Optimization and management of solar and wind production for standalone microgrid: a Moroccan case study

The increasing demand for sustainable and efficient energy solutions has prompted extensive research into optimizing renewable energy sources in microgrid systems. This paper focuses on optimizing renewable energy sources within a standalone microgrid using particle swarm optimization (PSO) as the sole algorithm. The microgrid model proposed integrates photovoltaic (PV), wind, battery storage, and serves a load represented by an agricultural firm. Real-world data from Agdz in Ouarzazate, Morocco, is utilized for analysis. The primary objective is to minimize excess production from PV and wind sources when the battery reaches full charge. This research addresses the increasing demand for sustainable energy solutions by emphasizing a single optimization technique, PSO, for achieving a balanced and efficient energy generation system. The study aims to closely align energy production with load demand to reduce wastage and ensure a reliable energy supply within the microgrid. The evaluation is conducted based on the ability of the PSO algorithm to diminish the gap between total energy production and load demand. The use of the PSO algorithm resulted in a 30% reduction in excess energy, effectively mitigating unnecessary energy wastage when the battery is fully charged. This outcome highlights the algorithm's capacity to adapt and optimize energy production from primary sources to precisely align with the specific requirements of the load

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Publication Details

Journal
International Journal of Applied Power Engineering (IJAPE)
Published
2024-12-13
DOI
https://doi.org/10.11591/ijape.v14.i1.pp202-211
Primary Topic
Microgrid Control and Optimization
Type
article
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article

Optimization and management of solar and wind production for standalone microgrid: a Moroccan case study

Rachid Alaoui, Mohamed El Hafydy, Youssef Oubail, Lahoussine Elmahni et al.
International Journal of Applied Power Engineering (IJAPE)
Microgrid Control and Optimization
article

Optimization and management of solar and wind production for standalone microgrid: a Moroccan case study

Rachid Alaoui, Mohamed El Hafydy, Youssef Oubail, Lahoussine Elmahni, Mohamed Benydir
article en

Abstract

The increasing demand for sustainable and efficient energy solutions has prompted extensive research into optimizing renewable energy sources in microgrid systems. This paper focuses on optimizing renewable energy sources within a standalone microgrid using particle swarm optimization (PSO) as the sole algorithm. The microgrid model proposed integrates photovoltaic (PV), wind, battery storage, and serves a load represented by an agricultural firm. Real-world data from Agdz in Ouarzazate, Morocco, is utilized for analysis. The primary objective is to minimize excess production from PV and wind sources when the battery reaches full charge. This research addresses the increasing demand for sustainable energy solutions by emphasizing a single optimization technique, PSO, for achieving a balanced and efficient energy generation system. The study aims to closely align energy production with load demand to reduce wastage and ensure a reliable energy supply within the microgrid. The evaluation is conducted based on the ability of the PSO algorithm to diminish the gap between total energy production and load demand. The use of the PSO algorithm resulted in a 30% reduction in excess energy, effectively mitigating unnecessary energy wastage when the battery is fully charged. This outcome highlights the algorithm's capacity to adapt and optimize energy production from primary sources to precisely align with the specific requirements of the load

International Journal of Applied Power Engineering (IJAPE)Vol. 14(1)
Université Ibn Zohr (MA)
Openalex Percentile: Top 73%
Microgrid Control and Optimization
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Optimization and management of solar and wind production for standalone microgrid: a Moroccan case study — Rachid Alaoui, Mohamed El Hafydy, et al. · International Journal of Applied Power Engineering (IJAPE) (2024) | TGRS Research Map | TGRS