Distributed Energy Resources in Local Integrated Energy Systems: Optimal Operation and Planning

Title-page_2021_Distributed-Energy-Resources-in-Local-Integrated-Energy-Syst
	Distributed Energy Resources in Local Integrated Energy Systems: Optimal Operation and Planning
Copyright_2021_Distributed-Energy-Resources-in-Local-Integrated-Energy-Syste
	Copyright
Contents_2021_Distributed-Energy-Resources-in-Local-Integrated-Energy-System
	Contents
List-of-contribut_2021_Distributed-Energy-Resources-in-Local-Integrated-Ener
	List of contributors
Chapter-1---Overview-of-distributed-energy-r_2021_Distributed-Energy-Resourc
	1 Overview of distributed energy resources in the context of local integrated energy systems
		Abbreviations
		1.1 Introduction
		1.2 Distributed energy resources
			1.2.1 Distributed generation based on different energy sources
			1.2.2 Combined production of different energy carriers
			1.2.3 Demand response
			1.2.4 Distributed storage
		1.3 Grid side aspects
			1.3.1 Evolution of the grid connection issues and standards
			1.3.2 Microgrids and local energy networks
			1.3.3 Integration among energy networks
			1.3.4 Analysis and optimization of the grid operation with local energy systems
			1.3.5 Provision of grid services
		1.4 Emergent paradigms and solutions
			1.4.1 Self-consumption and self-sufficiency
			1.4.2 Development of local energy markets
			1.4.3 The energy community paradigm
			1.4.4 Technical, regulatory, and social barriers
		References
Chapter-2---Architectures-and-concepts-_2021_Distributed-Energy-Resources-in
	2 Architectures and concepts for smart decentralised energy systems
		Abbreviations
		2.1 Introduction
		2.2 Why decentralizing the energy system?
			2.2.1 Decentralization in European future scenarios
			2.2.2 Decentralization in European R&D projects
			2.2.3 Pros and cons of decentralization
		2.3 Development of the decentralized architecture
			2.3.1 Level of decentralization
			2.3.2 How to control a decentralized system
		2.4 Grid-secure activations for ancillary services (real-time control)
		2.5 ELECTRA Web-of-Cells control concept
		2.6 Post-primary voltage control
		2.7 Balance restoration control
		2.8 Balance steering control
		2.9 Adaptive frequency containment control
		2.10 Inertia control
		2.11 Decentralizing the DA/ID energy market clearing and grid prequalification of ancillary services
			2.11.1 Decentralization and markets
			2.11.2 Open questions and unresolved issues
		2.12 What is next: evolution of roles and responsibilities necessary for decentralization the European regulatory framework
		2.13 Conclusions
		References
Chapter-3---Modeling-of-multienergy-carriers-d_2021_Distributed-Energy-Resou
	3 Modeling of multienergy carriers dependencies in smart local networks with distributed energy resources
		Abbreviations
		Nomenclature
		3.1 Introduction
			3.1.1 Infrastructure and carrier dependency
			3.1.2 Dependency categories
			3.1.3 Objectives
		3.2 Internal multicarrier dependency in a smart local system
			3.2.1 Components of a local energy systems
			3.2.2 Electricity—gas
			3.2.3 Electricity—hydrogen
			3.2.4 Electricity—gas—heating/cooling
			3.2.5 Electricity—gas—hydrogen—transportation
		3.3 External dependencies in a smart local system
			3.3.1 Multienergy demand
			3.3.2 Information/communication
		3.4 Interdependency modeling
			3.4.1 Coupling model of components and services
			3.4.2 Coupling model of local energy systems
				3.4.2.1 Energy hub method
				3.4.2.2 Energy network method
			3.4.3 Large-scale coupling
				3.4.3.1 Agent-based method
				3.4.3.2 Complex system method
		3.5 A case study on interdependent MES model
		3.6 Conclusions
		References
Chapter-4---Multiobjective-operation-optimizatio_2021_Distributed-Energy-Res
	4 Multiobjective operation optimization of DER for short- and long-run sustainability of local integrated energy systems
		Abbreviations
		Nomenclature
		4.1 Importance of multiobjective operation optimization for short- and long-run sustainability of local integrated energy s...
