Decentralized Frameworks for Future Power Systems: Operation, Planning and Control Perspectives

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Decentralized Frameworks for Future Power Systems; Operation, Planning, and Control Perspectives
Copyright
Contents
Contributors
Preface
Chapter 1: Energy transformation and decentralization in future power systems
1. Introduction
2. Energy transformation
3. Decentralized decision-making
3.1. Concepts of decentralized decision-making
3.2. Application of DDM in engineering
4. Implementation of DDM in future power systems
4.1. DDM based on MAS
4.2. Big data and decentralized data analytics
5. Application of DDM in future power system planning
5.1. Decentralized network expansion planning
5.2. Decentralized energy planning
6. Power system operation issues based on DDM
6.1. DER energy management
6.2. Decentralized demand side management
6.3. Decentralized optimal power flow
6.4. Decentralized economic dispatch
6.5. Decentralized unit commitment
7. Conclusions
References
Chapter 2: 5D Giga Trends in future power systems
1. Introduction
2. What are the 5D Giga Trends?
2.1. Decentralization
2.2. Deregulation
2.3. Digitalization
2.4. Decarbonization
2.5. Democratization
3. The existing power systems issues
4. The impacts of 5D Giga Trends on future power systems
4.1. Decentralization in power systems
4.2. Deregulation in power systems
4.3. Digitalization in power systems
4.4. Decarbonization in power systems
4.5. Democratization in power systems
5. Future power systems affected by 5D Giga Trends
6. Opportunities, challenges, and new issues of the future power systems under 5D Giga Trends
6.1. Opportunities and challenges of decentralization Giga Trend in power systems
6.2. Opportunities and challenges of deregulation Giga Trend in power systems
6.3. Opportunities and challenges of digitalization Giga Trend in power systems
6.4. Opportunities and challenges of decarbonization Giga Trend in power systems
6.5. Opportunities and challenges of democratization Giga Trend in power systems
7. Life cycle of 5D Giga Trends
References
Chapter 3: Grid transformation driven by high uptake of distributed energy resources-An Australian case study
1. Introduction
2. Energy transition
3. Grid transformation
4. Centralized versus decentralized
5. Distribution system operator
6. Grid transformation in Australia
References
Chapter 4: Multidimensional method for assessing nonwires alternatives within distribution system planning
