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

✍ Scribed by Ahmad Taher Azar (editor)

• Publisher: Academic Press
• Year: 2021
• Tongue: English
• Leaves: 713
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

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