Cooperative Optimal Control of Hybrid Energy Systems

✍ Scribed by Dong Yue, Huifeng Zhang, Chunxia Dou

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 376
• Category: Library

✦ Synopsis

This book mainly investigates the cooperative optimal control of hybrid energy system, it presents security control, multi-objective optimization, distributed optimization and distributed control approaches for tackling with security, economic and stability problem of the hybrid energy system.
It aims to solve some challenging problems including security issue, economic cost or benefits from both power generation side and load demand side, and coordination among different power generators.
The methods proposed in this book is novel and attractive, it consists of the hierarchical optimal control strategy for the security issue, multi-objective optimization for both economic and emission issue, and distributed optimal control for coordination among power generators.
Readers can learn novel methods or technique for tackling with the security issue, multiple-objective problem, and distributed coordination problem. It also may inspire readers to improve some drawbacks of existing alternatives.
Some fundamental knowledge prepared to read this book includes basic principles of the multi-agents system, robust optimization, Pareto-dominance optimization, and background of electrical engineering and renewable energy.

✦ Table of Contents

Preface
Structure and Readership
Contents
1 Introduction
1.1 Hybrid Energy Systems
1.2 The Overview of Security Control in Hybrid Energy System
1.2.1 The Voltage Security Control
1.2.2 The Supply-Security Control
1.3 The Overview of Optimization in Hybrid Energy System
1.3.1 The Multiobjective Optimization
1.3.2 The Distributed Optimization
1.3.3 The Game Optimization
1.4 The Overview of Cooperative Control in Hybrid Energy System
1.4.1 The Cooperative Control of DC Microgrid
1.4.2 The Cooperative Control of AC Microgrid
1.5 Structure of This Brief
References
Part I Cooperative Optimal Security Control of Hybrid Energy System
2 Multiagent System-Based Event-Triggered Hybrid Controls for High-Security Hybrid Energy Generation Systems
2.1 MAS-Based Control Scheme of HEGS
2.2 Event-Triggered Hybrid Controls
2.2.1 DHPN Model of the HEGS
2.2.2 Internal Switching Control
2.2.3 Distributed Dynamic Control
2.2.4 Coordinated Switching Control
2.3 Simulation Results
2.4 Conclusion
References
3 Multi-agent Based Hierarchical Hybrid Control for Smart Microgrid
3.1 The Multi-agent Based Hierarchical Hybrid Control Scheme
3.2 The Upper Level Energy Management Agent
3.2.1 The Energy Management Strategies
3.2.2 The Interactive Behaviors Between Energy Management Agent with Other Agents
3.3 The Middle Level Coordinated Control Agents
3.3.1 Coordinated Control Strategies of Operational Mode Between Different Unit Agentsitle
3.3.2 The Logic Coordinated Control Commands Between Middle Level and Lower Level Agents
3.4 The Lower Level Unit Control Agents
3.5 Simulation Studies
3.6 Conclusion
4 Two-Stage Optimal Operation Strategy of Isolated Power System with TSK Fuzzy Identification of Supply-Security
4.1 Takagi–Sugeno–Kang Fuzzy System with Recursive Least Square for Identification
4.2 Problem Formulation: Risk Degree Based Isolated Power System …
4.2.1 Uncertainty Analysis of Intermittent Energy Resources and System Load
4.2.2 Economic Dispatch Model with Uncertainty Degree in Isolated Power System
4.3 Fuzzy System Based Two Stage Optimization Strategy
4.3.1 TSK Fuzzy System Identification of Supply-Security in Isolated Power System
4.3.2 Switching Mechanisms Under Confidence Degree of Supply-Security
4.3.3 GD-MOCDE Approach for Optimizing the Economic Dispatch Problem of Isolated Power
