Renewable Energy Systems: Modelling, Optimization and Control

Length: 732 pages
Edition: 1
Language: English
Publisher: Academic Press
Publication Date: 2021-09-27
ISBN-10: 0128200049
ISBN-13: 9780128200049
Sales Rank: #0 (See Top 100 Books)
Renewable Energy Systems: Modelling, Optimization and Control aims to cross-pollinate recent advances in the study of renewable energy control systems by bringing together diverse scientific breakthroughs on the modeling, control and optimization of renewable energy systems by leading researchers. The book brings together the most comprehensive collection of modeling, control theorems and optimization techniques to help solve many scientific issues for researchers in renewable energy and control engineering. Many multidisciplinary applications are discussed, including new fundamentals, modeling, analysis, design, realization and experimental results. The book also covers new circuits and systems to help researchers solve many nonlinear problems.
This book fills the gaps between different interdisciplinary applications, ranging from mathematical concepts, modeling, and analysis, up to the realization and experimental work.
Title-page_2021_Renewable-Energy-Systems
	Renewable Energy Systems
Copyright_2021_Renewable-Energy-Systems
	Copyright
Contents_2021_Renewable-Energy-Systems
	Contents
List-of-contributors_2021_Renewable-Energy-Systems
	List of contributors
Preface_2021_Renewable-Energy-Systems
	Preface
		About the book
		Objectives of the book
		Organization of the book
		Book features
		Audience
		Acknowledgments
Chapter-1---Efficiency-maximization-of-wind-turbines-usin_2021_Renewable-Ene
	1 Efficiency maximization of wind turbines using data-driven Model-Free Adaptive Control
		1.1 Introduction
		1.2 Problem statement
			1.2.1 The problem of optimal power extraction for wind turbines
			1.2.2 Data-driven Model-Free Adaptive Control
		1.3 Control design
		1.4 Simulation study using FAST
		1.5 Conclusions
		References
Chapter-2---Advanced-control-design-based-on-sliding-modes-tech_2021_Renewab
	2 Advanced control design based on sliding modes technique for power extraction maximization in variable speed wind turbine*
		2.1 Introduction
			2.1.1 A description of wind turbines
			2.1.2 Wind turbines structures and operation conditions
				2.1.2.1 Operation regions
			2.1.3 Problem statement
			2.1.4 The main contribution
			2.1.5 Chapter structure
		2.2 Modeling variable speed wind turbine
			2.2.1 Aerodynamic subsystem of wind turbine
			2.2.2 Mechanical subsystem of wind turbine
			2.2.3 Electrical subsystem of wind turbine
			2.2.4 Control objectives for variable speed wind turbine
		2.3 Sliding mode control design
			2.3.1 Super twisting algorithm
			2.3.2 Variable speed wind turbine controller design
		2.4 Simulation results
			2.4.1 Test conditions
			2.4.2 Discussion of the simulation results
		2.5 Conclusion and future directions
		Acknowledgments
		Nomenclature
		References
		Appendix
Chapter-3---Generic-modeling-and-control-of-wind-turbin_2021_Renewable-Energ
	3 Generic modeling and control of wind turbines following IEC 61400-27-1
		3.1 Introduction
		3.2 Literature review
		3.3 Modeling, simulation and validation of the Type 3 WT model defined by Standard IEC 61400-27-1
			3.3.1 IEC Type 3 WT model
			3.3.2 Modeling of the generic Type 3 WT model
				3.3.2.1 Aerodynamic model
				3.3.2.2 Pitch control model
				3.3.2.3 Mechanical model
				3.3.2.4 P control model
				3.3.2.5 Q control model
				3.3.2.6 Q limitation model
				3.3.2.7 Current limitation model
				3.3.2.8 Generator system
			3.3.3 Simulation and validation of the generic Type 3 WT model
		3.4 Model validation results
			3.4.1 Full load validation test cases
			3.4.2 Partial load validation test cases
		3.5 Conclusions
		References
Chapter-4---Development-of-a-nonlinear-backstepping-approach_2021_Renewable-
	4 Development of a nonlinear backstepping approach of grid-connected permanent magnet synchronous generator wind farm structure
		4.1 Introduction
		4.2 Related work
		4.3 Mathematical model of wind turbine generator
			4.3.1 The wind turbine system
			4.3.2 PMSG modeling
		4.4 Control schemes of wind farm
			4.4.1 MPPT technique
			4.4.2 Nonlinear control of WFS
				4.4.2.1 Generator side converters control