		4.2 Multiobjective optimization for the operation of a local integrated energy system
			4.2.1 Description of the local integrated energy system under study and mathematical formulation
				4.2.1.1 Modeling of DER in the local integrated energy system
				4.2.1.2 Modeling of energy balances
				4.2.1.3 Economic objective
				4.2.1.4 Exergetic objective
				4.2.1.5 Environmental objective
			4.2.2 Solution methodologies
		4.3 Case study: eco-exergetic operation optimization of a local integrated energy system for a large hotel in Beijing
			4.3.1 Input data
			4.3.2 Case study results
		4.4 Operation optimization of multiple integrated energy systems in a local energy community
			4.4.1 Description of the local energy community under study and mathematical formulation
				4.4.1.1 Modeling of DER in the local energy community
				4.4.1.2 Modeling of energy balances
				4.4.1.3 Objective function
			4.4.2 Case study: eco-environmental optimization of a local energy community in the United States
				4.4.2.1 Input data
				4.4.2.2 Case study results
		4.5 Conclusions and key findings
		References
Chapter-5---Impact-of-neighborhood-energy-tradin_2021_Distributed-Energy-Res
	5 Impact of neighborhood energy trading and renewable energy communities on the operation and planning of distribution networks
		Abbreviations
		Nomenclature
		5.1 Introduction
		5.2 A distributed approach for the day-ahead scheduling of the LEC
			5.2.1 Distributed optimization model formulation
		5.3 Implementation and numerical tests
			5.3.1 Scalability of the distributed approach
			5.3.2 Scenario considering uncertainties on the energy generation and consumption
		5.4 Distribution network planning model considering nonnetwork solutions and neighborhood energy trading
			5.4.1 Concept of risk-managed planning
			5.4.2 Concept of planning with neighborhood energy trading
			5.4.3 Modeling of the uncertainties
			5.4.4 Modeling of nonnetwork solutions
			5.4.5 Modeling of NET
			5.4.6 Costing of NNSs
			5.4.7 Planning problem formulation
			5.4.8 Solution strategy
		5.5 Application of the planning model to case studies and analysis of the results
			5.5.1 Situation A, Case 1: IEEE 13-bus radial feeder
			5.5.2 Situation A, Case 2: A realistic 747-bus radial feeder
			5.5.3 Situation B: IEEE 33-bus radial feeder
		5.6 Conclusions
		Acknowledgment
		References
Chapter-6---Fostering-DER-integratio_2021_Distributed-Energy-Resources-in-Lo
	6 Fostering DER integration in the electricity markets
		Abbreviations
		6.1 Distributed energy resources as providers of flexibility services
			6.1.1 Products and services for voltage and frequency control
				6.1.1.1 Balancing or frequency control
				6.1.1.2 Congestion management
				6.1.1.3 Voltage control
				6.1.1.4 Inertial response
				6.1.1.5 Black start
			6.1.2 Characterization of distributed energy resources as flexibility providers
		6.2 The regulatory framework for the participation of distributed energy resources in different electricity markets
			6.2.1 European regulatory context
				6.2.1.1 Clean energy package for all Europeans
					6.2.1.1.1 DSOs, TSOs, and cooperation between DSOs and TSOs
					6.2.1.1.2 RES integration
					6.2.1.1.3 Active consumers
					6.2.1.1.4 Aggregation
				6.2.1.2 European green deal
				6.2.1.3 Electricity network codes and guidelines
			6.2.2 Current status of DERs as flexibility providers in several European countries
			6.2.3 Barriers to market access of DERs
		6.3 Flexibility needs in power systems
			6.3.1 Current practices in the estimation of flexibility requirements
				6.3.1.1 Frequency control (balancing) reserves
					6.3.1.1.1 Frequency containment reserves
					6.3.1.1.2 Frequency restoration reserves
					6.3.1.1.3 Replacement reserves
				6.3.1.2 Voltage control reserves
			6.3.2 Estimation of future needs of reserves in power systems with high shares of DERs
				6.3.2.1 Frequency control reserves
				6.3.2.2 Voltage control reserves
