The proliferation of renewable energy enhances the sustainability of power systems, but the inherent variability also poses great challenges to the planning and operation of large power grids. The corresponding electric power deficiencies can be compensated by fast ramping generators and energy storage devices. However, frequent ramp up/down power adjustments can increase the operation and the maintenance cost of generators. Moreover, storage devices are regarded as costly alternatives. Demand response (DR) and transactive energy can address this problem owing to its attractive and versatile capability for balancing the supply-demand, improving energy efficiency, and enhancing system resilience. Distributed resources are the typical participants of DR and transactive energy programs, which greatly contribute to keep the supply and demand in a balance.
Thermostatically controlled loads (TCLs) (i.e., air conditioners, water heaters, and refrigerators) represent an example of distributed resources, the ratio of which to the total power consumption in developed countries is up to 30%–40%. Providing tremendous potentials in adjustable power consumption, TCLs have attracted major interests in DR and transactive energy opportunities. It has highlighted the advantages of TCLs in responding to uncertainties in power systems.
This book provides an insight of TCLs as typical distributed resources in smart grids for demand response and transactive energy to address the imbalance between supply and demand problems in power systems. The key points on analysis of uncertainty parameters, aggregated control models, battery modelling, multi-time scale control, transactive control and robust restoration of TCLs are all included. These are the research points of smart grids and deserve much attention. We believe this book will offer the related researcher a better understanding on the integration of distributed resources into smart grid for demand response and transactive energy. And it will be helpful to address the problems in practical projects.

The grids are embracing more renewable energy, but its inherent variability also poses enormous challenges to the power systems. Traditional solutions from the supply side generally cause irreversible damage to generators and increased economic expense. In contrast, demand response (DR) and transactive energy can address the same problem from the demand side at less cost. Distributed resources, the typical participants in DR and transactive energy programmes, contribute significantly to addressing the imbalance between supply and demand because of the constant improvement in the penetration level of renewable energy.
Thermostatically controlled loads (TCLs), i.e. air conditioners, water heaters and refrigerators, represent an example of distributed resources, accounting for up to 30–40% of total power consumption in developed countries. They offer tremendous potential in adjustable power consumption and attract considerable interest in DR and transactive energy opportunities, which highlights their advantages in responding to uncertainties in power systems.
This book provides an insight into TCLs as typical distributed resources in smart grids for DR and transactive energy to address the imbalance between supply and demand. The key research on analysis of uncertainty parameters, modelling of cold load pickup, aggregated control, battery modelling, parameter identification, hierarchical scheduling and transactive control of TCLs in DR and transactive energy is all included. This book is written to offer the researchers, the staff in the power grid and college students a better understanding of the distributed resources integrated with smart grid for DR and transactive energy. Also, it is expected to help address the problems in practice.
The book is divided into ten chapters.
Chapter 1 provides the overview of TCLs in smart grids, including fundamental models, response modes, control strategies, modelling and control issues, system models, dispatch strategies, discussions and future research of TCLs.
Chapter 2 analyses the impacts of uncertain parameters on TCL power capacity. Because the parameters of TCLs are heterogeneous, the identification of those parameters that have a greater impact on their operational behaviour is of major importance. It helps to save the efforts on data collection and processing.
Chapter 3 models the most important characteristic of TCLs after an outage, namely cold load pickup. When TCLs are regulated in DR or transactive energy programmes, the cold load pickup phenomenon cannot be ignored and should be carefully considered.
Chapters 4 and 5 present two methods to model and control the aggregated TCLs at the aggregator or device level. Given the large number of TCLs, which are difficult to be regulated directly, aggregation is required in DR programmes.
Chapter 6 models TCLs as thermal batteries, which is quite different from other flexible loads. When TCLs are treated as batteries, they can be scheduled in a convenient manner.
Chapter 7 compares the thermal batteries of TCLs at the power system level under different control methods. TCLs are generally regulated by temperature setpoint adjustment and switch control in DR, which pose favourable characteristics for capacity-type and power-type batteries, respectively.
Chapter 8 models the operational behaviours of TCLs in multi-time scales. A parameter identification method is proposed due to the large number of TCLs and their varying parameters, which is a key point for scheduling TCLs at the power system level.
Chapter 9 presents the hierarchical scheduling method of TCLs for transactive energy based on the aggregated models in Chaps. 6–8. At the lower level, TCLs are regulated by aggregators via a virtual market. At the higher level, aggregator represents TCLs to trade with other aggregators.
Chapter 10 provides a multi-time scale transactive scheduling of TCLs to smooth microgrid tie flow fluctuations. TCLs are regulated by temperature set-point and frequency adjustment in hour and minute time scale, respectively. Moreover, the model parameters of TCLs in minute time scale are determined by the control behaviours of TCLs in hour time scale.
Nanjing, China Meng Song
August 2021 Ciwei Gao

