Demand Response Application in Smart Grids: Concepts and Planning Issues - Volume 1

Preface
Contents
About the Editors
Chapter 1: Concept and Glossary of Demand Response Programs
	1.1 Introduction
	1.2 Demand-Side Management
		1.2.1 Demand Response Programs’ Definition
		1.2.2 Demand Response Programs’ Benefits
	1.3 Applications of DRP in Power Systems
		1.3.1 DRP for Planning of Power Systems
			1.3.1.1 Distribution Expansion Planning (DEP)
			1.3.1.2 Generation and Transmission Expansion Planning (GEP and TEP)
			1.3.1.3 Resource Allocation
		1.3.2 DRP for Operation of Power Systems
			1.3.2.1 Frequency Regulation
			1.3.2.2 RES Integration
			1.3.2.3 Market Performance
			1.3.2.4 Energy Hub Scheduling
			1.3.2.5 Distribution Network Operation
			1.3.2.6 Generation and Transmission Level Operation
	1.4 Conclusion
	References
Chapter 2: Comprehensive Modeling of Demand Response Programs
	2.1 Introduction
	2.2 Modeling of DRPs
		2.2.1 Linear and Nonlinear Customer Demand Functions
		2.2.2 Price Elasticity of Demand
		2.2.3 Linear Modeling of Price-Based DRPs
		2.2.4 Linear Modeling of Price-Based/Incentive-Based DRPs
		2.2.5 Nonlinear Modeling of Price-Based/Incentive-Based DRPs
		2.2.6 Flexible Price Elasticity of Demand
			2.2.6.1 Linear Model
			2.2.6.2 Power Model
			2.2.6.3 Exponential Model
			2.2.6.4 Logarithmic Model
			2.2.6.5 Hyperbolic Model
		2.2.7 Comprehensive Modeling of DRPs
		2.2.8 Customer Response to DRPs Using Optimization Methods
		2.2.9 Market Clearing Programs
	2.3 Conclusion
	References
Chapter 3: Linear and Nonlinear Modeling of Demand Response Programs
	3.1 Introduction
	3.2 Demand Response Models
		3.2.1 Linear Demand Response Model
		3.2.2 Nonlinear Demand Response Models
			3.2.2.1 Power Model
			3.2.2.2 Exponential Model
			3.2.2.3 Logarithmic Model
	3.3 DRPs Prioritization Procedure
	3.4 Conclusion
	References
Chapter 4: Modeling an Improved Demand Response Program in Day-Ahead and Intra-day Markets
	4.1 Introduction
	4.2 Incentive-Based Demand Response Program (IBDRP)
	4.3 Formulation of an Improved IBDRP
		4.3.1 Elasticity Coefficients
		4.3.2 The Proposed IBDRP in Day-Ahead and Intra-day Market
	4.4 Numerical Results
		4.4.1 Case Study I: Peak Load of Spanish Electricity Market
		4.4.2 Case Study II: Domestic Consumers
	4.5 Conclusions
	References
Chapter 5: New Demand Response Platform with Machine Learning and Data Analytics
	5.1 Introduction
	5.2 Machine Learning and Data Science
	5.3 Topics on Machine Learning Methods
		5.3.1 Introduction
		5.3.2 ML Process
		5.3.3 Supervised, Unsupervised, and Semi-supervised Problems
			5.3.3.1 Supervised Machine Learning
			5.3.3.2 Unsupervised Machine Learning
			5.3.3.3 Semi-supervised Machine Learning
		5.3.4 Decision Tree Learning
		5.3.5 Deep Learning
		5.3.6 Reinforcement Learning
	5.4 Machine Learning in Electricity Markets
		5.4.1 Characteristics of Electricity Prices
		5.4.2 Electricity Price Modeling
		5.4.3 Examples of ML for Price Design in Electricity Markets
		5.4.4 ML for Customer Behavior Learning
			5.4.4.1 Load Forecast
			5.4.4.2 Load Profiling for Load Forecast
			5.4.4.3 ML for User Comfort and Preferences
			5.4.4.4 Machine Learning to Evaluate the Price Elasticity
		5.4.5 EV Charging Management
