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Distributed energy management of electrical power systems / Yinliang Xu, Wei Zhang, Wenxin Liu, Wen Yu.

By: Xu, Yinliang [author.].
Contributor(s): Zhang, Wei (Engineer) [author.] | Liu, Wenxin, 1978- [author.] | Yu, Wen, 1977- [author.] | IEEE Xplore (Online Service) [distributor.] | Wiley [publisher.].
Material type: materialTypeLabelBookSeries: IEEE Press series on power engineering: 100Publisher: Hoboken, New Jersey : John Wiley & Sons, Inc., [2020]Distributor: [Piscataqay, New Jersey] : IEEE Xplore, [2020]Edition: First edition.Description: 1 PDF.Content type: text Media type: electronic Carrier type: online resourceISBN: 9781119534877.Subject(s): Distributed generation of electric power | Electric power systems -- Management | Distributed parameter systemsGenre/Form: Electronic books.Additional physical formats: Print version:: Distributed energy management of electrical power systemsDDC classification: 621.31/213 Online resources: Abstract with links to resource Also available in print.
Contents:
1 Background 1 -- 1.1 Power Management 1 -- 1.2 Traditional centralized vs. distributed solutions to power management 2 -- 1.3 Existing distributed control approaches 3 -- 2 Algorithm Evaluation 5 -- 2.1 Communication Network Topology Configuration 5 -- 2.1.1 Communication Network Design for Distributed Applications 5 -- 2.1.2 N-1 Rule for communication network design 6 -- 2.1.3 Convergence of distributed algorithms with variant communication network typologies -- 2.2 Real-Time Digital Simulation 10 -- 2.2.1 Develop MAS Platform Using JADE 11 -- 2.2.2 Test distributed algorithms using MAS 12 -- 2.2.3 MAS based Real-time Simulation Platform 13 -- References 15 -- 3 Distributed Active Power Control 17 -- 3.1 Subgradient-based Active Power Sharing 17 -- 3.1.1 Introduction 17 -- 3.1.2 Preliminaries-Conventional Droop Control Approach 18 -- 3.1.3 The proposed Subgradient-Based Control Approach 19 -- 3.1.4 Control of Multiple Distributed Generators 24 -- 3.1.5 Simulation analyses 27 -- 3.1.6 Conclusion 33 -- 3.2 Distributed Dynamic Programming Based Approach for Economic Dispatch in Smart Grids 35 -- 3.2.1 Introduction 35 -- 3.2.2 Preliminary 36 -- 3.2.3 Graph theory 36 -- 3.2.4 Dynamic Programming 37 -- 3.2.5 Problem Formulation 37 -- 3.2.6 Economic Dispatch Problem 37 -- 3.2.7 Discrete Economic Dispatch Problem 38 -- 3.2.8 Proposed Distributed Dynamic Programming Algorithm 39 -- 3.2.9 Distributed Dynamic Programming Algorithm 39 -- 3.2.10 Algorithm Implementation 40 -- 3.2.11 Simulation Studies 41 -- 3.2.12 Four-generator system: synchronous iteration 41 -- 3.2.13 Four-generator system: Asynchronous iteration 44 -- 3.2.14 IEEE 162-bus system 46 -- 3.2.15 Hardware Implementation 47 -- 3.2.16 Conclusion 48 -- 3.3 Constrained Distributed Optimal Active Power Dispatch 48 -- 3.3.1 Problem Formulation 49 -- 3.3.2 Distributed Gradient Algorithm 50 -- 3.3.3 Distributed gradient algorithm 50 -- 3.3.4 Inequality Constraints Handling 52 -- 3.3.5 Numerical Example 53 -- 3.3.6 Control Implementation 56.
3.3.7 Communication Network Design 57 -- 3.3.8 Generator Control Implementation 58 -- 3.3.9 Simulation Studies 59 -- 3.3.10 Real-time Simulation Platform 59 -- 3.3.11 IEEE-30 Bus System 59 -- 3.3.12 Conclusion and Discussion 66 -- References 67 -- 4 Distributed Reactive Power Control 73 -- 4.1 Q-Learning based reactive power control 73 -- 4.1.1 Introduction 73 -- 4.1.2 Background 74 -- 4.1.3 Algorithm used to collect global information 74 -- 4.1.4 Reinforcement learning 75 -- 4.1.5 MAS based RL Algorithm for ORPD 75 -- 4.1.6 RL Reward Function Definition 76 -- 4.1.7 Distributed Q-Learning for ORPD 76 -- 4.1.8 MASRL implementation for ORPD 78 -- 4.1.9 Simulation Results 79 -- 4.1.10 The Ward-Hale 6-bus system 79 -- 4.1.11 Conclusion 86 -- 4.2 Sub-gradient Based Reactive Power Control 87 -- 4.2.1 Introduction 87 -- 4.2.2 Problem Formulation 89 -- 4.2.3 The Distributed Sub-gradient Algorithm 90 -- 4.2.4 Subgradient distribution Calculation 91 -- 4.2.5 Realization of Mas-based Solution 94 -- 4.2.6 Simulation and Tests 96 -- 4.2.7 CONCLUSION 105 -- References -- 5 Distributed Demand-Side Management 111 -- 5.1 System Design and Problem