| Autor: |
Saheed Ayodeji Adetoro, Lanre Olatomiwa, Jacob Tsado, Solomon Musa Dauda |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
e-Prime: Advances in Electrical Engineering, Electronics and Energy, Vol 4, Iss , Pp 100140- (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2772-6711 |
| DOI: |
10.1016/j.prime.2023.100140 |
| Popis: |
The availability of affordable and reliable power supply fosters social and economic growth and raises the standard of living. In most developing nations, there is a considerable gap between energy supply and demand, often resulting in load shedding and blackouts. Integrating two or more renewable power sources is a potential solution for the inconsistent nature of renewable energy, thereby supplying clean and sustainable electricity. However, proper component sizing and operation planning for different system components are necessary for a reliable and cost-effective system. This paper compares the performance of three widely used optimisation techniques (Artificial Bee Colony (ABC), Genetic Algorithm (GA), and Particle Swarm Optimisation (PSO)) in determining the size of a hybrid renewable energy system (HRES) with the lowest levelised cost of energy (LCOE) to meet the energy needs of a dairy farm in a rural settlement. PSO is observed to be the best-performed algorithm proposing a system with an LCOE of $0.162 per kWh, a net present cost (NPC) of 2.05 million dollars and a payback period of 5 years and 7 months when compared with the existing power system. The proposed HRES is determined to reduce annual diesel usage by 96%. Therefore, significantly decreasing greenhouse gas (GHG) emissions. The PSO algorithm performs satisfactorily in terms of results and convergence time compared to the results from commercially available hybrid optimisation software (HOMER Pro). |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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