			5.4.5.1 Numerical Example
	5.5 Applications of Machine Learning and Data Analytics in DR
		5.5.1 Uncovering Unknown Electricity Consumption Patterns
		5.5.2 Convincing More Customers to Enrol in DR Programs
		5.5.3 EV Integration
		5.5.4 Obtain Customers’ Loyalty
	5.6 Benefits, Challenges, and Drawbacks of Machine Learning Implementation in DR
		5.6.1 Benefits
		5.6.2 Barriers, Challenges, and Problems
	5.7 Conclusion
		5.7.1 Future Works
	References
Chapter 6: Demand-Side Management Programs of the International Energy Agency
	6.1 Introduction
	6.2 The International Energy Agency Demand-Side Management Program
	6.3 Tasks
		6.3.1 Completed Tasks
			6.3.1.1 Task 1
				Achieved Results
			6.3.1.2 Task 2
				Communications Technologies for Demand-Side Management
				Achieved Results
			6.3.1.3 Task 3
				Cooperative Procurement of Innovative Technologies for Demand-Side Management
			6.3.1.4 Task 4
				Development of Improved Methods for Integrating Demand-Side Management into Resource
					Planning
			6.3.1.5 Task 5
				Techniques for Implementation of Demand-Side Management Technology in the Marketplace
				Achieved Result
			6.3.1.6 Task 6
				DSM and Energy Efficiency in Changing Electricity Business Environments
				Achieved Results
			6.3.1.7 Task 7
				International Collaboration on Market Transformation
				Achieved Results
			6.3.1.8 Task 8
				Demand-Side Bidding in a Competitive Electricity Market
				Achieved Results
			6.3.1.9 Task 9
				The Role of Municipalities in a Liberalized System
				Achieved Results
			6.3.1.10 Task 10
				Performance Contracting
				Achieved Results
			6.3.1.11 Task 11
				Time-of-Use Pricing and Energy Use for Demand Management Delivery
				Achieved Results
			6.3.1.12 Task 12
				Cooperation on Energy Standards
			6.3.1.13 Task 13
				Demand Response Resources
				Achieved Results
			6.3.1.14 Task 14
				Market Mechanisms for White Certificates Trading
				Achieved Results
			6.3.1.15 Task 15
				Network-Driven DSM
				Achieved Results
			6.3.1.16 Task 16
				Competitive Energy Services (Energy Contracting ESCo Services)
				Achieved Results
			6.3.1.17 Task 17
				Integration of DSM, Energy Efficiency, Distributed Generation, Renewable Energy Sources, and Energy Storages, Phases 1–3
			6.3.1.18 Task 18
				Demand-Side Management and Climate Change
			6.3.1.19 Task 19
				Micro-Demand Response and Energy Saving
				Achieved Results
			6.3.1.20 Task 20
				Branding of Energy Efficiency
				Achieved Results
			6.3.1.21 Task 21
				Standardization of Energy-Saving Calculations
				Achieved Results
			6.3.1.22 Task 22
				Energy Efficiency Portfolio Standards
				Achieved Results
			6.3.1.23 Task 23
				The Role of the Demand Side in Delivering Effective Smart Grids
				Achieved Results
			6.3.1.24 Task 24
				Phase I: Behavior Change in DSM: From Theory to Policies and Practice
			6.3.1.25 Task 24
				Phase II: Behavior Change in DSM: Helping the Behavior Changers
		6.3.2 Current Task
			6.3.2.1 Task 25
				Business Models for a more Effective Market Uptake of DSM Energy Services
	6.4 Conclusion
	References
Chapter 7: Demand Response Application in Generation, Transmission, and Distribution Expansion Planning
	7.1 Introduction
		7.1.1 Motivation
		7.1.2 Literature Review