Formulation 112 -- 5.1.1 System description and problem formulation 112 -- 5.1.2 Problem Formulation 113 -- 5.1.3 Distributed Dynamic Programming 114 -- 5.1.4 Abstract Framework of Dynamic Programming (DP) 114 -- 5.1.5 Distributed Solution for Dynamic Programming Problem 115 -- 5.1.6 Numerical Example 117 -- 5.1.7 Implementation of the LM system 118 -- 5.1.8 Simulation Studies 119 -- 5.1.9 Test with IEEE 14-bus System 119 -- 5.1.10 Large test Systems 124 -- 5.1.11 Variable Renewable Generation 125 -- 5.1.12 With Time-delay/Packet Loss 126 -- 5.1.13 Conclusion and discussion 127 -- 5.2 Optimal Distributed Charging Rate Control of Plug-in Electric Vehicles for Demand -- Management 128 -- 5.2.1 Background 129 -- 5.2.2 Problem Formulation Of The Proposed Control Strategy 130 -- 5.2.3 Proposed Cooperative Control Algorithm 133 -- 5.2.4 MAS Framework 133.
5.2.5 The design and analysis of distributed algorithm 134 -- 5.2.6 Algorithm Implementation 134 -- 5.2.7 Simulation Studies 136 -- 5.2.8 Case Study 1 136 -- 5.2.9 Case Study 2 138 -- 5.2.10 Case Study 3 139 -- 5.2.11 Conclusion 140 -- References -- 6 Distributed Social Welfare Optimization 145 -- 6.1 Formulation of OEM Problem 146 -- 6.1.1 Social Welfare Maximization Model 147 -- 6.1.2 Market-based Self-interest Motivation Mode 148 -- 6.1.3 Relationship between two models 149 -- 6.2 Fully distributed MAS-Based OEM Solution 151 -- 6.2.1 Distributed Price Updating Algorithm 151 -- 6.2.2 Distributed Supply-demand Mismatch Discovery Algorithm 153 -- 6.2.3 Implementation of MAS-based OEM Solution 153 -- 6.3 Simulation Studies 154 -- 6.3.1 Tests with a 6-bus System 155 -- 6.3.2 Test with IEEE 30-bus System 161 -- 6.4 Conclusion 162 -- References -- 7 Distributed State Estimation 165 -- 7.1 A Distributed Approach for Multi-area State Estimation Based on Consensus Algorithm 165 -- 7.1.1 The Implementation for Distributed State Estimation 171 -- 7.1.2 Case Studies 172 -- 7.1.3 Conclusion and Discussion 175 -- 7.2 Multi-agent System Based Integrated Solution for Topology Identification and State Estimation -- 7.2.1 Measurement Model of Multi-Area Power System 180 -- 7.2.2 Distributed Subgradient Algorithm for MAS-Based Optimization 181 -- 7.2.3 Distributed Topology Identification 184 -- 7.2.4 Distributed State Estimation 187 -- 7.2.5 Implementation of the Integrated MAS Based Solution for TI and SE188 -- 7.2.6 Simulation Studies 189 -- 7.2.7 Conclusion and Discussion 197 -- References -- 8 Test through Power Hardware in the Loop Experimentation till Real-World Implementation -- 8.1 Steps of Algorithm Evaluation 201 -- 8.2 C-HIL Simulations 202 -- 8.2.1 PC-based C-HIL simulation 203 -- 8.2.2 DSP Control Board based Controller HIL Simulation 205 -- 8.2.3 Power Hardware-In-the-Loop Simulation 207 -- 8.2.4 Hardware Experimentation 209 -- 8.3 Development of Microgrid Test-beds 209.
8.3.1 Development of Modular 3-phase AC microgrid testbed 209 -- 8.3.2 Development of Modular Single-phase AC/DC microgrid testbed 210 -- 8.3.3 Control Algorithm Implementation with the New Microgrid Test-beds 211 -- 8.4 Hardware Experimental Studies 212 -- 8.4.1 Algorithm Design 212 -- 8.4.2 Algorithm Test 213 -- References -- 9 Conclusion and Future Work 217 -- 9.1 Consider More Constraints and Details 217 -- 9.2 Future work 220 References.
Summary: "The ever-growing demand, rising penetration level of renewable generation, and increasing complexity of electric power systems, pose new challenges to control, operation, management and optimization of power grids. Conventional centralized control structure requires a complex communication network with two-way communication links and a powerful central controller to process large amount of data, which reduces overall system reliability and increases its sensitivity to failures, thus it may not be able to operate under the increased number of distributed renewable generation units. Distributed control strategy enables easier scalability, simpler communication network, and faster distributed data processing, which can facilitate highly efficient information sharing and decision making"-- Provided by publisher.
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Includes bibliographical references and index.

