product iamge

Integration of Distributed Resources in Smart Grids for Demand Response and Transactive Energy: A Case Study of TCLs

This document was uploaded by one of our users. The uploader already confirmed that they had the permission to publish it. If you are author/publisher or own the copyright of this documents, please report to us by using this DMCA report form.

Download
Search on Amazon

Simply click on the Download Book button.

Yes, Book downloads on Ebookily are 100% Free.

Sometimes the book is free on Amazon As well, so go ahead and hit "Search on Amazon"

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.

Author(s): Meng Song, Ciwei Gao
Publisher: Springer
Year: 2021

Language: English
Pages: 288
City: Singapore

Preface
Acknowledgements
Contents
Nomenclature
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
Chapter 10
1 Overview of TCLs in Smart Grids
1.1 Introduction
1.2 Fundamental Models of TCLs
1.2.1 Energy Conversion
1.2.2 Energy Exchange
1.3 Response Modes of TCLs
1.4 Control Strategies of TCLs
1.4.1 Centralized Control
1.4.2 Decentralized/Distributed Control
1.5 Modelling and Control Issues of TCLs
1.5.1 Cold Load Pickup
1.5.2 Impact Factors of TCL Control Strategies
1.6 System Models of TCLs
1.7 Dispatch Strategies of TCLs
1.8 Discussions of TCLs in DR Programs
1.8.1 Non-invasive Parameter Estimation
1.8.2 Reward Allocation Mechanism
1.8.3 Optimal Coordination of Virtual and Real Energy Storage
1.9 Promising Research Field—Transactive Control and Scheduling of TCLs
1.10 Conclusions
References
2 Impact Analysis of Uncertain Parameters on TCL Power Capacity
2.1 Introduction
2.2 TCL Model and Uncertain Parameters
2.2.1 TCL Model
2.2.2 Uncertain Parameters
2.3 TCL Power Capacity Calculation Strategy
2.4 Impact of Uncertain TCL Parameters
2.4.1 HDMR
2.4.2 HDMR Sensitivity Analyses
2.4.3 Framework for Fast TCL Power Capacity Calculation
2.5 Case Studies
2.5.1 HDMR Modeling Results
2.5.2 Effect of Population Heterogeneity and Dispatch Period
2.5.3 Comparisons with Other Methods
2.6 Conclusions
References
3 Time-Dependent Cold Load Pickup of TCLs and Its Application in Distribution System Load Restoration
3.1 Introduction
3.2 Time-Dependent CLPU Modeling
3.3 Problem Formulation
3.3.1 DSR with Time-Dependent CLPU
3.3.2 IGDT-Based Robust DSR Model
3.4 Numerical Results
3.4.1 The Impact of Time-Dependent CLPU on IEEE 13-Node Test Feeder
3.4.2 The Impact of Load Demand Uncertainty on IEEE 13-Node Test Feeder
3.4.3 Simulation Results of IEEE 123-Node Test Feeder
3.5 Conclusions
References
4 Aggregated Control of TCLs Based on Modified State Space Model
4.1 Introduction
4.2 Control Frameworks for TCLs
4.3 Aggregated TCL Model Without Control Signals
4.3.1 Thermodynamic Model of TCL System
4.3.2 Aggregation and Control Problem Formulation
4.3.3 Derivation of A
4.4 Aggregated TCL Model with Control Signals
4.4.1 Problem Formulation
4.4.2 Aggregated TCL Model with Control Signals
4.5 Case Studies
4.5.1 Performance of Aggregated TCL Model Without Control Signal
4.5.2 Performance of Aggregated TCL Model with Control Signals
4.6 Conclusions
References
5 Uniform-Time State Bin Model of Aggregated TCLs for Regulation Services
5.1 Introduction
5.2 Problem Formulation
5.2.1 The Thermodynamic Model of TCL System
5.2.2 Utim
5.3 Aggregate Control Model of TCLs
5.4 Control of TCLs for Fast Regulation Service
5.5 Case Studies
5.5.1 Performances of UTIM
5.5.2 Performance of TCLs Following the Fast Regulation Signals
5.5.3 Time Delay Impact of the Compressor
5.5.4 Comparisons of Different Control Methods
5.6 Conclusions
References
6 Thermal Battery Modeling of TCLs for Demand Response