4.4 Case Study
4.4.1 Fuzzy System Identification with Intermittent Energy Resources and System Load
4.4.2 Optimal Operation of an Isolated Power System with a Two-Stage Optimization Strategy
4.5 Conclusion
References
Part II Multi-objective Optimization for Optimal Operation of Hybrid Energy System
5 Probabilistic PBI Approach for Risk-Based Optimal Operation of Hybrid Energy Systems
5.1 Problem Formulation of the Stochastic Hybrid Energy System
5.1.1 Intermittent Power Generation with Uncertainty Budget
5.1.2 Problem Formulation
5.1.3 Constraints
5.2 MOEA/D with Penalty-Based Boundary Intersection Approach
5.3 Fuzzy Decision-Making Method for the Probabilistic Optimal Problem
5.3.1 Fuzzy Decision-Making Approach
5.3.2 Probabilistic Risk Evaluation
5.4 Simulations
5.4.1 Parameters Settings and Basic Data
5.4.2 Results and Analysis
5.5 Conclusion
References
6 Gradient Decent Based Multi-objective Optimization for Economic Emission of Hybrid Energy Systems
6.1 Problem Formulation
6.1.1 The Objective of Economic Cost
6.1.2 The Objective of Emission Rate
6.1.3 Constraints
6.2 The Multi-objective Cultural Differential Evolution (MOCDE)
6.2.1 The Principles of Differential Evolution
6.2.2 The Culture Knowledge Structure for Multi-objective Differential Evolution
6.3 The Gradient Decent Based Multi-objective
6.3.1 Gradient Decent Based Mutation Operator
6.3.2 The Designed Knowledge Structure
6.3.3 Constraint Handling Technology
6.4 The Implementation of GD-MOCDE on Hybrid Energy Systems
6.5 Case Study
6.5.1 Test System 1
6.5.2 Test System 2
6.5.3 Test System 3
6.5.4 Test System 4
6.5.5 Test System 5
6.6 Conclusion
References
7 Two-Layered Optimization Strategy for Hybrid Energy Systems with Price Bidding Based Demand Response
7.1 Price Bidding Strategy of Demand Response
7.2 Upper Level Problem: Event-Triggered Multi-agent Coordinated …
7.2.1 Agent Definition
7.2.2 Event-Triggered Optimization of Hybrid Energy System with Probabilistic Risk
7.3 Lower Level Problem: Convex Optimization for Multi-agent Subsystem
7.3.1 Lagrangian Relaxation Approach for Load Shifting
7.3.2 Consensus with Regularization Algorithm for DED
7.4 Case Study
7.4.1 Test System 1: Hybrid Energy System Without Switching Mode
7.4.2 Test System 2: Hybrid Energy System with Event-Triggered Switching Mode
7.5 Conclusion
References
Part III Distributed Optimization for Energy Management of Microgrid
8 Consensus-Based Economic Hierarchical Control Strategy for Islanded MG Considering Communication Path Reconstruction
8.1 The Control Structure and Control Process
8.1.1 The Control Structure
8.1.2 Explain the Overall Control Process
8.2 The CBPC
8.2.1 Theory Basic
8.2.2 Economic Optimization Strategy
8.2.3 Path Planning of the Undirected Communication Path
8.2.4 Path Reconstruction Method
8.2.5 Consensus Controller
8.3 The CBSC
8.3.1 Cyber-Physical Vulnerability Assessment
8.3.2 Path Planning of the Directed Communication Path
8.3.3 The Secondary Controller on Voltage
8.3.4 Stability Analysis
8.4 Case Study and Analysis
8.4.1 Case 1 Verify the Effectiveness of Cyber-Physical Vulnerability Assessments
8.4.2 Case 2 Verify the Effectiveness of Path Planning and Reconstruction Method
8.4.3 Case 3 Verify the Effectiveness of CBPC
8.4.4 Case 4 Verify the Effectiveness of CBSC
8.5 Conclusion
References
9 Multi-agent-system-based Bi-level Bidding Strategy of Microgrid with Game Eheory in the Electricity Market
9.1 The Framework of MAS-Based Bi-level Bidding
9.1.1 Introduction
9.1.2 The Framework of Bi-level Bidding MAS-Based
9.2 Game Based Bidding Strategy
9.2.1 The Model of All Agents
9.2.2 Game Theory
9.2.3 Bidding Strategy
9.3 The Solution of Bidding Model Based on Game Theory
9.4 Case Study and Analysis
9.5 Conclusion
References