1 Background 1 -- 1.1 Power Management 1 -- 1.2 Traditional centralized vs. distributed solutions to power management 2 -- 1.3 Existing distributed control approaches 3 -- 2 Algorithm Evaluation 5 -- 2.1 Communication Network Topology Configuration 5 -- 2.1.1 Communication Network Design for Distributed Applications 5 -- 2.1.2 N-1 Rule for communication network design 6 -- 2.1.3 Convergence of distributed algorithms with variant communication network typologies -- 2.2 Real-Time Digital Simulation 10 -- 2.2.1 Develop MAS Platform Using JADE 11 -- 2.2.2 Test distributed algorithms using MAS 12 -- 2.2.3 MAS based Real-time Simulation Platform 13 -- References 15 -- 3 Distributed Active Power Control 17 -- 3.1 Subgradient-based Active Power Sharing 17 -- 3.1.1 Introduction 17 -- 3.1.2 Preliminaries-Conventional Droop Control Approach 18 -- 3.1.3 The proposed Subgradient-Based Control Approach 19 -- 3.1.4 Control of Multiple Distributed Generators 24 -- 3.1.5 Simulation analyses 27 -- 3.1.6 Conclusion 33 -- 3.2 Distributed Dynamic Programming Based Approach for Economic Dispatch in Smart Grids 35 -- 3.2.1 Introduction 35 -- 3.2.2 Preliminary 36 -- 3.2.3 Graph theory 36 -- 3.2.4 Dynamic Programming 37 -- 3.2.5 Problem Formulation 37 -- 3.2.6 Economic Dispatch Problem 37 -- 3.2.7 Discrete Economic Dispatch Problem 38 -- 3.2.8 Proposed Distributed Dynamic Programming Algorithm 39 -- 3.2.9 Distributed Dynamic Programming Algorithm 39 -- 3.2.10 Algorithm Implementation 40 -- 3.2.11 Simulation Studies 41 -- 3.2.12 Four-generator system: synchronous iteration 41 -- 3.2.13 Four-generator system: Asynchronous iteration 44 -- 3.2.14 IEEE 162-bus system 46 -- 3.2.15 Hardware Implementation 47 -- 3.2.16 Conclusion 48 -- 3.3 Constrained Distributed Optimal Active Power Dispatch 48 -- 3.3.1 Problem Formulation 49 -- 3.3.2 Distributed Gradient Algorithm 50 -- 3.3.3 Distributed gradient algorithm 50 -- 3.3.4 Inequality Constraints Handling 52 -- 3.3.5 Numerical Example 53 -- 3.3.6 Control Implementation 56.

3.3.7 Communication Network Design 57 -- 3.3.8 Generator Control Implementation 58 -- 3.3.9 Simulation Studies 59 -- 3.3.10 Real-time Simulation Platform 59 -- 3.3.11 IEEE-30 Bus System 59 -- 3.3.12 Conclusion and Discussion 66 -- References 67 -- 4 Distributed Reactive Power Control 73 -- 4.1 Q-Learning based reactive power control 73 -- 4.1.1 Introduction 73 -- 4.1.2 Background 74 -- 4.1.3 Algorithm used to collect global information 74 -- 4.1.4 Reinforcement learning 75 -- 4.1.5 MAS based RL Algorithm for ORPD 75 -- 4.1.6 RL Reward Function Definition 76 -- 4.1.7 Distributed Q-Learning for ORPD 76 -- 4.1.8 MASRL implementation for ORPD 78 -- 4.1.9 Simulation Results 79 -- 4.1.10 The Ward-Hale 6-bus system 79 -- 4.1.11 Conclusion 86 -- 4.2 Sub-gradient Based Reactive Power Control 87 -- 4.2.1 Introduction 87 -- 4.2.2 Problem Formulation 89 -- 4.2.3 The Distributed Sub-gradient Algorithm 90 -- 4.2.4 Subgradient distribution Calculation 91 -- 4.2.5 Realization of Mas-based Solution 94 -- 4.2.6 Simulation and Tests 96 -- 4.2.7 CONCLUSION 105 -- References -- 5 Distributed Demand-Side Management 111 -- 5.1 System Design and Problem Formulation 112 -- 5.1.1 System description and problem formulation 112 -- 5.1.2 Problem Formulation 113 -- 5.1.3 Distributed Dynamic Programming 114 -- 5.1.4 Abstract Framework of Dynamic Programming (DP) 114 -- 5.1.5 Distributed Solution for Dynamic Programming Problem 115 -- 5.1.6 Numerical Example 117 -- 5.1.7 Implementation of the LM system 118 -- 5.1.8 Simulation Studies 119 -- 5.1.9 Test with IEEE 14-bus System 119 -- 5.1.10 Large test Systems 124 -- 5.1.11 Variable Renewable Generation 125 -- 5.1.12 With Time-delay/Packet Loss 126 -- 5.1.13 Conclusion and discussion 127 -- 5.2 Optimal Distributed Charging Rate Control of Plug-in Electric Vehicles for Demand -- Management 128 -- 5.2.1 Background 129 -- 5.2.2 Problem Formulation Of The Proposed Control Strategy 130 -- 5.2.3 Proposed Cooperative Control Algorithm 133 -- 5.2.4 MAS Framework 133.