1. Introduction
2. Nonwires alternatives
3. Multidimensional planning
4. Case study
4.1. Study results
5. Analysis based on the DBT
6. Conclusions
References
Chapter 5: Green approaches in future power systems
1. Introduction
2. Green transformation
3. Energy issues
3.1. Finite resources
3.2. Environmental concerns
3.3. Energy security
4. Green resources
4.1. Clean power plants
4.2. Energy efficiency
4.3. Renewables
4.3.1. Sources
4.3.2. Energy conversion
4.3.3. Trends
4.3.4. Hydro resources
4.3.5. Solar resources
4.3.6. Wind resources
4.3.7. Marine resources
4.3.8. Biological resources
4.3.9. Geothermal resources
5. Decentralization viewpoint
5.1. Distributed generation
5.2. Energy storages
5.3. Demand response
5.4. Role of power electronics
5.5. Grid integration issues
5.5.1. Distribution grid issues
5.5.2. Transmission grid issues
5.6. Microgrids
6. Conclusions
References
Chapter 6: Blockchain for future renewable energy
1. Introduction
2. Challenges in renewable energy with decentralized frameworks for operation, management, and business
2.1. Systemic
2.2. Quality
2.3. Technical
2.4. Economic
2.5. Stability
2.6. Imbalance
3. Blockchain technology
4. Potential application of blockchain for future renewable energy
4.1. Electric vehicle
4.2. Decentralized (peer-to-peer) energy transaction
4.3. Certification and trading of carbon emissions
4.4. Physical information security
4.5. Energy transmission
4.6. Power-to-X
4.7. Internet of Energy
5. Implementation of blockchain for renewable energy
5.1. Blockchain system architecture
5.2. Data feed to the blockchain from the power grid
5.3. Consensus selection
5.4. Blockchain security and maintenance
5.5. Legal and regulatory
6. Conclusions
Acknowledgment
References
Chapter 7: Electricity market issues in future power systems
1. Introduction
2. Multiarea market
2.1. Multiarea market without coordinator entity
2.2. Central model for multiarea market
2.3. Decentralized market by the OCD method
2.4. Decentralized market by the LR method
2.5. Decentralized market by ALD (with APP and ADM methods)
2.6. Experience of US markets by implementing TO and CTS methods
2.7. Comparison of the decomposition-based methods
3. Local electricity markets for smart grids
3.1. P2P markets
3.1.1. Full P2P market
3.1.2. Community-based market
3.1.3. Hybrid P2P market
3.2. Use of the P2P concept in multiarea markets
3.3. Use of blockchains and edge computing in P2P market design
3.4. General structure of blockchain technology
3.5. Application of blockchains in the implementation of energy markets
3.5.1. First scheme
3.5.2. Second scheme
References
Chapter 8: Role of game theory in future decentralized energy frameworks
1. Introduction
2. What is the game theory model?
2.1. Nash equilibrium
3. Types of games
4. Types of games based on participants involvement
4.1. Cooperative game
4.1.1. Uses of cooperative games in decentralized energy framework
4.2. Noncooperative game
4.2.1. Uses of noncooperative games in decentralized energy framework
4.3. Evolutionary game
4.3.1. Uses of evolutionary games in decentralized energy framework
5. Conclusions
References
Chapter 9: Toward customer-centric power grid: Residential EV charging simulator for smart homes
1. Introduction
2. Literature review
2.1. Demand response
2.1.1. Flexibility management
2.1.2. Smart homes
2.1.3. Meters and smart devices
2.1.4. Third-party control and data collection
2.2. The role of operators, governments, and regulators
2.2.1. Incentives
Financial
Information
Energy services
2.2.2. Regulations and policies
2.3. Peer-to-peer (P2P) trading
3. Smart home demand response simulation
3.1. Nord Pool spot prices
3.2. EV charging simulator
3.2.1. Electricity profiles
3.2.2. Simulation results
3.2.3. Further improvements
4. Conclusions
References
Glossary
Chapter 10: Equivalent dynamic modeling of active distribution networks for TSO-DSO interactions
1. Introduction
2. Unconstrained gray-box linear modeling method
2.1. Linear equivalent dynamic model
2.2. Identification procedure
3. Operational constrained gray-box nonlinear modeling method
3.1. Nonlinear equivalent dynamic model
3.1.1. Model equations
3.1.2. Modeling and operating constraints
3.1.3. EDM
3.2. Identification procedure
3.2.1. Model definition
3.2.2. Model estimation
3.3. Extension to microgrids
4. Simulation and experimental results
4.1. Linear EDM simulation results
4.2. Nonlinear constrained EDM simulation results
4.2.1. Test network model
4.2.2. Scenarios
4.2.3. Events
4.2.4. Simulation procedure
4.2.5. Models identification
4.2.6. Results
4.3. Microgrid EDM experimental results
4.3.1. Test facility
4.3.2. Experimental scenarios
4.3.3. Identification and validation results
4.3.4. Cross-validation results
5. Conclusions
References
Chapter 11: Transactive control for residential demand-side management: Lessons learned from noncooperative game theory
1. Introduction
2. Literature review
3. Noncooperative games for the coordination of residential loads