		6.4 The market value of flexibility in the distribution system
			6.4.1 Flexibility market beneficiaries
			6.4.2 Cost-benefit analysis of market participation of DERs
		6.5 Local energy markets
			6.5.1 Local energy markets
			6.5.2 Roles in a local energy market
				6.5.2.1 Prosumers
				6.5.2.2 Aggregator
				6.5.2.3 Supplier
				6.5.2.4 Balance responsible parties (BRP)
				6.5.2.5 DSO
				6.5.2.6 TSO
			6.5.3 Components of functional local energy markets
		6.6 Conclusions
		References
Chapter-7---Challenges-and-directions-for-Bloc_2021_Distributed-Energy-Resou
	7 Challenges and directions for Blockchain technology applied to Demand Response and Vehicle-to-Grid scenarios
		Abbreviations
		7.1 Introduction
		7.2 The blockchain technology
			7.2.1 What is the blockchain
			7.2.2 Consensus algorithms
			7.2.3 Smart contracts
			7.2.4 State of art of blockchain applications for P2P, DR and V2G
		7.3 The energy blockchain: current trends and possible evolutions
			7.3.1 Peer-to-peer energy exchanges among prosumers
				7.3.1.1 The Brooklyn Microgrid
				7.3.1.2 Other energy trading projects
				7.3.1.3 Grid stabilization and Vehicle to Grid applications
				7.3.1.4 PPA management
				7.3.1.5 The BLORIN project
			7.3.2 Challenges of using the blockchain technology for DR and V2G applications
		7.4 Laboratory setup for energy blockchain testing
			7.4.1 Simulation and emulation of smart prosumers
			7.4.2 The smart contracts in the BLORIN project for DR and V2G implementation
				7.4.2.1 Future applications of the energy blockchain: the blockchain for energy communities
		7.5 Conclusions
		Acknowledgment
		References
Chapter-8---Optimal-management-of-energy-stor_2021_Distributed-Energy-Resour
	8 Optimal management of energy storage systems integrated in nanogrids for virtual “nonsumer” community
		Abbreviations
		Nomenclature
		8.1 Introduction
		8.2 Energy storage systems as distributed flexibility
			8.2.1 The flexibility in a distribution grid
			8.2.2 The main energy storage system technologies
				8.2.2.1 Li-Ion battery [5]
				8.2.2.2 Supercapacitor [6,7]
				8.2.2.3 PEM based power-to-hydrogen [8–11]
			8.2.3 The flexibility services provided by energy storage systems
		8.3 The energy storage system in a nanogrid: the configuration
			8.3.1 The nanogrid as enabling technology
			8.3.2 Nanogrid configuration schemes with integrated energy storage systems
			8.3.3 Modeling and control
				8.3.3.1 Modeling
				8.3.3.2 PEI DC/AC converter model
				8.3.3.3 MS DC/DC converter model
				8.3.3.4 Li-Ion battery model
				8.3.3.5 Li-Ion DC/DC converter
				8.3.3.6 Supercapacitor model
				8.3.3.7 SC DC/DC converter
				8.3.3.8 Power to hydrogen model
				8.3.3.9 Power-to-hydrogen (P-to-H) DC/DC converter model
				8.3.3.10 Control
				8.3.3.11 Master
				8.3.3.12 Slave
		8.4 Optimal energy management for virtual nonsumers nanogrid community
			8.4.1 Virtual nonsumers community review
			8.4.2 Mathematical model
			8.4.3 Solution algorithms
		8.5 The energy storage systems for grid ancillary service
			8.5.1 The ancillary services market
			8.5.2 The potential benefits of using energy storage to provide ancillary services
				8.5.2.1 Frequency regulation
				8.5.2.2 Spinning reserve reduction
				8.5.2.3 Inertia emulation
				8.5.2.4 Voltage regulation
				8.5.2.5 Black start
		8.6 Case study
			8.6.1 Problem formulation
			8.6.2 Simulation setup
			8.6.3 Simulation results and discussions
		8.7 Conclusions
		References
Chapter-9---Demand-response-role-for-enha_2021_Distributed-Energy-Resources-
	9 Demand response role for enhancing the flexibility of local energy systems
		Abbreviations
		Nomenclature
		9.1 Introduction
		9.2 Demand response programs for local energy systems
			9.2.1 Comprehensive assessment of DR programs
				9.2.1.1 Price-based Demand Response Programs
				9.2.1.2 Incentive-based Demand Response Programs
