Singh, M.; Dhillon, J. Stochastic Thermal Load Dispatch Employing Opposition-based Greedy Heuristic Search. Preprints2022, 2022070385. https://doi.org/10.20944/preprints202207.0385.v1
APA Style
Singh, M., & Dhillon, J. (2022). Stochastic Thermal Load Dispatch Employing Opposition-based Greedy Heuristic Search. Preprints. https://doi.org/10.20944/preprints202207.0385.v1
Chicago/Turabian Style
Singh, M. and JS Dhillon. 2022 "Stochastic Thermal Load Dispatch Employing Opposition-based Greedy Heuristic Search" Preprints. https://doi.org/10.20944/preprints202207.0385.v1
Abstract
A thermal load dispatch problem minimizes the number of objectives viz operating cost and emission of gaseous pollutants together while allocating the power demand among the committed generating units subject to physical and technological system constraints. A stochastic thermal load dispatch problem is undertaken while taking into consideration, the uncertainties, errors in data measurements and nature of load demand which is random. Owing to uncertain load demand, variance due to mismatch of power demand termed as risk, is considered as another conflicting objective to be minimized. Generally multiobjective problems generate a set of non-inferior solutions are generated and supplied to a decision maker to select the best solution from the set of non-inferior solutions. This paper proposes opposition-based greedy heuristic search (OGHS) method to generate a set of non-inferior solutions. Opposition-based learning is applied to generate initial population to select good candidates. Migration to maintain diversity in the set of feasible solutions is also based on opposition-based learning. Mutation strategy is implemented by perturbing the genes heuristically in parallel and better one solution is sought for each member. Feasible solutions are achieved heuristically by modifying the generation-schedules in such a manner that violation of operating generation limits are avoided. The OGHS method is simple to implement and provides global solutions derived from the randomness of the population generated without tuning of parameters. Decision maker exploits fuzzy membership functions to decide the final decision. Validity of the method has been demonstrated by analysing systems in different scenarios consisting of six generators and forty generators.
Engineering, Electrical and Electronic Engineering
Copyright:
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