Control and Communication for Demand Response with Thermostatically Controlled Loads

Description

The book focuses on control and communication for demand response with thermostatically controlled loads. This is achieved by providing in-depth study on a number of major topics such as load control, optimization strategies, communication network model, resource allocation methods, system design, implementation, and performance evaluation. Two major cost modeling methods are established in detail, which are cost modeling based on Taguchi Loss Function and cost modeling based on regulation errors. The comprehensive and systematic treatment of issues in optimization strategies and resource allocation for demand response are one of the major features of the book, which is particularly suited for readers who are interested to learn solutions in control and communication. The book can benefit researchers, engineers, and graduate students in fields of control theory, automation, communication engineering and economics, etc. Preface Contents 1 Introduction 1.1 Background 1.2 System Model 1.2.1 Load Control Model 1.2.2 Communication Network Model 1.3 Problems Studied in This Book 1.3.1 Load Control and Optimization Strategy 1.3.2 Communication Network and Resource Allocation 1.4 Summary Part I Load Control and Optimization Strategies 2 Switching Control Strategies of Aggregated Commercial HVAC Systems for Demand Response 2.1 Introduction 2.2 System Model and Control Strategies 2.2.1 Individual HVAC Model 2.2.2 Typical Control Strategies 2.2.3 Switching Control Model 2.3 Controller Design and Optimization 2.3.1 Parameter Optimization 2.3.2 Switching Control Strategies I and II 2.3.3 Switching Control Strategy III 2.4 Simulations 2.5 Conclusions 3 Hybrid Control Strategy of Aggregated TCLs for Demand Response 3.1 Introduction 3.2 System Modeling 3.3 Hybrid Control Strategies 3.3.1 Step Rule of the On/Off Control 3.3.2 Parameter Optimization of the Setpoint-Regulation Control 3.3.3 Parallel and Cascade Control Structures 3.3.4 Control Framework 3.4 Simulation Results 3.4.1 Evaluation of the Step Rule 3.4.2 Optimization of the Control Parameters 3.4.3 Computation of the Allocation Proportions 3.4.4 Comparison of the Control Strategies 3.4.5 Sensitivity Analysis for the Thermal Capacitance 3.4.6 Different Temperature Bands Under the On/Off Control 3.5 Conclusions 4 Fuzzy Neural Network Control Strategy of Aggregated TCLs for Demand Response 4.1 Introduction 4.2 Problem Formulation 4.2.1 Individual TCL Characteristic 4.2.2 Frequency Regulation Problem 4.3 Fuzzy Neural Network Controller 4.3.1 Fuzzy Neural Network Structure 4.3.2 Fuzzy Neural Network Learning Algorithm 4.3.3 Optimization of Initial Value of Adjustable Parameters 4.4 Simulation Results 4.5 Conclusion 5 Optimal Control of Aggregated TCLs Based on Tracking Differentiator 5.1 Introduction 5.2 System Model 5.2.1 Thermal Dynamics of Individual TCL 5.2.2 Thermal Dynamics of Aggregated TCLs 5.2.3 Frequency Regulation 5.3 Problem Formulation 5.3.1 Regulation Cost 5.3.2 Discomfort Cost 5.4 Effective Control Strategy Based on TD 5.4.1 Control Strategy Design 5.4.2 Implementation 5.5 Simulation Results 5.5.1 Tracking Performance 5.5.2 Grouping Performance 5.6 Conclusion 6 Optimizing Regulation of Aggregated TCLs Based on Multi-Swarm PSO 6.1 Introduction 6.2 System Model and Problem Formulation 6.2.1 Individual TCL Model 6.2.2 Problem Formulation 6.3 Optimal Solutions 6.3.1 Mapping 6.3.2 Binary DMS-PSO-CLS 6.4 Simulation Results 6.4.1 Case Comparisons 6.4.2 Parameter Analysis 6.5 Conclusion Part II Communication Network and Resource Allocation 7 Communication Network and Cost Modeling 7.1 Communication Network Model 7.1.1 Two-tier Communication Network 7.1.2 Cooperative Relaying Network 7.1.3 Packet Loss Model 7.2 Cost Modeling 7.2.1 Cost Modeling Based on Taguchi Loss Function 7.2.2 Cost Modeling Based on Regular Errors 7.3 Conclusion 8 Bandwidth Allocation for Cooperative Relaying Network 8.1 Introduction 8.2 Preliminaries 8.3 System Model 8.4 Bargaining Models and Solutions 8.4.1 Problem Formulation 8.4.2 Case Study 8.4.3 Model Extension 8.5 Simulation Results 8.6 Conclusion 9 Distributed Power Allocation and Relay Selection for Cooperative Relaying Network 9.1 Introduction 9.2 Noncooperative Game 9.3 System Model 9.3.1 Demand Response and Electricity Cost 9.3.2 Transmission Rates 9.4 Stackelberg Game Formulation and Analysis 9.4.1 Stackelberg Game Modeling 9.4.2 Payment Selection Game 9.4.3 Maximizing Profits of Telecom Operators 9.4.4 Relaying Conditions 9.4.5 Strategy Design for Relaying Group with One DAU 9.5 Implementation Protocols 9.5.1 Heuristic Algorithm 9.5.2 Potential Realization in 5G Networks 9.6 Simulation Results 9.6.1 DAU Assignment, Transmission Power Allocation and Payment Selection 9.6.2 Cost Reduction and Profit Improvement 9.7 Conclusion 10 Centralized Power Allocation and Relay Selection for Cooperative Relaying Network 10.1 Introduction 10.2 System Description 10.2.1 Demand-Side Cooperative Communication Network 10.2.2 Packet Loss Model and Costs to Utility Company 10.3 System Model and Solutions 10.4 MS-ABC Algorithm 10.5 Simulation Results 10.5.1 Comparisons Between MS-ABC and I-ABC 10.5.2 Relay Assignment and Power Allocation Under Case C 10.6 Conclusion 11 Interference Management and Power Control for Cognitive Radio Network 11.1 Introduction 11.2 System Model 11.3 Problem Formulation 11.4 Stackelberg Game Formulation an Analysis 11.4.1 The Optimal Strategies of Gateways 11.4.2 The Convergence of ADPP 11.4.3 The Optimal Solution of PBS 11.4.4 A Modified Distributed Power Control Method 11.5 Simulation Results 11.6 Conclusion 12 Power Allocation for Relaying-Based Cognitive Radio Network 12.1 Introduction 12.2 Cognitive Wireless Network Model in Smart Grid 12.2.1 Cognitive Wireless Network 12.2.2 Transmission Formulation of The Network 12.3 Problem Formulation and Solutions 12.3.1 PSO Algorithm 12.3.2 The Solution with One Relay 12.4 Simulation Results 12.5 Conclusion 13 Spectrum Allocation and Power Allocation for Relaying-Based Cognitive Radio Network 13.1 Introduction 13.2 Cooperative and Cognitive Network Model 13.2.1 Confidence Level of Sub-bands 13.2.2 Receiving Rates of DAUs 13.3 Cost Modeling and Minimization 13.3.1 Cost Modeling 13.3.2 Spectrum Allocation 13.3.3 Relay Power Optimization 13.3.4 Spectrum Allocation and Relay Power Optimization Algorithm 13.4 Simulation Results 13.5 Conclusion Appendix References