1 Overview of TCLs in Smart Grids 1

1.1 Introduction 1

1.2 Fundamental Models of TCLs 2

      1.2.1 Energy Conversion 2

      1.2.2 Energy Exchange 3

1.3 Response Modes of TCLs 4

1.4 Control Strategies of TCLs 5

      1.4.1 Centralized Control 5

      1.4.2 Decentralized/Distributed Control 7

1.5 Modelling and Control Issues of TCLs 7

      1.5.1 Cold Load Pickup 7

      1.5.2 Impact Factors of TCL Control Strategies 9

1.6 System Models of TCLs 10

1.7 Dispatch Strategies of TCLs 11

1.8 Discussions of TCLs in DR Programs 12

      1.8.1 Non-invasive Parameter Estimation 12

      1.8.2 Reward Allocation Mechanism 12

      1.8.3 Optimal Coordination of Virtual and Real Energy Storage 13

1.9 Promising Research Field—Transactive Control and Scheduling of TCLs 13

1.10 Conclusions 14

References 15

2 Impact Analysis of Uncertain Parameters on TCL Power Capacity 21

2.1 Introduction 21

2.2 TCL Model and Uncertain Parameters 23

      2.2.1 TCL Model 23

      2.2.2 Uncertain Parameters 24

2.3 TCL Power Capacity Calculation Strategy 25

2.4 Impact of Uncertain TCL Parameters 30

      2.4.1 HDMR 30

      2.4.2 HDMR Sensitivity Analyses 33

      2.4.3 Framework for Fast TCL Power Capacity Calculation 36

2.5 Case Studies 36

      2.5.1 HDMR Modeling Results 36

      2.5.2 Effect of Population Heterogeneity and Dispatch Period 41

      2.5.3 Comparisons with Other Methods 44

2.6 Conclusions 45

References 46

3 Time-Dependent Cold Load Pickup of TCLs and Its Application in Distribution System Load Restoration 49

3.1 Introduction 49

3.2 Time-Dependent CLPU Modeling 51

3.3 Problem Formulation 55

      3.3.1 DSR with Time-Dependent CLPU 55

      3.3.2 IGDT-Based Robust DSR Model 59

3.4 Numerical Results 63

      3.4.1 The Impact of Time-Dependent CLPU on IEEE 13-Node Test Feeder 63

      3.4.2 The Impact of Load Demand Uncertainty on IEEE 13-Node Test Feeder 68

      3.4.3 Simulation Results of IEEE 123-Node Test Feeder 70

3.5 Conclusions 71

References 72

4 Aggregated Control of TCLs Based on Modified State Space Model 75

4.1 Introduction 75

4.2 Control Frameworks for TCLs 77

4.3 Aggregated TCL Model Without Control Signals 78

      4.3.1 Thermodynamic Model of TCL System 78

      4.3.2 Aggregation and Control Problem Formulation 79

      4.3.3 Derivation of A 82

4.4 Aggregated TCL Model with Control Signals 85

      4.4.1 Problem Formulation 85

      4.4.2 Aggregated TCL Model with Control Signals 86

4.5 Case Studies 90

      4.5.1 Performance of Aggregated TCL Model Without Control Signal 92

      4.5.2 Performance of Aggregated TCL Model with Control Signals 94

4.6 Conclusions 98

References 99

5 Uniform-Time State Bin Model of Aggregated TCLs for Regulation Services 103

5.1 Introduction 103

5.2 Problem Formulation 105

      5.2.1 The Thermodynamic Model of TCL System 105

      5.2.2 Utim 107

5.3 Aggregate Control Model of TCLs 109

5.4 Control of TCLs for Fast Regulation Service 116

5.5 Case Studies 120

      5.5.1 Performances of UTIM 121

      5.5.2 Performance of TCLs Following the Fast Regulation Signals 123

      5.5.3 Time Delay Impact of the Compressor 124

      5.5.4 Comparisons of Different Control Methods 126

5.6 Conclusions 127

References 127

6 Thermal Battery Modeling of TCLs for Demand Response 129

6.1 Introduction 129

6.2 Fundamental Model of the Inverter TCL System 131

      6.2.1 Thermodynamic Model of the Inverter TCL System 131

      6.2.2 Electrical Model of the Inverter TCL System 131

6.3 Thermal Battery Modeling of Individual Inverter TCL System 132

      6.3.1 Lithium-Ion Battery Model 132

      6.3.2 TB Modeling of the Inverter TCL System 133

      6.3.3 Comparisons of the Two Battery Models 136

6.4 Hierarchical Control Design 138

      6.4.1 Battery Encapsulation and Conversion 138

      6.4.2 Aggregation and Control 139

      6.4.3 Optimal Dispatch 142

6.5 Case Studies 144

      6.5.1 Optimization Results of TBs Compared with Lithium-Ion Batteries in Real-Time Market 145

      6.5.2 Dispatch Period Impact 149

6.6 Conclusion 153

References 154

7 Comparison Analysis on Energy Storage Behaviors of TCLs Under Different Control Methods 157

7.1 Introduction 157

7.2 Basic Model of Inverter TCL System 158

7.3 Power Type Battery Modeling of Inverter TCLs 159

      7.3.1 Circuit Model of PTBM 159

      7.3.2 Mathematical Model of PTBM 161

