allocation Methods;68
7.1;4.1 Introduction;68
7.2;4.2 Transmission Pricing in Modern Electric Power Systems;69
7.2.1;4.2.1 Energy Transition Ongoing;70
7.2.1.1;4.2.1.1 Major Changes and Repercussions;70
7.2.1.2;4.2.1.2 How the Energy Transition Impacts on the TCA Paradigm;72
7.2.1.3;4.2.1.3 Integration of Devices and Systems;73
7.2.1.4;4.2.1.4 How the Power Sector Integration Trend May Be Captured by TCA Methods;75
7.2.1.5;4.2.1.5 Outlook over Selected Transmission Systems;76
7.2.2;4.2.2 Transmission Fundaments;77
7.2.2.1;4.2.2.1 Economics and Regulation;77
7.2.2.2;4.2.2.2 Principles to Allocate the Costs;78
7.2.2.3;4.2.2.3 Requirements to Develop Algorithms;80
7.2.2.4;4.2.2.4 The Ideal TCA Method;81
7.3;4.3 Transmission Cost Allocation Methods: Review and Analysis;81
7.3.1;4.3.1 Relevant Publications;83
7.3.1.1;4.3.1.1 Power Flow Based;83
7.3.1.2;4.3.1.2 Incremental Cost;84
7.3.1.3;4.3.1.3 Marginal Cost;84
7.3.1.4;4.3.1.4 Alternative Strategies;84
7.3.1.5;4.3.1.5 Newfound Approaches;86
7.3.2;4.3.2 Literature: Broad Findings;86
7.3.3;4.3.3 Publications with the Most Suitable Features;87
7.4;4.4 Conclusions and Future Directions;90
7.5;References;91
8;5 Quantifying the Effect of Autonomous Demand Response Program on Self-Scheduling of Multi-carrier Residential Energy Hub;98
8.1;Nomenclature;98
8.2;Sets and Indices;98
8.3;Parameters;98
8.4;Variables;100
8.5;Functions;100
8.6;5.1 Introduction;100
8.7;5.2 Energy Hub;103
8.8;5.3 Problem Formulation;104
8.8.1;5.3.1 Component Modelling;106
8.8.1.1;5.3.1.1 Energy Storage;106
8.8.1.2;5.3.1.2 CHP Unit;107
8.8.1.3;5.3.1.3 Solar Panel;109
8.8.1.4;5.3.1.4 Load Modelling;109
8.8.1.5;5.3.1.5 Uncertainty Modeling;111
8.8.1.6;5.3.1.6 Heat and Power Balance;112
8.8.1.7;5.3.1.7 Objective Function;112
8.9;5.4 Numerical Results;113
8.9.1;5.4.1 Data;113
8.9.2;5.4.2 Results;114
8.10;5.5 Conclusion;117
8.11;References;118
9;6 Offering Strategy of Thermal-Photovoltaic-Storage Based Generation Company in Day-Ahead Market;120
9.1;6.1 Introduction;120
9.2;6.2 Uncertainty Modeling;122
9.3;6.3 Problem Formulation;124
9.3.1;6.3.1 Objective Function;124
9.3.2;6.3.2 Emission Constraint;125
9.3.3;6.3.3 CVaR Constraints;126
9.3.4;6.3.4 Imbalance Constraints;126
9.3.5;6.3.5 BSS Constraints;127
9.3.6;6.3.6 Thermal Units Constraints;127
9.3.7;6.3.7 PV System Constraints;129
9.3.8;6.3.8 Offering Curves Constraints;129
9.4;6.4 Numerical Results;130
9.4.1;6.4.1 Input Data;130
9.4.2;6.4.2 Simulation Results;130
9.5;6.5 Conclusion;133
9.6;Nomenclature;137
9.6.1;Indices;137
9.6.2;Constants;138
9.6.3;Variables;138
9.7;References;139
10;7 Risk-Based Purchasing Energy for Electricity Consumers by Retailer Using Information Gap Decision Theory Considering Demand Response Exchange;141
10.1;Nomenclature;141
10.2;Parameters;141
10.3;Numbers;142
10.4;Variables;142
10.5;Functions;142
10.6;7.1 Introduction;143
10.6.1;7.1.1 Literature Review;143
10.6.2;7.1.2 Novelty and Contributions;145
10.6.3;7.1.3 Chapter Organization;148
10.7;7.2 Problem Formulation;149
10.7.1;7.2.1 Objective Function and Power Balance Constraint;149
10.7.2;7.2.2 Wholesale Market Suppliers;149
10.7.2.1;7.2.2.1 Pool Market;150
10.7.2.2;7.2.2.2 Forward Contract;150
10.7.3;7.2.3 Pool-Order Option;151
10.7.4;7.2.4 Forward DR;152
10.7.5;7.2.5 Reward-Base DR;153
10.8;7.3 IGDT Technique;154
10.8.1;7.3.1 System Model;154
10.8.2;7.3.2 Operation Requirements;154
10.9;7.4 Proposed IGDT-Based Risk-Constraint Formulation;155
10.9.1;7.4.1 Uncertainty Modeling;155
10.9.2;7.4.2 Robustness Function (Risk-Averse Strategy);156