				4.4.2.2 Pitch angle control
				4.4.2.3 Control of inverter
			4.4.3 Vector control technique of WFS
				4.4.3.1 Regulator of PMSG side
					Constitution of current regulators
					Velocity regulation
				4.4.3.2 Control technique for the inverter
					Reactive and active power regulation
					dc-Link control
		4.5 Simulation result analysis
		4.6 Conclusions
		Appendix
		References
		Further reading
Chapter-5---Model-predictive-control-based-energy-management_2021_Renewable-
	5 Model predictive control-based energy management strategy for grid-connected residential photovoltaic–wind–battery system
		5.1 Introduction
			5.1.1 Motivations
			5.1.2 Contributions
			5.1.3 Organization of the chapter
		5.2 Related works
		5.3 The architecture of original grid-tied PV–WT–battery and optimal control strategy
			5.3.1 Subsystems
			5.3.2 PV generator
			5.3.3 Wind generator
			5.3.4 Battery storage system
			5.3.5 Utility grid and electricity tariff
		5.4 Energy management strategy and the model of the open-loop control
			5.4.1 Energy management strategy
			5.4.2 Objective function
			5.4.3 Constraints and power flow limits
				5.4.3.1 Power balance
				5.4.3.2 Constraints
				5.4.3.3 Limitations of power flow
			5.4.4 The applied algorithm
		5.5 Model predictive control for the PV/wind turbine/battery system
			5.5.1 Multiinput–multioutput linear state-space model of the designed system
			5.5.2 Design of the model predictive control
				5.5.2.1 The objective function of the MPC approach and constraints
			5.5.3 Pseudo code of the model predictive control approach
		5.6 Results and discussion
			5.6.1 Case study description
			5.6.2 Simulation results and discussion
			5.6.3 Economic analysis
		5.7 Conclusion
		References
Chapter-6---Efficient-maximum-power-point-tracking-in-fue_2021_Renewable-Ene
	6 Efficient maximum power point tracking in fuel cell using the fractional-order PID controller
		6.1 Introduction
		6.2 PEMFC system description
			6.2.1 Working principle
			6.2.2 Mathematical model: PCM
			6.2.3 Characteristic power versus current plots of the used PEMFC
		6.3 MPPT control configuration
			6.3.1 MPPT controller
			6.3.2 PWM generator
			6.3.3 DC/DC converter
			6.3.4 Load
		6.4 Design and implementation of FOPID MPPT control technique
		6.5 Controller tuning using GWO
		6.6 MPPT performance analysis
			6.6.1 Case A: performance assessment: variation in λ
				6.6.1.1 Transient analysis
				6.6.1.2 Steady-state analysis
			6.6.2 Case B: performance assessment: variation in T
				6.6.2.1 Transient analysis
				6.6.2.2 Steady-state analysis
		6.7 Conclusion
		References
Chapter-7---Robust-adaptive-nonlinear-controller-of-wind-ene_2021_Renewable-
	7 Robust adaptive nonlinear controller of wind energy conversion system based on permanent magnet synchronous generator
		7.1 Introduction
		7.2 Speed-reference optimization: power to optimal speed
			7.2.1 Power characteristic of the turbine P(Ω,vw)
			7.2.2 Optimal power characteristic of the turbine (Popt,Ω)
		7.3 Modeling of the association “permanent magnet synchronous generator–AC/DC/AC converter”
			7.3.1 Modeling of the combination “permanent magnet synchronous generator–AC/DC rectifier”
			7.3.2 Modeling of the combination “DC/AC inverter–grid”
		7.4 State-feedback nonlinear controller design
			7.4.1 Control objectives
			7.4.2 Speed regulator design for synchronous generator
			7.4.3 d-Axis current regulation
			7.4.4 Reactive power and DC voltage controller
				7.4.4.1 DC voltage loop
				7.4.4.2 Reactive power loop
		7.5 Output-feedback nonlinear controller design
			7.5.1 Permanent magnet synchronous generator model in αβ-coordinates
			7.5.2 Model transformation and observability analysis
			7.5.3 High-gain observer design and convergence analysis
			7.5.4 Observer structure
			7.5.5 Stability analysis of the proposed observer
			7.5.6 Observer in ξ-coordinates
			7.5.7 Output-feedback controller
			7.5.8 Simulation results
				7.5.8.1 Simulation protocols
				7.5.8.2 Construction of the speed-reference optimizer
				7.5.8.3 Illustration of the observer performances
				7.5.8.4 Output-feedback controller performances
		7.6 Digital implementation
			7.6.1 Foreground general considerations
			7.6.2 Practical scheme
