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