Nature Inspired Optimization for Electrical Power System (Algorithms for Intelligent Systems)

Preface
Synopsis
Contents
About the Editors
1 Teaching-Learning-Based Optimization for Static and Dynamic Load Dispatch
	1 Introduction
	2 Problem Statement
	3 Teaching–Learning-Based Optimization
	4 Description of Problems and Simulation Results
	5 Conclusion
	References
2 Application of Elitist Teacher–Learner-Based Optimization Algorithm for Congestion Management
	1 Introduction
	2 Problem Formulation
		2.1 Equality Constraints
		2.2 Inequality Constraints
		2.3 Fitness Function
	3 Frame of Elitist Teacher–Learner-Based Optimization (ETLBO)
		3.1 Teacher Phase
		3.2 Learner Phase
		3.3 Elitism
	4 Elitist TLBO for Congestion Management
		4.1 About Test Systems
		4.2 Line Outage Contingency: Case I
		4.3 Sudden Increment in Demand with Single Line Outage: Case II
		4.4 Abrupt Line Power Limits Variation: Case III and IV
		4.5 Generation Rescheduling for CM
		4.6 ETLBO for Solution of CM Problem: Mathematical Procedure
	5 Numerical Results and Analysis
		5.1 Convergence Analysis of ETLBO
	6 Conclusions
	References
3 PSO-Based Optimization of Levelized Cost of Energy for Hybrid Renewable Energy System
	1 Introduction
	2 Problem Formulation
	3 Optimization of LCOE
		3.1 Power Generation Equality/Inequality Constraint
	4 Results and Discussion
		4.1 Test Case Description
		4.2 Optimization of LCOE
		4.3 Effect of Capacity Factor on Optimal Value of LCOE
		4.4 Convergence Characteristics of the Solver
		4.5 Validation of Results Using Particle Swarm Optimization
	5 Conclusion
	References
4 PSO-Based PID Controller Designing for LFC of Single Area Electrical Power Network
	1 Introduction
	2 Problem Formulation
		2.1 System Description
		2.2 A Brief Introduction of PID Controller
		2.3 Objective Function Formulation
	3 Employed Optimization Techniques
		3.1 GA
		3.2 PSO
	4 Results and Discussions
		4.1 Case 1: Objective Function—IAE
		4.2 Case 2: Objective Function—ISE
		4.3 Case 3: Objective Function-ITAE
		4.4 Case 4: Objective Function-ITSE
	5 Conclusion
	References
5 Combined Economic Emission Dispatch of Hybrid Thermal PV System Using Artificial Bee Colony Optimization
	1 Introduction
	2 Problem Formulation
		2.1 Objective Function
		2.2 Equality Constraint
		2.3 Inequality Constraint
	3 Artificial Bee Colony Optimization
	4 Results and Discussion
		4.1 Description of Test Cases
		4.2 Simulation Results
	5 Conclusion
	References
6 Dynamic Scheduling of Energy Resources in Microgrid Using Grey Wolf Optimization
	1 Introduction
	2 Problem Formulation
		2.1 Inequality Constraints
		2.2 Equality Constraints
	3 Grey Wolf Optimization
	4 Results and Discussion
		4.1 Description of Test Cases
		4.2 Simulation Results
	5 Conclusion
	References
7 Mixed-Integer Differential Evolution Algorithm for Optimal Static/Dynamic Scheduling of a Microgrid with Mixed Generation
	1 Introduction
	2 Problem Formulation for Microgrid with Mixed Generation
		2.1 Generating Unit Limits
		2.2 Supply and Load Balance Constraint
		2.3 Generator Ramp Rate Limits
		2.4 Formulation of Total Cost Function for the Wind–PV–Diesel Microgrid
		2.5 SO and Two-Objective Optimization Functions
	3 Mixed-Integer Differential Evolution (MIDE)
	4 Results and Discussion
		4.1 Description of the Modified Microgrid Test System
		4.2 Setting of the Optimal Parameters of MIDE
		4.3 SO Optimal Static Scheduling of Microgrid Using MIDE
		4.4 SO Optimal Dynamic Scheduling of Wind–PV–Diesel Microgrid
		4.5 Two-Objective Dynamic Optimal Scheduling of Wind–PV–Diesel Microgrid
	5 Comparison and Validation of Results
	6 Conclusion
	References
8 NSGA-II Based Reactive Power Management in Radial Distribution System Integrated with DGs
	1 Introduction
	2 Multi-Objective Reactive Power Management
		2.1 Objective Functions of RPM Problem
	3 Non-dominated Sorting Genetic Algorithm-II for MORPM
	4 Results and Discussion
		4.1 Case1: Minimization of PL and TVV
		4.2 Case 2: Minimization of PL and TCRPS
		4.3 Case 3: Minimization of PL, TVV, and TCRPS
	5 Conclusion
	References
9 Short-Term Hydrothermal Scheduling Using Bio-inspired Computing: A Review
	1 Introduction
	2 Formulation of SHTS Problem
		2.1 Objective Function
		2.2 Operational Constraints
	3 Bio-Inspired Algorithm and Their Application
		3.1 Genetic Algorithm (GA)
		3.2 Particle Swarm Optimization (PSO)
		3.3 Differential Evolution (DE)
		3.4 Evolutionary Programming (EP)
		3.5 Artificial Bee Colony (ABC) Algorithm
		3.6 Gravitational Search Algorithm (GSA)
		3.7 Cuckoo Search Algorithm (CSA)
		3.8 Teaching-Learning-Based Optimization (TLBO)
		3.9 Flower Pollination Algorithm (FPA)
	4 Conclusion
	References