Short-term hydrothermal generation scheduling model using a genetic algorithm

Gil, Esteban; Bustos, Javier Torres; Rudnick, H.

doi:10.1109/tpwrs.2003.819877

Cited by 201 publications

(61 citation statements)

References 43 publications

(25 reference statements)

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“…Each particle moves its position in the search space and updates its velocity according to its own flying experience and neighbor's flying experience. After finding the two best values, the particle update its velocity according to equation (17).…”

Section: Particle Swarm Optimization With Constriction Factor 1)mentioning

confidence: 99%

Variable Head Hydrothermal Generation Scheduling Using Genetic Algorithm and Constriction Factor Based Particle Swarm Optimization Technique

Salama¹,

Elgazar²,

Abdelmaksoud³

et al. 2013

IOSRJEN

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-In this paper, a genetic algorithm and constriction factor based particle swarm optimization technique are proposed for solving the short term variable head hydrothermal scheduling problem with transmission line losses. The performance efficiency of the proposed techniques is demonstrated on hydrothermal test system comprising of two thermal units and two hydro power plants. the simulation results obtained from the constriction factor based particle swarm optimization technique are compared with the outcomes obtained from the genetic algorithm to reveal the validity and verify the feasibility of the proposed methods. The results show that the constriction factor based particle swarm optimization technique give the same results as obtained by genetic algorithm but the computation time of the constriction factor based particle swarm optimization method is less than genetic algorithm.

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Section: Particle Swarm Optimization With Constriction Factor 1)mentioning

confidence: 99%

Variable Head Hydrothermal Generation Scheduling Using Genetic Algorithm and Constriction Factor Based Particle Swarm Optimization Technique

Salama¹,

Elgazar²,

Abdelmaksoud³

et al. 2013

IOSRJEN

View full text Add to dashboard Cite

show abstract

“…Recently, stochastic search algorithms such as simulated annealing (SA) [5], evolutionary programming (EP) [6], genetic algorithm (GA) [7,8], evolutionary programming technique [9], differential evolution (DE) [10][11][12], particle swarm optimization [13], artificial immune system [14], clonal selection algorithm [15] and teaching learning based optimization [16] have been successfully used to solve hydrothermal scheduling problem.…”

Section: Introductionmentioning

confidence: 99%

Opposition-based differential evolution for hydrothermal power system

Pattanaik

Basu

Dash³

2017

Prot Control Mod Power Syst

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This paper presents opposition-based differential evolution to determine the optimal hourly schedule of power generation in a hydrothermal system. Differential evolution (DE) is a population-based stochastic parallel search evolutionary algorithm. Opposition-based differential evolution has been used here to improve the effectiveness and quality of the solution. The proposed opposition-based differential evolution (ODE) employs opposition-based learning (OBL) for population initialization and also for generation jumping. The effectiveness of the proposed method has been verified on two test problems, two fixed head hydrothermal test systems and three hydrothermal multi-reservoir cascaded hydroelectric test systems having prohibited operating zones and thermal units with valve point loading. The results of the proposed approach are compared with those obtained by other evolutionary methods. It is found that the proposed opposition-based differential evolution based approach is able to provide better solution.

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“…Thus, the problem of minimizing the operational cost of a hydrothermal system is to minimize the fuel cost of thermal plants subjected to various equality and inequality constraints offered by the operation of hydrothermal plants and power system network. Limited energy storing capacity of water reservoirs and having the stochastic nature of availability of water makes the solution more difficult for hydrothermal systems compared to pure thermal systems [1]. Hydroelectric plants also meet purposes other than power generation including flood control and irrigation purpose [2].…”

Section: Introductionmentioning

confidence: 99%

Short Term Hydrothermal Scheduling Using Particle Swarm Optimization

2016

IJAERD

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Operation of a system having both hydro and thermal plants is a challenging problem as hydro plants have negligible operating cost, but are required to operate under constraints of water available for hydro generation in a given period of time. The problem of minimizing the operating cost of a hydrothermal system can be viewed as one of minimizing the fuel cost of thermal plants underconstrains of water availability for hydro generation over a given period of operation. Earlier, a wide variety of optimization techniques have applied to solve the Hydrothermal scheduling problems such as dynamic programing, gradient search but these methods has drawbacks such as large computation time, algorithm complexity. The work done in this work presents solution to short-term hydrothermal scheduling problem. The solution approach based on Particle Swarm Optimization (PSO) under matlab software.

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Short-term hydrothermal generation scheduling model using a genetic algorithm

Cited by 201 publications

References 43 publications

Variable Head Hydrothermal Generation Scheduling Using Genetic Algorithm and Constriction Factor Based Particle Swarm Optimization Technique

Variable Head Hydrothermal Generation Scheduling Using Genetic Algorithm and Constriction Factor Based Particle Swarm Optimization Technique

Opposition-based differential evolution for hydrothermal power system

Short Term Hydrothermal Scheduling Using Particle Swarm Optimization

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