			7.6.3 Observer discretization
				7.6.3.1 Technical discussion
				7.6.3.2 Digital synthesis of the observer
			7.6.4 Digital output-feedback controller
			7.6.5 Simulation results
		7.7 Conclusion
		References
Chapter-8---Improvement-of-fuel-cell-MPPT-performance-w_2021_Renewable-Energ
	8 Improvement of fuel cell MPPT performance with a fuzzy logic controller
		8.1 Introduction
		8.2 Modeling of proton-exchange membrane fuel cells
			8.2.1 Static model of PEMFC
			8.2.2 Dynamic model of PEMFC
		8.3 Mathematical model of DC–DC converter
		8.4 Proposed algorithm
		8.5 Results and analysis
		8.6 Discussion
		8.7 Conclusion and perspectives
		References
Chapter-9---Control-strategies-of-wind-energy-conversion-_2021_Renewable-Ene
	9 Control strategies of wind energy conversion system-based doubly fed induction generator
		9.1 Introduction
		9.2 Modeling with syntheses of PI controllers of wind system elements
			9.2.1 Mathematical model and identification of wind turbine parameters
			9.2.2 Synthesis of wind turbine MPPT regulation
				9.2.2.1 Overview of the PI controller in the MPPT model
			9.2.3 Mathematical model and identification of DFIG parameters
			9.2.4 Synthesis of direct and indirect vector commands with DFIG PI
				9.2.4.1 Direct PI vector control synthesis with power loops
				9.2.4.2 Synthesis of indirect PI vector control with and without power loops
			9.2.5 Modeling and synthesis of the adjacent PWM control of the inverter
				9.2.5.1 Synthesis of control by sine-delta modulation
			9.2.6 Modeling and synthesis of the DC bus PI and the network filter
				9.2.6.1 Synthesis of the PI controller of your DC bus voltage (Nazari et al., 2017)
				9.2.6.2 Overview of the PI filter current controllers ifd and ifq
		9.3 Results and discussions
			9.3.1 Step 1: simulation of DFIG power control with DVC-PI, IVCOL-PI, and IVCCL-PI techniques in an ideal system
			9.3.2 Step 2: simulation of the control of the wind energy conversion chain of the real system with the DVC-PI, the IVCOL-P...
		9.4 Conclusion
		Appendix
		References
Chapter-10---Modeling-of-a-high-performance-three-phase-volt_2021_Renewable-
	10 Modeling of a high-performance three-phase voltage-source boost inverter with the implementation of closed-loop control
		10.1 Introduction
		10.2 Mathematical analysis of the three-phase boost inverter
			10.2.1 Mathematical analysis based on one-leg operation
				10.2.1.1 Mode I operation
				10.2.1.2 Mode II operation
			10.2.2 State space representation of the one-leg operation
			10.2.3 State space analysis considering six state variables
			10.2.4 Transfer function modeling
			10.2.5 Selection of inductor and capacitor values
		10.3 System description
			10.3.1 Closed-loop control
		10.4 Results and discussions
		10.5 Conclusion
		References
Chapter-11---Advanced-control-of-PMSG-based-wind-energy-con_2021_Renewable-E
	11 Advanced control of PMSG-based wind energy conversion system applying linear matrix inequality approach
		11.1 Introduction
			11.1.1 Context and problematic
			11.1.2 Contribution
			11.1.3 Chapter organization
		11.2 Recent research on control in wind energy conversion systems
		11.3 Model of the PMSG-based WECS
			11.3.1 Model of the wind turbine
			11.3.2 Model of the PMSG
			11.3.3 Model of the PWM converter
			11.3.4 Model of the DC-link voltage
			11.3.5 Model of the filter
		11.4 Controller design of the PMSG-based WECS
			11.4.1 Maximum power point tracking and pitch angle control system
			11.4.2 Designing a T-S fuzzy control for the PMSG side rectifier
				11.4.2.1 Model of the PMSG-WT fuzzy
				11.4.2.2 T-S fuzzy controller
				11.4.2.3 DRM and N-LTR controller
		11.5 Simulation results and discussion
			11.5.1 Simulation results of the proposed control
			11.5.2 Comparison of the proposed and PI controllers’ performance
		11.6 Conclusion
		Appendix
		References
Chapter-12---Fractional-order-controller-design-and-impleme_2021_Renewable-E
	12 Fractional-order controller design and implementation for maximum power point tracking in photovoltaic panels
		12.1 Introduction
		12.2 Related work
			12.2.1 Perturb and observe (P&O)
			12.2.2 Incremental conductance
			12.2.3 Fractional open circuit voltage
		12.3 Problem formulation
			12.3.1 Fractional-order calculus
