Particle swarm optimization for micro-grid power management and load scheduling
(2020) In International Journal of Energy Economics and Policy 10(2). p.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... (More)
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.
(Less)
- author
- Kerboua, Abdelfettah ; Boukli-Hacene, Fouad and Mourad, Khaldoon A. LU
- organization
- publishing date
- 2020
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Operating Cost, Power Management, Renewable Energy, S: Particle Swarm Optimization Algorithm
- in
- International Journal of Energy Economics and Policy
- volume
- 10
- issue
- 2
- pages
- 10 pages
- publisher
- EconJournals
- external identifiers
-
- scopus:85078331360
- ISSN
- 2146-4553
- DOI
- 10.32479/ijeep.8568
- language
- English
- LU publication?
- yes
- id
- ed5b2a46-91c6-4f24-abd2-ff68c0f001d3
- date added to LUP
- 2020-02-10 13:31:50
- date last changed
- 2023-10-08 00:14:21
@article{ed5b2a46-91c6-4f24-abd2-ff68c0f001d3,
abstract = {{<p>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.</p>}},
author = {{Kerboua, Abdelfettah and Boukli-Hacene, Fouad and Mourad, Khaldoon A.}},
issn = {{2146-4553}},
keywords = {{Operating Cost; Power Management; Renewable Energy; S: Particle Swarm Optimization Algorithm}},
language = {{eng}},
number = {{2}},
pages = {{71--80}},
publisher = {{EconJournals}},
series = {{International Journal of Energy Economics and Policy}},
title = {{Particle swarm optimization for micro-grid power management and load scheduling}},
url = {{http://dx.doi.org/10.32479/ijeep.8568}},
doi = {{10.32479/ijeep.8568}},
volume = {{10}},
year = {{2020}},
}