Influence of PV Technologies on Optimal Battery Capacity and System Cost in Hybrid PV–Wind Energy Systems: An Optimization-Based Perspective on System Performance

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The increasing penetration of renewable energy has intensified the need for hybrid systems that can ensure reliable power supply while minimizing storage and system costs. In hybrid PV–wind–battery configurations, photovoltaic (PV) technology is commonly treated as a fixed component, despite its strong influence on system dynamics and storage behavior. This study advances existing research by explicitly modeling PV technology as a system-level optimization variable and quantifying its impact on battery sizing and techno-economic performance. Five PV technologies, monocrystalline silicon, polycrystalline silicon, amorphous silicon, cadmium telluride (CdTe), and copper indium gallium selenide (CIGS), are analyzed under identical load, wind-resource, and cost assumptions using the HOMER Pro optimization model. The results reveal that PV technology significantly reshapes system architecture, with optimal PV capacities ranging from 725 to 828 kW, battery storage requirements varying from 5,777 to 6,632 kWh, and converter capacities between 571 and 711 kW. This corresponds to a variation of approximately 15% in optimal battery capacity across technologies, indicating a substantial impact on system cost and investment planning. Although wind dominates annual energy supply in all cases, PV energy contribution ranges from 1.04 to 1.32 GWh/year, leading to unmet load values below 0.06% across all configurations. Under the adopted uniform PV-cost and economic assumptions, crystalline silicon technologies (mono-Si and poly-Si) achieve the lowest system cost (net present cost (NPC) = $3.50 million; levelized cost of energy (LCOE) = $0.100/kWh), while the CIGS configuration results in the highest cost (NPC = $3.62 million; LCOE = $0.104/kWh). The findings reveal that battery sizing is governed by temporal power mismatch rather than annual energy yield, challenging conventional assumptions in hybrid system design. Overall, this study provides a comparative optimization-based framework linking PV technology characteristics to storage requirements and cost outcomes, offering new insights for cost-effective and reliable hybrid renewable energy system design.