			12.3.2 Dynamic model of the MPPT system
		12.4 Fractional-order design techniques for MPPT of photovoltaic panels
			12.4.1 FOPID MPPT controller design
			12.4.2 FOTSMC MPPT controller design
		12.5 Numerical experiments
			12.5.1 Experiment 1: FOPID MPPT controller
			12.5.2 Experiment 2: FOTSMC for MPPT
		12.6 Discussion
		12.7 Conclusion
		References
Chapter-13---Techno-economic-modeling-of-stand-alone-and-hyb_2021_Renewable-
	13 Techno-economic modeling of stand-alone and hybrid renewable energy systems for thermal applications in isolated areas
		13.1 Introduction
			13.1.1 Objectives of the work
		13.2 Materials and methods
			13.2.1 Selection of study region
			13.2.2 Assessment of load and demand
			13.2.3 Proposed system
			13.2.4 Energy modeling
			13.2.5 Economic modeling
			13.2.6 Simulation of proposed chilling system
		13.3 Results and discussions
			13.3.1 Thermal and economic performance
			13.3.2 Thermal performance of cooling system working with hybrid energy
			13.3.3 Economic aspects of the chilling system—powered by hybrid energies
		13.4 Technoeconomic analysis of the hybrid energy-based cooling system
		13.5 Sensitivity analysis
		13.6 Conclusion
			13.6.1 Scope for future work
		References
Chapter-14---Solar-thermal-system-an-insight-into-parabol_2021_Renewable-Ene
	14 Solar thermal system—an insight into parabolic trough solar collector and its modeling
		14.1 Introduction
			14.1.1 Motivation
			14.1.2 Background
			14.1.3 Problem statement
			14.1.4 Chapter outline
		14.2 Related work
		14.3 Parabolic trough solar collector—history
		14.4 Parabolic trough solar collector—an overview
		14.5 Performance evaluation of PTSC
			14.5.1 Optical evaluation
			14.5.2 Thermal evaluation
				14.5.2.1 Heat flux and temperature profile
				14.5.2.2 Thermal loss coefficient
			14.5.3 Heat transfer evaluation
				14.5.3.1 Single-phase flow
				14.5.3.2 Double phase flow
		14.6 Analytical thermal models
			14.6.1 Based on flux distribution
			14.6.2 Based on the considered direction of temperature gradient
			14.6.3 Based on the prospect of energy analyzed
			14.6.4 Other models
		14.7 1-D heat transfer model
			14.7.1 Development
			14.7.2 Advantages and limitations
		14.8 Potential applications
			14.8.1 Power generation
			14.8.2 Industrial processes
			14.8.3 Air heating systems
			14.8.4 Desalination processes
		14.9 Discussion
		14.10 Conclusion
		Nomenclature
			Greek letters
			Subscripts
			Abbreviations
		References
Chapter-15---Energy-hub--modeling--control--and-op_2021_Renewable-Energy-Sys
	15 Energy hub: modeling, control, and optimization
		15.1 Introduction
		15.2 Energy management systems
			15.2.1 Energy management information system
			15.2.2 Energy management constraints
		15.3 Concept of energy hub
			15.3.1 Necessity of energy hub
			15.3.2 Types of energy hub
				15.3.2.1 Residential energy hub
				15.3.2.2 Commercial energy hub
				15.3.2.3 Industrial energy hubs
				15.3.2.4 Agricultural energy hubs
		15.4 Mathematical modeling of energy hub
			15.4.1 Modeling of electrical hub
				15.4.1.1 Electrical grid energy
				15.4.1.2 Solar energy
				15.4.1.3 Conversion of gas to electricity
				15.4.1.4 Electrical load balance constraint
				15.4.1.5 Electrical grid constraint
				15.4.1.6 Electric chiller constraint
				15.4.1.7 CHP constraint
			15.4.2 Modeling of heating hub
				15.4.2.1 Gas balance constraints
				15.4.2.2 CHP
				15.4.2.3 Boiler
				15.4.2.4 Heating load balance constraint
				15.4.2.5 Gas grid constraint
				15.4.2.6 Boiler constraint
			15.4.3 Modeling of cooling hub
				15.4.3.1 Absorption chiller
				15.4.3.2 Electric chiller
				15.4.3.3 Cooling load balance constraint
				15.4.3.4 Absorption chiller constraint
		15.5 Energy hub with storage capacities
			15.5.1 Mathematical modeling of ESS
				15.5.1.1 Electrical storages
				15.5.1.2 Heat storages
				15.5.1.3 Cold storages
		15.6 Integration of renewable resources to energy hub
			15.6.1 Modeling of solar energy
			15.6.2 Modeling of wind energy
		15.7 Simulations
		15.8 Optimization of energy hub in GAMS
			15.8.1 Optimization of energy hub with storage capacities
			15.8.2 Optimization of energy hub with renewable energy resources
			15.8.3 Optimization of energy hub with storage capacities including renewable energy resources