5.2.5 The design and analysis of distributed algorithm 134 -- 5.2.6 Algorithm Implementation 134 -- 5.2.7 Simulation Studies 136 -- 5.2.8 Case Study 1 136 -- 5.2.9 Case Study 2 138 -- 5.2.10 Case Study 3 139 -- 5.2.11 Conclusion 140 -- References -- 6 Distributed Social Welfare Optimization 145 -- 6.1 Formulation of OEM Problem 146 -- 6.1.1 Social Welfare Maximization Model 147 -- 6.1.2 Market-based Self-interest Motivation Mode 148 -- 6.1.3 Relationship between two models 149 -- 6.2 Fully distributed MAS-Based OEM Solution 151 -- 6.2.1 Distributed Price Updating Algorithm 151 -- 6.2.2 Distributed Supply-demand Mismatch Discovery Algorithm 153 -- 6.2.3 Implementation of MAS-based OEM Solution 153 -- 6.3 Simulation Studies 154 -- 6.3.1 Tests with a 6-bus System 155 -- 6.3.2 Test with IEEE 30-bus System 161 -- 6.4 Conclusion 162 -- References -- 7 Distributed State Estimation 165 -- 7.1 A Distributed Approach for Multi-area State Estimation Based on Consensus Algorithm 165 -- 7.1.1 The Implementation for Distributed State Estimation 171 -- 7.1.2 Case Studies 172 -- 7.1.3 Conclusion and Discussion 175 -- 7.2 Multi-agent System Based Integrated Solution for Topology Identification and State Estimation -- 7.2.1 Measurement Model of Multi-Area Power System 180 -- 7.2.2 Distributed Subgradient Algorithm for MAS-Based Optimization 181 -- 7.2.3 Distributed Topology Identification 184 -- 7.2.4 Distributed State Estimation 187 -- 7.2.5 Implementation of the Integrated MAS Based Solution for TI and SE188 -- 7.2.6 Simulation Studies 189 -- 7.2.7 Conclusion and Discussion 197 -- References -- 8 Test through Power Hardware in the Loop Experimentation till Real-World Implementation -- 8.1 Steps of Algorithm Evaluation 201 -- 8.2 C-HIL Simulations 202 -- 8.2.1 PC-based C-HIL simulation 203 -- 8.2.2 DSP Control Board based Controller HIL Simulation 205 -- 8.2.3 Power Hardware-In-the-Loop Simulation 207 -- 8.2.4 Hardware Experimentation 209 -- 8.3 Development of Microgrid Test-beds 209.

8.3.1 Development of Modular 3-phase AC microgrid testbed 209 -- 8.3.2 Development of Modular Single-phase AC/DC microgrid testbed 210 -- 8.3.3 Control Algorithm Implementation with the New Microgrid Test-beds 211 -- 8.4 Hardware Experimental Studies 212 -- 8.4.1 Algorithm Design 212 -- 8.4.2 Algorithm Test 213 -- References -- 9 Conclusion and Future Work 217 -- 9.1 Consider More Constraints and Details 217 -- 9.2 Future work 220 References.

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"The ever-growing demand, rising penetration level of renewable generation, and increasing complexity of electric power systems, pose new challenges to control, operation, management and optimization of power grids. Conventional centralized control structure requires a complex communication network with two-way communication links and a powerful central controller to process large amount of data, which reduces overall system reliability and increases its sensitivity to failures, thus it may not be able to operate under the increased number of distributed renewable generation units. Distributed control strategy enables easier scalability, simpler communication network, and faster distributed data processing, which can facilitate highly efficient information sharing and decision making"-- Provided by publisher.

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