Part IV Distributed Cooperative Control of DC Microgrid
10 Multiagent System-Based Distributed Coordinated Control for Radial DC Microgrid Considering Transmission Time Delays
10.1 The MAS Based Distributed Coordinated Control Scheme of DC MG
10.2 Control Mode and Dynamic Modeling
10.3 The Controller Design
10.3.1 Local Controller Design in First-Level Unit Agent
10.3.2 MAS Based Distributed Coordinated Controller Design
10.3.3 Implementation of the Distributed Coordinated Control
10.4 Experiment Studies
10.4.1 Case 1: The Load Demand Doubled in the DERj
10.4.2 Case 2: The Load Changes in Both DERp and DERq
10.5 Conclusion
References
11 MAS-Based Distributed Cooperative Control for DC Microgrid Through Switching Topology Communication Network with Time-Varying Delays
11.1 MAS-Based Control Structure
11.2 MAS-Based Distributed Cooperative Control Strategies
11.2.1 Secondary Control
11.2.2 Primary Control
11.2.3 Implementation Strategies of Distributed Cooperative Control Based on the MAS
11.3 Simulation Studies
11.3.1 Case 1: Load Variations
11.3.2 Case 2: ``Plug and Play'' of a der Unit
11.3.3 Case 3: Different Communication Delays
11.3.4 Case 4: Local Load Disturbance
11.4 Conclusion
References
12 Multiagent System-Based Integrated Design of Security Control and Economic Dispatch for Interconnected Microgrid Systems
12.1 MAS-Based Hierarchical Control Scheme
12.1.1 Lower Level Unit Agent
12.1.2 Upper Level Unit Agent
12.2 Insecurity-Events-Triggered Switching Controls
12.2.1 DHPN Model of the IMS
12.2.2 Local Switching Control
12.2.3 Coordinated Switching Control
12.3 Dynamic Economic Dispatch Model
12.3.1 Insecurity-Event-Triggered Global Optimization
12.3.2 DMPC-Based Distributed Optimal Control
12.4 Simulation Results
12.4.1 Case Study 1
12.4.2 Case Study 2
12.5 Conclusion
References
Part V Distributed Cooperative Control of Islanded AC Microgrids
13 Distributed Event-Triggered Cooperative Control for Frequency and Voltage Stability and Power Sharing in Isolated Inverter-Based Microgrid
13.1 Problem Formulation and Preliminaries
13.1.1 Inverter and Load Dodels
13.1.2 Communication Network
13.1.3 Control Purposes
13.2 Main Results
13.2.1 Distributed Event-Triggered Restoration Mechanisms
13.2.2 Distributed Event-Triggered Mechanism
13.2.3 Modified Distributed Event-Triggered Mechanism
13.3 Simulation
13.3.1 Implementation with Event-Triggered Mechanism 1
13.3.2 Implementation with Event-Triggered Mechanism 2
13.4 Conclusion
References
14 Event-Triggered Mechanism Based Distributed Optimal Frequency Regulation of Power Grid
14.1 Problem Formulation
14.1.1 Power Grid Model
14.1.2 Communication Network
14.1.3 Control Purpose
14.2 Main Results
14.2.1 Distributed Optimal Frequency Regulation Based on Event-Triggered Sampling Data
14.2.2 Static Event-Triggered Mechanism
14.2.3 Dynamic Event-Triggered Mechanism
14.3 Simulation
14.3.1 Implementation with Static Event-Triggered Mechanism
14.3.2 Implementation with Dynamic Event-Triggered Mechanism
14.4 Conclusion
References
15 A Virtual Complex Impedance Based P- Droop Method for Parallel-Connected Inverters in Low-Voltage AC Microgrids
15.1 Islanded Microgrid Structure, Modeling and Control
15.1.1 Voltage and Current Control Loop in Stationary Frame
15.1.2 Virtual Complex Impedance Strategy
15.1.3 Modified Droop Control Equation
15.2 Proposed P- Droop Control Method
15.2.1 Original P- Droop Control Method
15.2.2 Restoration Mechanism
15.2.3 Modified P- Droop Control Method
15.3 Discussion on the Effects of Relative Coefficients
15.4 Simulation Results
15.4.1 Performance Comparison of P-V and P- Droop Method
15.4.2 Improved Performance Brought by Modified Droop Method
15.4.3 Performance Comparison Based on a Complex Microgrid
15.5 Conclusion
References