Control and Communication for Demand Response with Thermostatically Controlled Loads

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