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Energy Management Strategy and Capacity Planning of an Autonomous Microgrid: A Comparative Study of Metaheuristic Optimization Searching Techniques

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dc.contributor.authorBukar A.L.en_US
dc.contributor.authorTan C.W.en_US
dc.contributor.authorLau K.Y.en_US
dc.contributor.authorToh C.L.en_US
dc.contributor.authorAyop R.en_US
dc.contributor.authorDahiru A.T.en_US
dc.contributor.authorid56971314400en_US
dc.contributor.authorid35216732200en_US
dc.contributor.authorid37665178700en_US
dc.contributor.authorid8690228000en_US
dc.contributor.authorid57193828123en_US
dc.contributor.authorid57211084199en_US
dc.date.accessioned2023-05-29T09:10:09Z
dc.date.available2023-05-29T09:10:09Z
dc.date.issued2021
dc.descriptionBiomimetics; Electric power transmission; Energy management; Fossil fuels; MATLAB; Particle swarm optimization (PSO); Renewable energy resources; Charging energies; Energy management schemes; Energy-based; Metaheuristic; Metaheuristic optimization; Microgrid; Optimal sizing; PV; Renewable energies; Searching techniques; Carbon dioxideen_US
dc.description.abstractElectricity generation using renewable energy-based microgrid (REM) is a prerequisite to achieve one of the cardinal objectives of sustainable development goals. Nonetheless, the optimum design and sizing of the REM is challenging. This is because the REM needs to supply the fluctuating demand considering the sporadic behaviour of the renewable energy sources (RES). This paper, therefore, proposes a nature-inspired metaheuristic optimization searching technique (MOST) to optimize the components of an autonomous microgrid integrating a diesel generator {\left(D_{\text{GEN}}\right)}, battery bank, photovoltaic and wind turbine. In this regard, a cycle-charging energy management scheme (CEMS) control is proposed and implemented using a rule-based algorithm. The proposed CEMS provide a power delivery sequence for the different components of the microgrid. Subsequently, the CEMS is optimized using the metaheuristic optimization searching techniques (MOSTs). To benchmark, the paper compares the success of six different MOSTs. The simulation is performed for the climatic conditions of Yobe State, in northern Nigeria using MATLAB software. The comparative results show that the grasshopper optimization algorithm is found to yield a better result because it gives the least fitness function relative to other studied MOSTs. Remarkably, it outperforms the grey wolf optimizer, the ant lion optimizer, and the particle swarm optimization by ? 3.0 percent, ? 5.8 percent, and ? 3.6 percent (equivalent to a cost savings of 8332.38, 4219.87, and 5144.64 from the target microgrid project). Results also indicate that the proposed CEMS adopted for the microgrid control strategy has led to the implementation of a clean and affordable energy system, as it's significantly minimized CO2 (by 92.3%), fuel consumption (by 92.4%), compared fossil fuel-based {D_{\text{GEN}}}. � 2021 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/CENCON51869.2021.9627311
dc.identifier.epage195
dc.identifier.scopus2-s2.0-85123581364
dc.identifier.spage190
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85123581364&doi=10.1109%2fCENCON51869.2021.9627311&partnerID=40&md5=b45922b5485b61de564d856290c635a6
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26410
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle5th IEEE Conference on Energy Conversion, CENCON 2021
dc.titleEnergy Management Strategy and Capacity Planning of an Autonomous Microgrid: A Comparative Study of Metaheuristic Optimization Searching Techniquesen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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