10.9.3;7.4.3 Opportunity Function (Risk-Taker Strategy);157
10.9.4;7.4.4 Base Function (Risk-Neutral Strategy);158
10.10;7.5 Proposed Algorithm for Obtaining Optimal Bidding Strategy;158
10.11;7.6 Case Study;159
10.11.1;7.6.1 Risk-Neutral Results Without IGDT;162
10.11.2;7.6.2 Robustness and Opportunity Functions;163
10.11.3;7.6.3 Optimal Bidding Strategy Result;165
10.11.4;7.6.4 Comparison of Risk-Based Results;166
10.11.4.1;7.6.4.1 Analysis Results of Proposed DR Schemes;166
10.11.4.2;7.6.4.2 Analysis Results of Wholesale Market Suppliers;168
10.12;7.7 Conclusion;169
10.13;References;171
11;8 Stochastic Cooperative Charging Scheduling of PEV Fleets in Networked Microgrids Considering Price Responsive Demand and Emission Constraints;175
11.1;8.1 Introduction;175
11.1.1;8.1.1 Motivation;175
11.1.2;8.1.2 Literature Review;178
11.1.3;8.1.3 Contributions;180
11.1.4;8.1.4 Chapter Organization;181
11.2;8.2 Incentive-Based DR Programs;182
11.2.1;8.2.1 Concepts;182
11.2.2;8.2.2 Modeling;184
11.3;8.3 Electric Vehicle;185
11.3.1;8.3.1 Aim;185
11.3.2;8.3.2 Stages of EV Development;186
11.3.3;8.3.3 Classification of EVs;186
11.3.4;8.3.4 V2G Technology;187
11.4;8.4 Problem Formulation;189
11.4.1;8.4.1 Objective Function;189
11.4.1.1;8.4.1.1 Total Cost Function;189
11.4.1.2;8.4.1.2 Operation Cost of DGRs;190
11.4.1.3;8.4.1.3 Operation Cost of PEVs;190
11.4.1.4;8.4.1.4 Cost of Greenhouse Gas Emission;190
11.4.2;8.4.2 Constraints;191
11.4.2.1;8.4.2.1 Power Mismatch Constraint;191
11.4.2.2;8.4.2.2 Lines Limit;192
11.4.2.3;8.4.2.3 Limit of Power Flow in the Lines;192
11.4.2.4;8.4.2.4 Under/Over Voltage Limits;192
11.4.2.5;8.4.2.5 PEVs Limitations;192
11.4.2.6;8.4.2.6 DGRs Constraints;193
11.5;8.5 Scenario Modeling;193
11.6;8.6 Simulation Results;194
11.6.1;8.6.1 Data and Case Study;194
11.6.2;8.6.2 Numerical Results;197
11.7;8.7 Conclusion;200
11.8;References;201
12;9 Robust Scheduling of Plug-In Electric Vehicles Aggregator in Day-Ahead and Reserve Markets;204
12.1;Nomenclature;204
12.2;Set;204
12.3;Parameters;204
12.4;Numbers;205
12.5;Variables;205
12.6;9.1 Introduction;205
12.7;9.2 Deterministic-Based Scheduling of PEV Aggregator;207
12.8;9.3 Robust Optimization-Based Scheduling of PEV Aggregator;209
12.9;9.4 Case Study;211
12.10;9.5 Conclusion;216
12.11;References;216
13;10 Optimal Scheduling of Water Distribution Systems' Participation in Demand Response and Frequency Regulation Services;218
13.1;10.1 Introduction;218
13.2;10.2 Problem Formulation;219
13.3;10.3 Robust Optimization Approach;220
13.4;10.4 Water Distribution System Model;221
13.5;10.5 Case Study;225
13.6;10.6 Conclusion;232
13.7;References;232
14;11 Optimal Power Scheduling of a GenCo Using Two-Point Estimate Method;234
14.1;Nomenclature;234
14.2;Set;234
14.3;Known Parameters;234
14.4;Decision Variables;235
14.5;Functions;235
14.6;11.1 Introduction;235
14.7;11.2 Deterministic-Based Scheduling of a GenCo;236
14.8;11.3 Background of TPEM;238
14.9;11.4 Numerical Study;240
14.10;11.5 Comparison and Discussion;248
14.11;11.6 Conclusion;249
14.12;References;249
15;12 Bidding and Offering Strategies for Integration of Battery Storage System and Wind Turbine;252
15.1;Nomenclature;252
15.2;Indices;252
15.3;Input;252
15.4;Variables;253
15.5;12.1 Introduction;253
15.6;12.2 Problem Formulation;255
15.6.1;12.2.1 Objective Function;256
15.6.2;12.2.2 WT Model;256
15.6.3;12.2.3 BSS Model;257
15.7;12.3 Numerical Simulation;258
15.8;12.4 Conclusion;263
15.9;References;264
16;Index;267