			15.8.4 Discussion
		15.9 Conclusion
		References
Chapter-16---Simulation-of-solar-powered-desiccant-assis_2021_Renewable-Ener
	16 Simulation of solar-powered desiccant-assisted cooling in hot and humid climates
		16.1 Introduction
		16.2 Literature survey
		16.3 System description
		16.4 Measurements
		16.5 Data reduction and uncertainty analysis
		16.6 Results and discussion
		16.7 Prediction of system performance by use of TRNSYS simulation
			16.7.1 Weather data reader—type 109 TMY2
			16.7.2 Online graphical plotter—type 65d
			16.7.3 Psychrometrics—type 33e
			16.7.4 Heat recovery wheel—type 760b
			16.7.5 Sensible cooler—type 506c
			16.7.6 Room load—type
			16.7.7 Rotary desiccant dehumidifier—type
		16.8 Conclusion
		Nomenclature
		References
Chapter-17---Recent-optimal-power-flow-algorith_2021_Renewable-Energy-System
	17 Recent optimal power flow algorithms
		17.1 Introduction
		17.2 Moth-flame optimization technique
			17.2.1 Mathematical representation of moth-flame optimization
			17.2.2 Improved moth-flame optimization concept
			17.2.3 Improved moth-flame optimization mathematical formulation
		17.3 Moth swarm algorithm
			17.3.1 Inspiration
			17.3.2 Mathematical modeling of moth swarm algorithm
				17.3.2.1 Reconnaissance phase
					17.3.2.1.1 Suggested diversity index
					17.3.2.1.2 Lévy flights
					17.3.2.1.3 Difference vectors Lévy mutation
					17.3.2.1.4 Suggested acclimatized crossover process
					17.3.2.1.5 Selection strategy
				17.3.2.2 Transverse orientation
				17.3.2.3 Heavenly navigation
					17.3.2.3.1 Gaussian walks
					17.3.2.3.2 Assistive educating scheme with instant recollection
		17.4 Multiverse optimization
			17.4.1 Inspiration
			17.4.2 Mathematical modeling of multiverse optimization
		17.5 Wale optimization algorithm
			17.5.1 Inspiration
			17.5.2 Mathematical modeling of wale optimization algorithm
				17.5.2.1 Circling prey
				17.5.2.2 Bubble-net attacking method
				17.5.2.3 Search for prey
		17.6 Objective functions
			17.6.1 Single objective function
				17.6.1.1 Quadratic fuel cost
				17.6.1.2 Optimum power flow for fuel cost with valve-point loadings
				17.6.1.3 Optimum power flow for emission
				17.6.1.4 Optimum power flow for power loss minimization
			17.6.2 Multiobjective function
				17.6.2.1 Optimum power flow for fuel cost with voltage stability index
				17.6.2.2 Optimum power flow for fuel cost with emission
				17.6.2.3 Optimum power flow for fuel cost with active power losses
				17.6.2.4 Optimum power flow for fuel cost with voltage deviation
			17.6.3 Constraints
				17.6.3.1 State variables
				17.6.3.2 Control variables
				17.6.3.3 Operating constraints
				17.6.3.4 Equality constraints
				17.6.3.5 Inequality constraints
		17.7 Results and discussions
			17.7.1 Case 5-1: Optimum power flow for fuel cost minimization
			17.7.2 Case 5-2: Optimum power flow for minimization of quadratic fuel cost with valve-point loadings
			17.7.3 Case 5-3: Optimum power flow for emission cost minimization
			17.7.4 Case 5-4: Optimum power flow for power loss minimization
			17.7.5 Case 5-5: Optimum power flow for minimization of fuel cost with voltage stability index
			17.7.6 Case 5-6: Optimum power flow for minimization of fuel cost with emission
			17.7.7 Case 5-7: Optimum power flow for minimization of fuel cost and active power losses
			17.7.8 Case 5-8: Optimum power flow for minimization of fuel cost and voltage deviation
		17.8 Conclusion
		Appendix A (Tables 17.A1–17.A5)
		References
Chapter-18---Challenges-for-the-optimum-penetration-of_2021_Renewable-Energy
	18 Challenges for the optimum penetration of photovoltaic systems
		Nomenclature
		18.1 Introduction
		18.2 PV system management
			18.2.1 Control and monitoring
			18.2.2 Communications
			18.2.3 Metering
		18.3 PV system grid connection
			18.3.1 General criteria
			18.3.2 Inverters
			18.3.3 Electrical protection systems
			18.3.4 Voltage sags control
		18.4 Future technical regulatory aspects
		18.5 Conclusions
		Acknowledgments
		References
Chapter-19---Modeling-and-optimization-of-performance-of-a-stra_2021_Renewab
	19 Modeling and optimization of performance of a straight bladed H-Darrieus vertical-axis wind turbine in low wind speed co...
