Scheduling short-term hydrothermal generation using evolutionary programming techniques

Yang, Po‐Chih; Yang, Hong-Tzer; Huang, Ching-Lien

doi:10.1049/ip-gtd:19960463

Cited by 101 publications

(56 citation statements)

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“…(ii) Water availability constraints: Determination of generation level of slack generator Thermal generators and hydro generators deliver their power output subject to the power balance constraint (2), water availability constraint (4) and respective capacity constraints (5) and (6). Assuming the power loading of Ν Ρ and first (Ν s -1) generators are known, the power level of the Ν s th generator (i.e.…”

Section: Constraintsmentioning

confidence: 99%

“…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%

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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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Section: Constraintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Opposition-based differential evolution for hydrothermal power system

Pattanaik

Basu

Dash³

2017

Prot Control Mod Power Syst

View full text Add to dashboard Cite

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“…Therefore, many methods have been developed to solve this problem over the past decades. The major methods include variational calculus (Grake & algorithm (Turgeon 1981;Lee 1989), Lagrangian relaxation method (Tufegdzic 1996;Salam & Mohamed 1998) and modern heuristics algorithms such as artificial neural networks (Naresh & Sharma 1999), evolutionary algorithm [17 -20] (Chen & Chang 1996;Yang & Yang 1996;Orero & Irving 1998;Werner & Verstege 1999), chaotic optimization (Yuan & Yuan 2002), ant colony (Huang 2001), Tabu search (Bai & Shahidehpour 1996) and simulated annealing (Wong & Wong 1994). But these methods have one or another drawback such as dimensionality difficulties, large memory requirement or an inability to handle nonlinear characteristics, premature phenomena and trapping into local optimum, taking too much computation time.…”

Section: Short-term Hydrothermal Generation Scheduling (Shgs) Ismentioning

confidence: 99%

Hydrothermal systems generation scheduling using cultural algorithm

Yuan

Nie

Yuan

et al. 2009

Journal of Hydroinformatics

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This paper proposes an enhanced cultural algorithm to solve the short-term generation scheduling of hydrothermal systems problem, in which differential evolution is embedded into a cultural algorithm and applies two knowledge sources to influence the variation operator of differential evolution and couples with simple selection criteria based on feasibility rules and heuristic search strategies to handle constraints in the cultural algorithm effectively. A test hydrothermal system is used to verify the feasibility and effectiveness of the proposed method.Results are compared with those of other optimization methods reported in the literature. It is shown that the proposed method is capable of yielding higher quality solutions.

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“…Many techniques have now been designed to solve optimal scheduling issue in the past decades. The most important technique includes the variation calculus [2], function analysis [3] dynamic programming [4], nonlinear programming [5], and Evolutionary algorithm [6].…”

Section: Introductionmentioning

confidence: 99%