3.1. Load modeling
3.2. Total cost function
3.2.1. Quadratic cost function
3.2.2. Peak pricing function
3.3. Billing functions for defining consumers utilities
3.3.1. Proportional to consumption
3.3.2. Per time slot
3.4. Noncooperative scheduling game
4. Game aspects
4.1. Existence of Nash equilibria
4.1.1. Game with PTC billing to schedule energy invariant loads
4.1.2. Game with PTC billing to schedule energy variant loads
4.1.3. Game with PTS billing and quadratic total cost
4.2. Solution algorithm to solve potential games
4.3. Multiplicity of Nash equilibria
4.4. Fairness of different billings
4.5. Strategy proof of different billings
4.5.1. Proportional to consumption
4.5.2. Per time slot
4.6. Price of anarchy
5. An application of noncooperative games to coordinate thermal loads
5.1. Existence of Nash equilibria
5.2. Fairness of the PTS and PTC
5.3. Strategy proof of the billing mechanisms
5.3.1. Per time slot
5.3.2. Proportional to consumption
6. Conclusions
References
Chapter 12: Distributed dynamic algorithm for energy management in smart grids
1. Introduction
1.1. Review of literature
1.2. Contribution
2. Preliminaries
2.1. Distributed consensus algorithms
2.1.1. Graph theory
2.1.2. Dynamic average consensus
2.2. Convex optimization
2.2.1. Constrained optimization
2.2.2. Convex functions
2.2.3. Optimality conditions: KKT conditions
2.3. Distributed optimization
2.3.1. Primal and dual decomposition
2.3.2. Distributed gradient algorithm
2.3.3. Algorithm stability: Discrete dynamic systems
3. Application of distributed algorithms in economic dispatch problem
3.1. Economic dispatch problem
3.2. Dual decomposition of economic dispatch
3.3. Decentralized algorithm for economic dispatch
3.4. Distributed economic dispatch
3.5. Distributed algorithm for economic dispatch
4. Numerical stability and convergence
5. Results and discussions
5.1. Simulation setup
5.2. Algorithm performance
6. Conclusions
References
Chapter 13: Decentralized power exchange control methods among subsystems in future power network
1. Introduction
2. Classification of linkage topologies for AC and DC subsystems in future power networks
2.1. Linkage of subsystems using stand-alone BLPC
2.2. Linkage of subsystems using multiple BLPCs
2.3. Linkage of subsystems using SSTs
2.4. Linkage of subsystems using ERO
2.5. Linkage of subsystems using FACTS devices
2.6. Comparison of linkage strategies
3. Power exchange control strategies among subsystems
4. Decentralized control of multiple BLPCs for interlinking subsystems
4.1. Droop-based control of multiple BLPCs
4.2. Intelligent control of multiple BLPCs
4.3. Robust, observer-based, and optimal control of multiple BLPCs
4.4. Active power sharing strategies for control of multiple BLPCs
4.5. Instantaneous power theory-based control of multiple BLPCs
5. Conclusions
References
Chapter 14: Peer-to-peer management of energy systems
1. Introduction
1.1. Current trends and impacts of P2P power markets
2. Modeling the P2P energy management scheme in a local energy system with a multiagent structure
3. Extending the developed P2P power market in local energy systems
4. Extending the developed P2P power market to address the congestion issue in the energy grid
5. Further operational points associated with modeling the P2P energy management framework
6. Conclusions
References
Chapter 15: False data injection attacks on distributed demand response: Im paying less: A targeted false data injection ...
1. Literature review
2. System model
2.1. Optimization
3. Attack model
3.1. Attack motivation
3.2. Preliminaries
3.3. Attack-free scenario
3.4. Attack scenario
4. Experiment
4.1. Dataset
4.2. Process
5. Results
6. Discussion
7. Conclusions
References
Chapter 16: Toward building decentralized resilience frameworks for future power grids
1. Introduction
2. Power grid modeling
3. Problem formulation
4. Part one: Incorporating smart devices
5. Part two: The proposed decentralized resiliency framework
5.1. Phase one: The resiliency assessment
5.1.1. Distortion-based resiliency assessment
5.1.2. Clustering-coefficient-based resiliency assessment
5.2. Phase two: The resiliency enhancement
6. Experimental results
6.1. Set 1: Distortion-based experimental results
6.1.1. Validation
6.1.2. Effectiveness
6.2. Set 2: Clustering coefficient-based experimental results
6.2.1. Validation
6.2.2. Effectiveness
6.3. Discussion
7. Conclusions
References
Chapter 17: Modeling and evaluation of power system vulnerability against the hurricane
1. Introduction
2. Temporal and spatial dynamics of hurricanes
3. Hurricane velocity anticipation based on the chaos theory and LS-SVM
4. Vulnerability of lines and poles against the hurricane
5. Scheduling of a network in a normal/hurricane condition
6. Test system and main assumptions
7. Results and analysis of the proposed model
8. Conclusions
References
Index
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