Large Scale Grid Integration of Renewable Energy Sources: Solutions and technologies (Energy Engineering)

Cover
’Contents
’About the editor
’1 The power grid as part of a sustainable energy’system
	’1.1 Introduction
	’1.2 Status and trends in the voluntary use of renewable’energy
		1.2.1 The world&x00027;s leading companies are using 100% renewable energies
		1.2.2 The islands leading toward 100% renewable
	1.3 The 100% renewable energy system
	’1.4 Flexibility
	’1.5 The role of the electricity transport
	’1.6 The role of energy storage
	1.7 Reliability in the 100% renewable energy system
		’1.7.1 The view of the electricity consumer
		’1.7.2 The view of the electricity producer
		’1.7.3 The view of the system operator
	1.8 The transition stage: toward 100% renewable energy
	’1.9 Renewable energy integration issues
	’1.10 The prosumer role
	’1.11 Key technologies
	’References
’2 Recent developments and future challenges for large integration of renewable energy sources
	’2.1 General overview
	’2.2 Ancillary services in RES: comparisons among different countries
		’2.2.1 Active power reserves and frequency control
		’2.2.2 Reactive power control/voltage control
	’2.3 RES under disturbances: fault ride-through capability
	’2.4 Wind energy plants. End of useful life
		’2.4.1 Europe case
		’2.4.2 Repowering case: multifactorial analysis
	’2.5 Conclusions
	’References
’3 Wind energy forecasting methods
	’3.1 Wind forecasting in grid and market operations
		’3.1.1 Uncertainty in wind energy production
		’3.1.2 Effects of the wind forecasts uncertainty in the power’system
		’3.1.3 Wind uncertainty in market operations
	’3.2 Wind power forecasting systems
		’3.2.1 Wind control centers
		’3.2.2 Description of wind power forecasting systems
		’3.2.3 Wind power forecasting system results: representation and validation
	’3.3 Physical approaches for wind forecasting
		’3.3.1 Numerical weather prediction
			’3.3.1.1 Global models
			’3.3.1.2 Limited area models
			’3.3.1.3 Competitive ensemble forecasting
		’3.3.2 Physical approaches focused on wind forecasting
	’3.4 Statistical approaches for wind forecasting
	’3.5 Enhancing predictions with nowcasting
	’References
’4 Solutions and active measures for wind power’integration
	’4.1 Introduction
	’4.2 Energy policy
	’4.3 Technology overview and prospective changes in the power grid
		’4.3.1 Overview of wind power plant technologies
		’4.3.2 Impact of electric transportation and electric vehicles
		’4.3.3 Impact on consumers
			’4.3.3.1 Demand side response
			’4.3.3.2 Aggregated prosumers
		’4.3.4 Impact of grid operators and generators
	’4.4 Technical and economic impacts of large-scale wind’integration
		’4.4.1 Technical challenges
		’4.4.2 Impacts on existing power plant economics and electricity market
		’4.4.3 System frequency regulation and increasing wind capacity impacts on regulating reserves
	’4.5 Measures to support large-scale wind integration
		’4.5.1 Aggregated thermal storages for balancing of power generation forecast errors
		’4.5.2 Pumped hydro energy storage for balancing of power generation forecast errors
		’4.5.3 Demand side management for providing balancing’power
			’4.5.3.1 EV charging as alternative storage for renewable’energy
			’4.5.3.2 Industrial consumers as power generation balancing entity
	’4.6 Conclusion
	’References
’5 Grid integration of large-scale PV plants: dealing with power fluctuations
	’5.1 Introduction
	’5.2 The photovoltaic observatory
	’5.3 Irradiance and power output fluctuations in large PV’plants
		’5.3.1 At a PV plant level
			’5.3.1.1 Irradiance fluctuations
			’5.3.1.2 Power fluctuations
		’5.3.2 Power fluctuations at a PV plant group level
	’5.4 Simulating power fluctuations at PV plants
		’5.4.1 PV plant model
		’5.4.2 Model of a group of PV plants
	’5.5 Smoothing power output fluctuations by using energy storage systems
		’5.5.1 The worst fluctuation model
		’5.5.2 Conventional ramp-rate control
		’5.5.3 Power ramp-rate control based on the PV power plant’model
		’5.5.4 Power ramp-rate control based on the PV power plant model and PV power forecasting
	’5.6 The potential of forecasting to attenuate PV power’fluctuations
	’References
’6 Towards the extensive use of renewable energy’resources: needs, conditions and enabling’technologies
	’6.1 Introduction
	’6.2 Measurement and assessment of the renewable’generation
		’6.2.1 Use of a PV monitoring system on time in a grid-connected PV park
		’6.2.2 Temporal requirements in the measurement of parameters to control the power quality of the generated signal
			’6.2.2.1 Temporary measurement intervals
			’6.2.2.2 Time-stamping
	’6.3 The interconnection between renewable generation and the electricity grid
		’6.3.1 Temporary requirements for protections
			’6.3.1.1 Voltage operational limits
			’6.3.1.2 Frequency operational limits
			’6.3.1.3 Islanding
			’6.3.1.4 Response to recovery of normal power grid conditions
