Economic optimization of hybrid renewable energy resources for rural electrification

Isaiah G. Adebayo, Yanxia Sun, Umar Awal · 2024 · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering

Summary. Researchers used the bat algorithm to optimize hybrid renewable energy systems for rural electrification in Kalema village, comparing it against diesel-only and genetic algorithm approaches. The bat algorithm reduced energy costs by 45.6% and carbon emissions by 62.2% compared to diesel generators alone, outperforming the genetic algorithm on both metrics. This demonstrates that optimized hybrid renewable systems are more cost-effective and environmentally sustainable than traditional diesel generation for rural areas.

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@article{adebayo-2024-economic-optimization-hybrid-renewable-energy,
  title   = {Economic optimization of hybrid renewable energy resources for rural electrification},
  author  = {Isaiah G. Adebayo and Yanxia Sun and Umar Awal},
  journal = {International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering},
  year    = {2024},
  doi     = {10.11591/ijpeds.v15.i2.pp1147-1157},
  url     = {https://doi.org/10.11591/ijpeds.v15.i2.pp1147-1157}
}

TY  - JOUR
TI  - Economic optimization of hybrid renewable energy resources for rural electrification
AU  - Isaiah G. Adebayo
AU  - Yanxia Sun
AU  - Umar Awal
JO  - International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering
PY  - 2024
DO  - 10.11591/ijpeds.v15.i2.pp1147-1157
UR  - https://doi.org/10.11591/ijpeds.v15.i2.pp1147-1157
ER  - 

Details

DOI

10.11591/ijpeds.v15.i2.pp1147-1157

Countries

Nigeria, South Africa

Regions

Africa

Categories

energy, climate-and-environment

Added

2026-04-28