      7.3.3 Aggregation of Heterogeneous PTBMs 163

7.4 Capacity Type Battery Modeling of Inverter TCL 165

      7.4.1 Circuit Model of CTBM 165

      7.4.2 Mathematical Model of CTBM 166

7.5 Comparisons Between PTBM and CTBM 167

      7.5.1 Response Speed 167

      7.5.2 Power and Energy Capacity 168

      7.5.3 Cost of Control 170

7.6 Dispatch Strategy for Output Optimizing of Wind Generation 171

7.7 Case Studies 173

      7.7.1 Optimization Results 173

      7.7.2 Impact of the End-users' Comfort Setting 176

7.8 Conclusions 176

References 177

8 Multi-time Scale Models and Parameter Identification Method of TCLs 181

8.1 Introduction 181

8.2 Multi Time-Scale Dispatch Framework for Smoothing Out Wind Power Generation Variability 183

8.3 Modeling of Virtual Generator of FTCLs on Hourly Time-Scale 184

      8.3.1 Control Method 184

      8.3.2 Virtual Generator Modeling of FTCLs 187

8.4 Modeling of Virtual Battery of ITCLs on the Minute Time-Scale 188

      8.4.1 Control Method 188

      8.4.2 Virtual Battery Modeling of ITCLs 188

8.5 Modeling of Virtual Battery of FTCLs on the Second-Time Scale 189

      8.5.1 Control Method 189

      8.5.2 Modeling of Virtual Battery of FTCLs 190

8.6 Aggregated Parameter Estimation 193

      8.6.1 Heterogeneity and Uncertainty of TCL Parameters 193

      8.6.2 Parameter Estimation Via HDMR 194

8.7 Case Studies 194

      8.7.1 HDMR Modeling Results 195

      8.7.2 Impacts of the Number of Samples and TCL Parameter Distribution 199

      8.7.3 Multi Time-Scale Dispatch Results 200

8.8 Conclusion 202

Appendix 203

References 205

9 Hierarchical Scheduling of TCL Flexibility for Transactive Energy 209

9.1 Introduction 209

9.2 TCL Scheduling Framework 211

9.3 Aggregation and De-aggregation of TCLs 213

      9.3.1 Aggregation of TCLs at the Lower Stage 213

      9.3.2 De-aggregation of TCLs at the Lower Stage 216

9.4 Transactive Energy Market Operation with Aggregated TCL Flexibility 218

9.5 Case Studies 221

      9.5.1 Aggregators' Transactive Trading Results at the Upper Stage 222

      9.5.2 Control Performances of TCLs at the Lower Stage 226

      9.5.3 Comparison of Conventional and Virtual Batteries 228

9.6 Conclusions 231

References 231

10 Multi-time Scale Transactive Scheduling of TCLs for Smoothing Microgrid Tie Flow Fluctuations 235

10.1 Introduction 235

10.2 The Framework of Multi-time Scale Coordinated Control and Scheduling 237

10.3 Multi-time Scale Scheduling Models of TCLs 239

      10.3.1 Inverter TCL Model 239

      10.3.2 Hour-Time Scale Control Model 239

      10.3.3 Minute-Time Scale Control Model 240

      10.3.4 Coordination of Hour and Minute-Time Scale Control of Inverter TCLs 240

10.4 Transactive Control of TCLs 242

      10.4.1 Hour-Time Scale Response Curve 242

      10.4.2 Minute-Time Scale Response Curve 243

10.5 Problem Formulation of Microgrid Scheduling 245

      10.5.1 Hour-Time Scale Stochastic Control Strategy 245

      10.5.2 Minute-Time Scale Control Strategy 245

      10.5.3 Linearization 246

10.6 Case Studies 249

      10.6.1 Parameter Setup 249

      10.6.2 Hour-Time Scale Control Results 250

      10.6.3 Minute-Time Scale Control Results 253

      10.6.4 Comparison of Single and Multi-time Scale Control of TCLs 256

10.7 Conclusions 258

References 258

Prof. Ciwei Gao is the full professor of school of electrical engineering, Southeast University, P.R.China. He is the IEEE senior member, received the B.S. degree from North China Electric Power University, Baoding, China in 1999, the M.S. degree from Wuhan University, Wuhan, China in 2003, and Ph.D. degree from Politecnico di Torino in 2006 and Shanghai Jiaotong University in 2007. He has More than 20 years research experience in the field of electrical engineering, focusing on electricity market, demand side management and demand response, power planning and integrated energy systems. Provide course "Power system analysis", "Fundamentals of Power Economics" and "Power Planning" in the university for the graduate and undergraduate students. Authored nearly 200 publications of journals or conferences. PI of more than 100 research projects cooperating with State Grid Corporation of China, Southern Grid Corporation of China, China EPRI (Electrical Power Research Institute), Electricity/Energy Regulation Agencies, Energy Foundation of America and Prosperity Fundation of UK etc.

中科院文献情报中心