@article{Regis_Muriithi_Ngoo_2019, place={Greece}, title={Optimal Battery Sizing of a Grid-Connected Residential Photovoltaic System for Cost Minimization using PSO Algorithm}, volume={9}, url={https://etasr.com/index.php/ETASR/article/view/3094}, DOI={10.48084/etasr.3094}, abstractNote={<p style="text-align: justify;">This paper proposes a new optimization technique that uses Particle Swarm Optimization (PSO) in residential grid-connected photovoltaic systems. The optimization technique targets the sizing of the battery storage system. With the liberation of power systems, the residential grid-connected photovoltaic system can supply power to the grid during peak hours or charge the battery during non-peak hours for later domestic use or for selling back to the grid during peak hours. However, this can only be achieved when the battery energy system in the residential photovoltaic system is optimized. The developed PSO algorithm aims at optimizing the battery capacity that will lower the operation cost of the system. The computational efficiency of the developed algorithm is demonstrated using real PV data from Strathmore University. A comparative study of a PV system with and without battery energy storage is carried out and the simulation results demonstrate that PV system with battery is more efficient when optimized with PSO.</p>}, number={6}, journal={Engineering, Technology & Applied Science Research}, author={Regis, N. and Muriithi, C. M. and Ngoo, L.}, year={2019}, month={Dec.}, pages={4905–4911} }