		7.1.3 Contributions
	7.2 Planning Model
		7.2.1 Demand Response-Based Generation Expansion Model
			7.2.1.1 Investment Cost of Generation Expansion Planning (GEP)
			7.2.1.2 Performance Cost of GEP
			7.2.1.3 Demand Response Cost of GEP
		7.2.2 Demand Response-Based Transmission Expansion Planning
			7.2.2.1 Investment Cost of TEP
			7.2.2.2 Performance Cost of TEP
			7.2.2.3 Demand Response Cost
		7.2.3 Demand Response-Based Distribution Expansion Planning (DEP)
			7.2.3.1 Investment Cost of DEP
			7.2.3.2 Performance Cost of DEP
			7.2.3.3 Demand Response Cost of DEP
	7.3 Multipurpose Comprehensive Learning Bacterial Foraging (MP-CLBF) Algorithm
		7.3.1 Health Assessment
		7.3.2 Non-dominance Process
		7.3.3 Comprehensive Learning Concept
		7.3.4 Inspection of Limitations
	7.4 Fuzzy TOPSIS Concept
	7.5 Flowchart of the Proposed Model
	7.6 Numerical Study
		7.6.1 DR-Based Generation Expansion Planning
		7.6.2 DR-Based Transmission Expansion Planning
		7.6.3 DR-Based Distribution Expansion Planning
	7.7 Conclusion
	References
Chapter 8: Optimal Stochastic Planning of DERs in a Game Theory Framework Considering Demand Response and Pollution Issues
	8.1 Introduction
	8.2 Problem Formulation
	8.3 Scenario Generation
	8.4 Stackelberg Game and Distributed Algorithm
	8.5 Numerical Results
	8.6 Conclusion
	References
Chapter 9: Impact of Demand Response Program on Hybrid Renewable Energy System Planning
	9.1 Introduction
	9.2 The Optimal Planning of HRES
	9.3 Demand Response (DR)
		9.3.1 Different DR Programs
		9.3.2 Variety of the Loads and DR Resources
		9.3.3 The Contribution of DR in HRES Planning
	9.4 Optimization Problem
		9.4.1 Objective Function
		9.4.2 HOMER Optimization Tool
		9.4.3 The Levelized Cost of Energy (LCoE)
	9.5 HRES Components
		9.5.1 Wind Turbine (WT)
		9.5.2 Photovoltaic (PV) Cell System
		9.5.3 Energy Storage System (ESS)
		9.5.4 Convertors
	9.6 Simulation Results
		9.6.1 Case Study
		9.6.2 Results and Discussions
			9.6.2.1 Scenario I: Only RES
			9.6.2.2 Scenario II: RES and Microturbine
			9.6.2.3 Scenario III: RES and Grid Connection
	9.7 Conclusions
	References
Chapter 10: The Application of Demand Response Program on the Dynamic Planning of Energy Storage System Allocation in Distribution Networks
	10.1 Introduction
		10.1.1 Literature Review
		10.1.2 Chapter Organization
	10.2 Problem Formulation
		10.2.1 Objective Function
		10.2.2 ACPF Constraints
		10.2.3 Distributed Generation
		10.2.4 ESS Constraints
		10.2.5 Demand Response Program
	10.3 Case Study
		10.3.1 Necessary Data
	10.4 Results
		10.4.1 Case 1: ACOPF on the MG
		10.4.2 Case 2: Optimal ESS Sizing and Siting Without DRP
		10.4.3 Case 3: Optimal ESS Sizing and Siting Considering DRP
	10.5 Conclusion
	References
Chapter 11: FACTS Device Allocation in the Presence of Demand Response Program
	11.1 Introduction
	11.2 DFACTS Devices
	11.3 Advantages of DFACTS
	11.4 Various Technologies of DFACTS
		11.4.1 DSTATCOM
		11.4.2 DVR
	11.5 The Optimization Method of Location and Size of DFACTS
		11.5.1 Formulation of the DR Program
		11.5.2 Objective Functions
			11.5.2.1 The Loss Index
			11.5.2.2 The Voltage Index
		11.5.3 Multi-Objective Optimization Algorithm
	11.6 Simulation Results
	11.7 Conclusion
	References
Index