		19.1 Introduction
		19.2 Related work
			19.2.1 Research gap and contribution of the present chapter
		19.3 Turbine design and experimental description
		19.4 Integrated entropy–multicriteria ratio analysis method
		19.5 Modeling of vertical-axis wind turbine using integrated entropy–multicriteria ratio analysis method
		19.6 Results and discussion
			19.6.1 Parametric analysis
			19.6.2 Optimization of vertical-axis wind turbine parameters
			19.6.3 Utility of the optimization results
			19.6.4 Confirmatory test
		19.7 Conclusions and scope for future work
		References
Chapter-20---Maximum-power-point-tracking-design-using-partic_2021_Renewable
	20 Maximum power point tracking design using particle swarm optimization algorithm for wind energy conversion system connec...
		20.1 Introduction
		20.2 Wind energy conversion system modeling
			20.2.1 Wind profile modeling
			20.2.2 Wind turbine and gearbox modeling
			20.2.3 Doubly fed induction generator modeling
			20.2.4 Modeling of the back-to-back converters
			20.2.5 Grid modeling
			20.2.6 Phase-Locked Loop technique
				20.2.6.1 Determination of the phase-locked loop controller parameters
		20.3 Control strategies of the maximum power point tracking
			20.3.1 Classical proportional–integral for maximum power point tracking
			20.3.2 Particle swarm optimization for maximum power point tracking
				20.3.2.1 Particle swarm optimization algorithm overview and concept
				20.3.2.2 Implementation of particle swarm optimization into proportional–integral controller for maximum power point tracking
				20.3.2.3 Algorithm steps and pseudo-code of basic particle swarm optimization
		20.4 Field-oriented control technique of the active and reactive power
			20.4.1 Active and reactive power control
			20.4.2 Rotor side converter control
				20.4.2.1 Determination of the proportional–integral controller parameters
			20.4.3 Grid side converter control
				20.4.3.1 Determination of the DC-link controller parameters
				20.4.3.2 Determination of the grid side converter controller parameters
		20.5 Simulation results and discussion
		20.6 Conclusion
		Appendix A
		References
Chapter-21---Multiobjective-optimization-based-energy-managem_2021_Renewable
	21 Multiobjective optimization-based energy management system considering renewable energy, energy storage systems, and ele...
		21.1 Introduction
		21.2 System description
			21.2.1 Photovoltaic model
			21.2.2 Wind turbine system
			21.2.3 Electric vehicle system
		21.3 Proposed scheduling and optimization model
			21.3.1 Optimization model
			21.3.2 Objective function
				21.3.2.1 Energy storage system
				21.3.2.2 Electric vehicle system
		21.4 Results and discussion
		21.5 Conclusion
		References
Chapter-22---Fuel-cell-parameters-estimation-using-op_2021_Renewable-Energy-
	22 Fuel cell parameters estimation using optimization techniques
		22.1 Introduction
		22.2 Mathematical model of proton exchange membrane fuel cell stacks
			22.2.1 The concept of proton exchange membrane fuel cell
			22.2.2 Formulation of the objective function
		22.3 Optimization techniques
			22.3.1 Grey wolf optimizer
			22.3.2 Salp swarm algorithm
			22.3.3 Whale optimization algorithm
				22.3.3.1 Bubble-net assaulting strategy (exploitation stage)
				22.3.3.2 Scan for prey (investigation stage)
		22.4 Case study
		22.5 Results and discussion
			22.5.1 Statistical measures
			22.5.2 Parameters’ estimation of proton exchange membrane fuel cell stacks
			22.5.3 Results of simulation under various operating conditions
		22.6 Conclusion
		References
Chapter-23---Optimal-allocation-of-distributed-generation-s_2021_Renewable-E
	23 Optimal allocation of distributed generation/shunt capacitor using hybrid analytical/metaheuristic techniques
		23.1 Introduction
		23.2 Objective function
			23.2.1 Equality and inequality constraints
		23.3 Mathematical formulation of the analytical technique
		23.4 Metaheuristic technique
			23.4.1 Sine cosine algorithm
			23.4.2 Whale optimization algorithm
