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Short Term Hydro-Thermal Scheduling Using Backtracking Search Algorithm

Short Term Hydro-Thermal Scheduling Using Backtracking Search Algorithm

Koustav Dasgupta, Provas Kumar Roy
Copyright: © 2020 |Volume: 11 |Issue: 4 |Pages: 26
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781799802877|DOI: 10.4018/IJAMC.2020100103
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MLA

Dasgupta, Koustav, and Provas Kumar Roy. "Short Term Hydro-Thermal Scheduling Using Backtracking Search Algorithm." IJAMC vol.11, no.4 2020: pp.38-63. http://doi.org/10.4018/IJAMC.2020100103

APA

Dasgupta, K. & Roy, P. K. (2020). Short Term Hydro-Thermal Scheduling Using Backtracking Search Algorithm. International Journal of Applied Metaheuristic Computing (IJAMC), 11(4), 38-63. http://doi.org/10.4018/IJAMC.2020100103

Chicago

Dasgupta, Koustav, and Provas Kumar Roy. "Short Term Hydro-Thermal Scheduling Using Backtracking Search Algorithm," International Journal of Applied Metaheuristic Computing (IJAMC) 11, no.4: 38-63. http://doi.org/10.4018/IJAMC.2020100103

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Abstract

In this article, a new optimization technique, the backtracking search algorithm (BSA), is proposed to solve the hydrothermal scheduling problem. The BSA has mainly unique five steps: (i) Initialization; (ii) Selection – I; (iii) Mutation; (iv) Crossover; and (v) Selection – II; which have been applied to minimize fuel cost of the hydro-thermal scheduling problem. The BSA is very fast, robust, reliable optimization technique and gives an accurate, optimized result. Mutation and crossover are very effective steps of the BSA, which help to determine the better optimum value of the objective function. Here, four hydro and three thermal power generating units are considered. Performance of each committed generating units (hydro and thermal) are also analyzed using a new proposed algorithm, the BSA. A multi-reservoir cascaded hydroelectric with a nonlinear relationship between water discharge rate and power generation is considered. The valve point loading effect is also considered with a fuel cost function. The proposed optimum fuel cost obtained from the BSA shows the better result as compared to other techniques like particle swarm optimization (PSO), teaching learning-based optimization (TLBO), quasi-oppositional teaching learning-based optimization (QOTLBO), real-coded genetic algorithm (RCGA), mixed-integer linear programming (MILP) and krill herd algorithm (KHA), etc.

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