		’6.3.2 The active management of the interconnection
		’6.3.3 Solutions for the interconnection with electrical grid: Smart Inverter
	’6.4 Wide area network: data model with the IEC 61850’standard for smart grid
		’6.4.1 Integration of renewables in wide area networks
		’6.4.2 Detection of faults in cascade and fall of the network’(blackout)
		’6.4.3 Data model with the IEC 61850’standard
		’6.4.4 IEC 61850’modelling for DER applications
		’6.4.5 Stability with synchrophasors and synchronisation with PTP
		’6.4.6 Justification of the distributed synchronism through the IEEE 1588 v2 protocol
	’6.5 Conclusions
	’6.6 Appendix technology update 2023
		’6.6.1 Integration of renewables in wide area networks
			’6.6.1.1 Temporal requirements in the measurement of energy parameters
	’References
’7 Distributed energy resources integration and’demand response: the role of stochastic demand modelling
	’7.1 Introduction
	’7.2 Overview of modelling techniques for energy demand prediction
		’7.2.1 Top-down models
		’7.2.2 Bottom-up models
		’7.2.3 Comparison
	’7.3 Time-of-use-based bottom-up models
		7.3.1 Occupancy and consumers’ behaviour
			’7.3.1.1 Model basics
			’7.3.1.2 Input parameters
			’7.3.1.3 Simulation algorithm
		’7.3.2 Lighting system consumption
			’7.3.2.1 Model basics
			’7.3.2.2 Input data
			’7.3.2.3 Simulation algorithm
		’7.3.3 Consumption of general appliances
			’7.3.3.1 Model basics
			’7.3.3.2 Input data
			’7.3.3.3 Simulation algorithm
		’7.3.4 Heating and cooling consumption
			’7.3.4.1 Model basics
			’7.3.4.2 Input data
			’7.3.4.3 Simulation algorithm
		’7.3.5 Remarks on the model
	’7.4 Applications of bottom-up stochastic models
		’7.4.1 Demand prediction
		’7.4.2 Energy policies and demand response strategies’assessment
		’7.4.3 Integration of distributed resources
	’7.5 Conclusion
	’References
’8 DC distribution systems and microgrids
	’8.1 Introduction
	’8.2 DC microgrid system overview
		’8.2.1 Single-bus topologies
		’8.2.2 Multi-bus topologies
		’8.2.3 Reconfigurable topologies
		’8.2.4 Hybrid AC/DC MGs
	’8.3 Operation and control of DC microgrids
		’8.3.1 Local control functionalities
		’8.3.2 Coordinated control
			’8.3.2.1 Centralized coordination
			’8.3.2.2 Decentralized coordination
			’8.3.2.3 Distributed coordination
	’8.4 DC microgrid system protection
		’8.4.1 Types of faults
		’8.4.2 Grounding
		’8.4.3 Protective devices
		’8.4.4 Design of protection systems
	’8.5 Application of DC microgrids to future smart grids
		’8.5.1 High-efficiency households
		’8.5.2 Renewable energy parks
		’8.5.3 Hybrid ESS
		’8.5.4 EV fast charging stations
	’8.6 Conclusions
	’References
’9 Distributed micro-storage systems at residential level in smart communities with high penetration of photovoltaic generation
	’9.1 Overview of micro-storage technologies
		’9.1.1 Conventional batteries
			’9.1.1.1 Lead-acid batteries
			’9.1.1.2 Lithium-ion (Li-ion) batteries
			9.1.1.3 Nickel–cadmium (NiCd) and nickel metal hydride (NiMH) batteries
			’9.1.1.4 Sodium-sulfur (NaS) and sodium nickel chloride (ZEBRA) batteries
			’9.1.1.5 Metal-air batteries
		’9.1.2 Flow batteries
			’9.1.2.1 Vanadium redox flow battery (VRFB)
			9.1.2.2 Zinc–bromine (ZnBr) flow battery
		’9.1.3 Supercapacitors
		’9.1.4 Superconducting magnetic energy storage (SMES)
		’9.1.5 Flywheels
		’9.1.6 Comparison of characteristics of micro-storage system technologies
	’9.2 Topologies for the bidirectional electronic converter
		’9.2.1 Standard topologies
			’9.2.1.1 Single stage
			’9.2.1.2 Double stage
		’9.2.2 Multilevel topologies
		’9.2.3 Multiport topologies
	’9.3 Control strategies for the ESMS of the storage device
		’9.3.1 Active power control strategies
		’9.3.2 Reactive power control strategies
		’9.3.3 Power quality and imbalance reduction control strategies
	’9.4 Power interfaces
		’9.4.1 Analysis of typical solution
		’9.4.2 An improved solution based on cooperative converters
	’9.5 Conclusions
	’References
’10 Hydrogen energy systems
	’10.1 Introduction
		’10.1.1 Current issues and the use of hydrogen as an energy carrier to address them
		’10.1.2 Brief history of art
		’10.1.3 Hydrogen types
	’10.2 Hydrogen technologies
		’10.2.1 Production (electrolysis and types)
		’10.2.2 Hydrogen storage (type)
			’10.2.2.1 Electric generation (fuel cells)
	’10.3 Hydrogen applications
		’10.3.1 Stationary hydrogen applications
			’10.3.1.1 Current state of technology
			’10.3.1.2 Benefits of using FC M-CHP systems
		’10.3.2 Mobility hydrogen applications
			’10.3.2.1 Hydrogen vehicles
			’10.3.2.2 Maritime transportation
			’10.3.2.3 Railway transportation
			’10.3.2.4 Hydrogen in aviation
	’10.4 Hydrogen as an energy storage system
		’10.4.1 Electrical microgrid
		’10.4.2 Hydrogen integration in distributed systems
		’10.4.3 Optimization control system for hybrid energy systems in a microgrid
	’10.5 Conclusion
	’References
Index
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