		23.5 Simulation results
			23.5.1 IEEE 33-bus RDS
			23.5.2 IEEE 69-bus RDS
		23.6 Conclusion
		References
Chapter-24---Optimal-appliance-management-system-with-ren_2021_Renewable-Ene
	24 Optimal appliance management system with renewable energy integration for smart homes
		24.1 Introduction
		24.2 Related work
		24.3 System architecture
			24.3.1 The home appliances
			24.3.2 Communication protocol technology
			24.3.3 Electricity tariffs
		24.4 The proposed approach for scheduling the home appliances
			24.4.1 Scheduling problem formulation
			24.4.2 Solar panels generation model
			24.4.3 Energy storage system model
			24.4.4 Objective function formulation
		24.5 Results and discussion
			24.5.1 Basic scenario: the main grid provides the whole power need
			24.5.2 Second scenario: solar panels and main grid
			24.5.3 Third scenario: solar panels, battery storage, and main grid
		24.6 Conclusion
		References
Chapter-25---Solar-cell-parameter-extraction-using-the-_2021_Renewable-Energ
	25 Solar cell parameter extraction using the Yellow Saddle Goatfish Algorithm
		25.1 Introduction
		25.2 Solar cell mathematical modeling
		25.3 Yellow Saddle Goatfish Algorithm-based solar cell extraction
			25.3.1 Stage 1: initialization
			25.3.2 Stage 2: chasing
			25.3.3 Stage 3: blocking
			25.3.4 Stage 4: role change
			25.3.5 Stage 5: zone change
		25.4 Results and discussion
		25.5 Experimental data measurement of 250 Wp PV module (SVL0250P) using SOLAR-4000 analyzer
		25.6 Conclusion
		References
Chapter-26---Reactive-capability-limits-for-wind-turbine-b_2021_Renewable-En
	26 Reactive capability limits for wind turbine based on SCIG for optimal integration into the grid
		26.1 Introduction
		26.2 Literature survey and grid code requirements
			26.2.1 Reactive power capability curves in the grid code requirements
			26.2.2 European grid codes for wind power production
			26.2.3 Reactive capability of synchronous generator
			26.2.4 Reactive capability of DFIG
		26.3 Reactive capability limits for squirrel cage induction generator
			26.3.1 Model of squirrel cage induction generator
			26.3.2 Characteristics of SCIG and the maximum rotor flux
			26.3.3 Reactive capability limits under constraints of stator voltage and current
			26.3.4 Reactive capability limits under rotor current constraint
			26.3.5 Steady-state stability limit
		26.4 Estimation of reactive power limits for the grid side system
			26.4.1 Reactive capability limit under the filter voltage constraint
			26.4.2 Reactive capability limit under the grid side current constraint
			26.4.3 The constraints of AC/DC/AC full converter for PQ control
		26.5 Reactive capability for DC bus capacitor
			26.5.1 DC capacitance power production
			26.5.2 Mitigation of the ripples and DC bus capacitance limit
		26.6 Validation results
		26.7 Conclusion
		Abbreviations
		Appendix A
		References
Chapter-27---Demand-side-strategy-management-using-PSO-and_2021_Renewable-En
	27 Demand-side strategy management using PSO and BSA for optimal day-ahead load shifting in smart grid
		27.1 Introduction
			27.1.1 Context and problematic
			27.1.2 State of art
			27.1.3 Contribution
			27.1.4 Chapter organization
		27.2 DSM driven approaches
			27.2.1 Environmental goal
			27.2.2 Economic dispatch
			27.2.3 The network driven
		27.3 Mathematical formulation of the problem
			27.3.1 Problem formulation
		27.4 Proposed demand management optimization algorithm
		27.5 Energy management of the proposed system
			27.5.1 Solar PV modules
			27.5.2 Grid
			27.5.3 Battery
			27.5.4 Converter
		27.6 Results and discussion
			27.6.1 Peak load reduction
			27.6.2 Electricity generation
		27.7 Conclusion
		References
Chapter-28---Optimal-power-generation-and-power-flow-cont_2021_Renewable-Ene
	28 Optimal power generation and power flow control using artificial intelligence techniques
		28.1 Introduction
		28.2 Conventional methods
			28.2.1 Gradient method
			28.2.2 Newton method
