Renewable Energy Systems: Modelling, Optimization and Control (Advances in Nonlinear Dynamics and Chaos (ANDC))

Renewable Energy Systems
Copyright
Contents
List of contributors
Preface
	About the book
	Objectives of the book
	Organization of the book
	Book features
	Audience
	Acknowledgments
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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 690
		16.7.7 Rotary desiccant dehumidifier—type 683
	16.8 Conclusion
	Nomenclature
	References
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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