article · Engineering Technology & Applied Science Research
Liberalised power markets allow residential grid-connected solar setups to supply electricity to the grid or store energy during non-peak hours for later use or export. To capture these economic benefits, the battery storage component must be correctly sized. A new optimisation technique uses the Particle Swarm Optimisation algorithm to determine the optimal battery capacity for residential grid-connected photovoltaic systems with the specific goal of lowering operational costs. The method was evaluated using real photovoltaic generation data collected from Strathmore University. Comparative simulation results between solar systems with and without battery storage show that incorporating an appropriately sized battery through this optimisation approach improves overall system efficiency and cost performance.
Homes with solar panels often struggle to balance energy generation, domestic consumption, and grid sales cost-effectively. Sizing batteries appropriately allows households to store cheaper or excess energy and export it when electricity prices peak, significantly lowering ongoing energy costs while improving overall efficiency.
This technique could be applied by residential solar installers, energy management software providers, and microgrid planners to design more cost-effective solar-plus-storage systems. Tested through computational simulations using real operational data from Strathmore University, the work appears to be at an applied research stage requiring integration into commercial sizing tools before market deployment.
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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.
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DOI: 10.48084/etasr.3094
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