		9.3 Flexibility assessment of local energy systems in the presence of energy storage systems and DR programs
		9.4 Energy management framework for DER integrated distribution networks
		9.5 Simulation results
		9.6 Conclusion remarks
		Acknowledgment
		References
Chapter-10---The-integration-of-electric-vehi_2021_Distributed-Energy-Resour
	10 The integration of electric vehicles in smart distribution grids with other distributed resources
		Abbreviations
		Nomenclature
		10.1 Introduction to electric vehicles and charging infrastructures
			10.1.1 Characteristics of electric vehicles
				10.1.1.1 Series PHEV
				10.1.1.2 Parallel PHEV
				10.1.1.3 Series-parallel PHEV
			10.1.2 Low power AC charging infrastructures
				10.1.2.1 Mode 1
				10.1.2.2 Mode 2
				10.1.2.3 Mode 3
			10.1.3 High power DC charging infrastructures
		10.2 Integration of electric vehicles in smart distribution grids
			10.2.1 Impact of the charging infrastructures on distribution grids
			10.2.2 Planning of the charging infrastructures
		10.3 Vehicle-to-Grid
			10.3.1 The use of EVs for grid support
				10.3.1.1 Vehicle-to-Home
				10.3.1.2 Vehicle-to-Vehicle
				10.3.1.3 Vehicle-to-Grid
			10.3.2 V2G functions for frequency regulation
			10.3.3 Synergies between electric vehicles and renewable energy sources
		10.4 Conclusions
		References
Chapter-11---Assessing-renewables-uncertain_2021_Distributed-Energy-Resource
	11 Assessing renewables uncertainties in the short-term (day-ahead) scheduling of DER
		Abbreviations
		Nomenclature
		11.1 Introduction
			11.1.1 Present and future energy landscape
			11.1.2 Future system grid projection
		11.2 RES uncertainties description and assessment
			11.2.1 Impact of RES on power system grids
				11.2.1.1 Impact of variability in the secure and efficient operation of the power system
				11.2.1.2 Impact on overall inertia
				11.2.1.3 Impact on voltage regulation
				11.2.1.4 Other impacts of RES on the system
			11.2.2 Benefits of DER on power system grids
		11.3 Uncertainties affecting system resilience
			11.3.1 Metrics for assessing distribution system resilience
				11.3.1.1 Signs of vulnerability
				11.3.1.2 Total restoration cost
			11.3.2 Resilience trapezoid
			11.3.3 ΦΛΕΠ Resilience quantitative framework
			11.3.4 Flexibility and resilience matrix
			11.3.5 Increasing resilience of a high RES system with flexible resources
			11.3.6 Operational measurements
		11.4 Assessing renewables uncertainties in the short-term (day-ahead) scheduling of DER
			11.4.1 Methodology
			11.4.2 Grid system under investigation
			11.4.3 DER operational strategies
			11.4.4 Scenario under study
			11.4.5 Simulation case results
		11.5 Discussion and conclusions
		References
Chapter-12---Load-forecasting-in-the_2021_Distributed-Energy-Resources-in-Lo
	12 Load forecasting in the short-term scheduling of DERs
		Abbreviations
		Nomenclature
		12.1 Introduction
		12.2 New trends in load forecasting
			12.2.1 Introduction of load forecasting for individual energy customers
			12.2.2 Dynamic probabilistic household load forecasting
			12.2.3 Consumption behavior-driven household load forecasting
		12.3 Trans-active energy systems with DERs
			12.3.1 Distribution market mechanism for DERs with zero marginal costs
			12.3.2 Decentralized market mechanism for DER transactions
		12.4 Short-term scheduling of DERs in demand side
			12.4.1 Short-term scheduling of DERs in buildings
			12.4.2 Short-term scheduling of DERs in microgrids
				12.4.2.1 Centralized and distributed DER scheduling in microgrids
				12.4.2.2 Resilient DER scheduling in microgrids
			12.4.3 Short-term scheduling of DERs in VPPs
		12.5 Conclusions and future thoughts
		References
Chapter-13---Conclusions-and-key-findings-of-optim_2021_Distributed-Energy-R
	13 Conclusions and key findings of optimal operation and planning of distributed energy resources in the context of local i...
Index_2021_Distributed-Energy-Resources-in-Local-Integrated-Energy-Systems
	Index