scholarly journals Economic Load Dispatch using Lambda Iteration, Particle Swarm Optimization & Genetic Algorithm

2021 ◽  
Vol 9 (8) ◽  
pp. 972-977
Author(s):  
Zaineb Nisar Jan
Keyword(s):  
Genetic Algorithm ◽  
Power Supply ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Economic Load Dispatch ◽  
Swarm Optimization ◽  
Load Demand ◽  
Reduced Costs ◽  
Reliable Power Supply ◽  
Fuel Costs

Abstract: In economic load dispatch problem scheduling of loads is done in order to achieve reliable power supply with reduced costs. With the increase in load demand with each passing year energy crisis is increasing hence an area of study where fuel costs can be reduced was proposed. This can be achieved using various methods among which the methods discussed in this paper are Lambda Iteration, Particle Swarm Operation and Genetic Algorithm. Based on numerical results the best optimization technique can be figured out among the discussed methods. Keywords: Lambda Iteration, PSO, GA, economic load dispatch, optimization solutions

scholarly journals Solving Economic Load Dispatch Problem using Grey Wolf Optimizer Method

2019 ◽  
Vol 8 (4) ◽  
pp. 684-688
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Grey Wolf Optimizer ◽  
Economic Load Dispatch ◽  
Grey Wolf ◽  
Fuel Cost ◽  
Swarm Optimization ◽  
Load Demand ◽  
Alpha Beta ◽  
Critical Phases

The aim of economic load dispatch (ELD) is to accomplish the load demand with less fuel cost by the generators. This research shows a new grey wolf-inspired algorithm called the Grey Wolf Optimizer (GWO) to achieve ELD. The GWO algorithm follows mainly the grey wolves hierarchy and hunting scheme. The controlling hierarchy is driven by four wolves, namely alpha, beta, delta, and omega. Three critical phases of hunting are implemented, looking for a target, surrounding a target, and attacking a target. Now, on 20 generating units, the algorithm is used and is equated with Particle Swarm Optimization (PSO). The findings show that, compared to PSO, the GWO algorithm is set to yield economic results.

A Comparative Study of Genetic and Particle Swarm Optimization Algorithms and Their Hybrid Method in Water Flooding Optimization

2018 ◽  
Vol 140 (10) ◽  
Author(s):  
Majid Siavashi ◽  
Mohsen Yazdani
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Newton Method ◽  
Oil Recovery ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Optimization Techniques ◽  
Water Flooding ◽  
Swarm Optimization

Optimization of oil production from petroleum reservoirs is an interesting and complex problem which can be done by optimal control of well parameters such as their flow rates and pressure. Different optimization techniques have been developed yet, and metaheuristic algorithms are commonly employed to enhance oil recovery projects. Among different metaheuristic techniques, the genetic algorithm (GA) and the particle swarm optimization (PSO) have received more attention in engineering problems. These methods require a population and many objective function calls to approach more the global optimal solution. However, for a water flooding project in a reservoir, each function call requires a long time reservoir simulation. Hence, it is necessary to reduce the number of required function evaluations to increase the rate of convergence of optimization techniques. In this study, performance of GA and PSO are compared with each other in an enhanced oil recovery (EOR) project, and Newton method is linked with PSO to improve its convergence speed. Furthermore, hybrid genetic algorithm-particle swarm optimization (GA-PSO) as the third optimization technique is introduced and all of these techniques are implemented to EOR in a water injection project with 13 decision variables. Results indicate that PSO with Newton method (NPSO) is remarkably faster than the standard PSO (SPSO). Also, the hybrid GA-PSO method is more capable of finding the optimal solution with respect to GA and PSO. In addition, GA-PSO, NPSO, and GA-NPSO methods are compared and, respectively, GA-NPSO and NPSO showed excellence over GA-PSO.

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