6.1 Introduction
6.2 Fundamental Model of the Inverter TCL System
6.2.1 Thermodynamic Model of the Inverter TCL System
6.2.2 Electrical Model of the Inverter TCL System
6.3 Thermal Battery Modeling of Individual Inverter TCL System
6.3.1 Lithium-Ion Battery Model
6.3.2 TB Modeling of the Inverter TCL System
6.3.3 Comparisons of the Two Battery Models
6.4 Hierarchical Control Design
6.4.1 Battery Encapsulation and Conversion
6.4.2 Aggregation and Control
6.4.3 Optimal Dispatch
6.5 Case Studies
6.5.1 Optimization Results of TBs Compared with Lithium-Ion Batteries in Real-Time Market
6.5.2 Dispatch Period Impact
6.6 Conclusion
References
7 Comparison Analysis on Energy Storage Behaviors of TCLs Under Different Control Methods
7.1 Introduction
7.2 Basic Model of Inverter TCL System
7.3 Power Type Battery Modeling of Inverter TCLs
7.3.1 Circuit Model of PTBM
7.3.2 Mathematical Model of PTBM
7.3.3 Aggregation of Heterogeneous PTBMs
7.4 Capacity Type Battery Modeling of Inverter TCL
7.4.1 Circuit Model of CTBM
7.4.2 Mathematical Model of CTBM
7.5 Comparisons Between PTBM and CTBM
7.5.1 Response Speed
7.5.2 Power and Energy Capacity
7.5.3 Cost of Control
7.6 Dispatch Strategy for Output Optimizing of Wind Generation
7.7 Case Studies
7.7.1 Optimization Results
7.7.2 Impact of the End-users’ Comfort Setting
7.8 Conclusions
References
8 Multi-time Scale Models and Parameter Identification Method of TCLs
8.1 Introduction
8.2 Multi Time-Scale Dispatch Framework for Smoothing Out Wind Power Generation Variability
8.3 Modeling of Virtual Generator of FTCLs on Hourly Time-Scale
8.3.1 Control Method
8.3.2 Virtual Generator Modeling of FTCLs
8.4 Modeling of Virtual Battery of ITCLs on the Minute Time-Scale
8.4.1 Control Method
8.4.2 Virtual Battery Modeling of ITCLs
8.5 Modeling of Virtual Battery of FTCLs on the Second-Time Scale
8.5.1 Control Method
8.5.2 Modeling of Virtual Battery of FTCLs
8.6 Aggregated Parameter Estimation
8.6.1 Heterogeneity and Uncertainty of TCL Parameters
8.6.2 Parameter Estimation Via HDMR
8.7 Case Studies
8.7.1 HDMR Modeling Results
8.7.2 Impacts of the Number of Samples and TCL Parameter Distribution
8.7.3 Multi Time-Scale Dispatch Results
8.8 Conclusion
Appendix
References
9 Hierarchical Scheduling of TCL Flexibility for Transactive Energy
9.1 Introduction
9.2 TCL Scheduling Framework
9.3 Aggregation and De-aggregation of TCLs
9.3.1 Aggregation of TCLs at the Lower Stage
9.3.2 De-aggregation of TCLs at the Lower Stage
9.4 Transactive Energy Market Operation with Aggregated TCL Flexibility
9.5 Case Studies
9.5.1 Aggregators’ Transactive Trading Results at the Upper Stage
9.5.2 Control Performances of TCLs at the Lower Stage
9.5.3 Comparison of Conventional and Virtual Batteries
9.6 Conclusions
References
10 Multi-time Scale Transactive Scheduling of TCLs for Smoothing Microgrid Tie Flow Fluctuations
10.1 Introduction
10.2 The Framework of Multi-time Scale Coordinated Control and Scheduling
10.3 Multi-time Scale Scheduling Models of TCLs
10.3.1 Inverter TCL Model
10.3.2 Hour-Time Scale Control Model
10.3.3 Minute-Time Scale Control Model
10.3.4 Coordination of Hour and Minute-Time Scale Control of Inverter TCLs
10.4 Transactive Control of TCLs
10.4.1 Hour-Time Scale Response Curve
10.4.2 Minute-Time Scale Response Curve
10.5 Problem Formulation of Microgrid Scheduling
10.5.1 Hour-Time Scale Stochastic Control Strategy
10.5.2 Minute-Time Scale Control Strategy
10.5.3 Linearization
10.6 Case Studies
10.6.1 Parameter Setup
10.6.2 Hour-Time Scale Control Results
10.6.3 Minute-Time Scale Control Results
10.6.4 Comparison of Single and Multi-time Scale Control of TCLs
10.7 Conclusions
References