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Multi-Objective Electrical Power System Design Optimization Using a Modified Bat Algorithm

Abstract : The design of energy systems is very important in order to reduce operating costs and guarantee the reliability of a system. This paper proposes a new algorithm to solve the design problem of optimal multi-objective redundancy of series-parallel power systems. The chosen algorithm is based on the hybridization of two metaheuristics, which are the bat algorithm (BA) and the generalized evolutionary walk algorithm (GEWA), also called BAG (bat algorithm with generalized flight). The approach is combined with the Ushakov method, the universal moment generating function (UMGF), to evaluate the reliability of the multi-state series-parallel system. The multi-objective design aims to minimize the design cost, and to maximize the reliability and the performance of the electric power generation system from solar and gas generators by taking into account the reliability indices. Power subsystem devices are labeled according to their reliabilities, costs and performances. Reliability hangs on an operational system, and implies likewise satisfying customer demand, so it depends on the amassed batch curve. Two different design allocation problems, commonly found in power systems planning, are solved to show the performance of the algorithm. The first is a bi-objective formulation that corresponds to the minimization of system investment cost and maximization of system availability. In the second, the multi-objective formulation seeks to maximize system availability, minimize system investment cost, and maximize the capacity of the system.
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https://hal-utt.archives-ouvertes.fr/hal-03321084
Contributor : Jean-Baptiste Vu Van Connect in order to contact the contributor
Submitted on : Tuesday, August 17, 2021 - 9:23:01 AM
Last modification on : Wednesday, October 13, 2021 - 3:28:01 AM

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Khaled Guerraiche, Latifa Dekhici, Eric Chatelet, Abdelkader Zeblah. Multi-Objective Electrical Power System Design Optimization Using a Modified Bat Algorithm. Energies, MDPI, 2021, 14 (13), pp.3956. ⟨10.3390/en14133956⟩. ⟨hal-03321084⟩

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