			28.2.3 Linear programming
		28.3 Artificial neural network and fuzzy logic to optimal power flow
			28.3.1 Artificial neural network
				28.3.1.1 Artificial neural network applied to optimal power flow
			28.3.2 Fuzzy logic method
		28.4 Genetic algorithm
		28.5 Application of expert system to power system
			28.5.1 Overview of expert system
			28.5.2 Application to power system
		28.6 Assessment of optimal power flow by game playing concept
		References
Chapter-29---Nature-inspired-computational-intelligence-fo_2021_Renewable-En
	29 Nature-inspired computational intelligence for optimal sizing of hybrid renewable energy system
		29.1 Introduction
		29.2 Mathematical hybrid system model
			29.2.1 Models of wind generator and PV panel
			29.2.2 Battery model
		29.3 Optimization formulation
		29.4 Nature-inspired algorithms
		29.5 Advantages and limitations of the algorithms
		29.6 Numerical data
		29.7 Results and discussion
			29.7.1 Values used for the parameters
			29.7.2 Experimental results and discussions
		29.8 Findings of the study
		29.9 Conclusion and future directions
		Acknowledgments
		References
Chapter-30---Optimal-design-and-techno-socio-economic-anal_2021_Renewable-En
	30 Optimal design and techno-socio-economic analysis of hybrid renewable system for gird-connected system
		30.1 Introduction
		30.2 Motivation and potential benefits of hybrid renewable sources
		30.3 Hybrid renewable energy system design and optimization
		30.4 Availability of renewable sources and utilization for case study
		30.5 Modeling of hybrid renewable system components
			30.5.1 Solar–photovoltaic
			30.5.2 Wind turbine
			30.5.3 Battery storage system
			30.5.4 System converter
			30.5.5 Diesel generator
			30.5.6 Load profile of system
		30.6 Explanation of problem and methodology for case study
			30.6.1 Technical parameters
				30.6.1.1 Reliability of system
				30.6.1.2 Resilience of system
				30.6.1.3 Renewable factor
			30.6.2 Economic parameter
				30.6.2.1 Total net present cost
				30.6.2.2 Levelized cost of energy
				30.6.2.3 Total annualized cost
				30.6.2.4 Annualized savings
				30.6.2.5 Capital investment
				30.6.2.6 Internal rate of return
				30.6.2.7 Return on investment
				30.6.2.8 Simple payback
			30.6.3 Social parameters
		30.7 Results and discussion
			30.7.1 Analysis of base system (current system): diesel generator+DVC-NITD-grid
			30.7.2 Analysis of the proposed HRES: solar PV–wind–battery storage–diesel generator connected with DVC-NITD-grid
		30.8 Conclusion
		Acknowledgment
		References
Chapter-31---Stand-alone-hybrid-system-of-solar-photovoltai_2021_Renewable-E
	31 Stand-alone hybrid system of solar photovoltaics/wind energy resources: an eco-friendly sustainable approach
		31.1 Introduction
		31.2 Renewable energy sources
			31.2.1 Solar energy
				31.2.1.1 Sunlight-based PV
				31.2.1.2 Solar thermal energy
			31.2.2 Wind energy
			31.2.3 Biomass
			31.2.4 Small hydropower
			31.2.5 Other RES
				31.2.5.1 Geothermal energy
				31.2.5.2 Nuclear energy
				31.2.5.3 Hydrogen energy resource
		31.3 Hybrid renewable energy systems
			31.3.1 Importance of HRES
			31.3.2 Energy management of HRES
			31.3.3 Operation modes of HRES
				31.3.3.1 Grid-tied HRES
				31.3.3.2 Stand-alone HRES
				31.3.3.3 Smart grid-based HRES
		31.4 Modeling of SPV/wind HRES
			31.4.1 System components of SPV/wind HRES
				31.4.1.1 Solar photovoltaic array
				31.4.1.2 Wind turbine
				31.4.1.3 Battery storage
				31.4.1.4 Inverter
				31.4.1.5 Diesel generator
			31.4.2 Control strategies of SPV/wind HRES
			31.4.3 Mathematical modeling of SPV/wind HRES
				31.4.3.1 Modeling of PV array
				31.4.3.2 Wind turbine modeling
				31.4.3.3 Battery storage modeling
		31.5 Optimization and sizing of SPV/wind HRES
			31.5.1 Optimal design criteria for HRES
			31.5.2 Optimization problem
			31.5.3 Optimization algorithm
			31.5.4 Sizing techniques
		31.6 Future of SPV/wind HRES
		31.7 Conclusion
		References
Index_2021_Renewable-Energy-Systems
	Index