Particle Swarm Optimization for Micro-Grid Power Management and Load Scheduling

Abdelfettah Kerboua, Fouad Boukli-Hacene and Khaldoon A Mourad
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Abdelfettah Kerboua: Department of Second Cycle, High School in Applied Sciences of Tlemcen, BP 165 RP Bel Horizon, 13000 Tlemcen, Algeria,
Fouad Boukli-Hacene: Department of Second Cycle, High School in Applied Sciences of Tlemcen, BP 165 RP Bel Horizon, 13000 Tlemcen, Algeria,
Khaldoon A Mourad: The Center for Sustainable Visions www.c4sv.com and Lund University, 22100 Lund, Sweden.

International Journal of Energy Economics and Policy, 2020, vol. 10, issue 2, 71-80

Abstract: A smart power management strategy is needed to economically manage local production and consumption while maintaining the balance between supply and demand. Finding the best-distributed generators' set-points and the best city demand scheduling can lead to moderate and judicious use out of critical moments without compromising smart city residents' comfort. This paper aimed at applying the Particle Swarm Optimization (PSO) to minimize the operating cost of the consumed energy in a smart city supplied by a micro-grid. Two PSO algorithms were developed in two steps to find the optimal operating set-points. The first PSO algorithm led to the optimal set-points powers of all micro-grid generators that can satisfy the non-shiftable needs of the smart city demand with a low operating cost. While the second PSO algorithm aimed at scheduling the shiftable city demand in order to avoid peak hours when the operating cost is high. The results showed that the operating costs during the day were remarkably reduced by using optimal distributed generators' set-points and scheduling shiftable loads out of peaks hours. To conclude, the main advantages of the proposed methodology are the improvement in the local energy efficiency of the micro-grid and the reduction in the energy consumption costs.

Keywords: PSO algorithm; renewable energy; power management; operating cost. (search for similar items in EconPapers)
JEL-codes: C61 C62 Q21 Q42 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eco:journ2:2020-02-10

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