Optimal Sizing of PV-Battery based Hybrid Renewable System using Particle Swarm Optimization for Economic Sustainability
Integrating energy storage (ES) such as batteries with renewable sources like photovoltaic (PV) systems offers eco-friendly power generation, but optimizing the scale of hybrid renewable systems (HRSs) is complex due to PV intermittency, discharge uncertainty, and economic factors. The article has p...
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| Format: | Conference Paper |
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Institute of Electrical and Electronics Engineers Inc.
2024
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| Summary: | Integrating energy storage (ES) such as batteries with renewable sources like photovoltaic (PV) systems offers eco-friendly power generation, but optimizing the scale of hybrid renewable systems (HRSs) is complex due to PV intermittency, discharge uncertainty, and economic factors. The article has proposed an optimal solution for a small-scale PV-battery-based hybrid renewable system aimed at improving economic sustainability using particle swarm optimization (PSO). The main objective is to minimize the levelized cost of energy (LCOE) while finding the optimal PV and battery sizes. By conducting simulations and analyses using MATLAB, the findings vividly illustrate the significant influence of PSO in reducing the overall LOCE of 80.36%. Through iterative exploration and optimization of PV capacity, battery capacity, and power rating, the PSO algorithm achieves an optimal configuration, minimizing costs while meeting energy demands. The optimal configuration includes a 3.3kW of PV and a one kWh battery with an NPC of $24,974.29 and an LCOE of 0.011 $/kWh. The system has a renewable fraction (RF) of 100% with no CO2 emission. The PSO-driven method, based on real-world data on power demand, PV generation, and EV charging, demonstrates its novel impact on renewable energy system design, accelerating the transition to greener and more cost-effective energy solutions � 2023 IEEE. |
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