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Optimal planning of solar PV-based electric vehicle charging stations empowered by energy storage system: Feasibility and green charge potential

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  • Ozturk, Zafer
  • Demirci, Alpaslan
  • Terkes, Musa
  • Yumurtaci, Recep

Abstract

The rapid growth of electric vehicle (EV) adoption and declining photovoltaic (PV) costs have accelerated global efforts to integrate renewables into EV charging infrastructure. In emerging economies like Türkiye, these developments pose both opportunities and challenges due to limited PV installation areas, variable charging demand, and the capital-intensive nature of energy storage systems (ESS). This study presents a techno-economic and environmental optimization of hybrid solar-powered EV charging stations (EVCS) across 12 climatically diverse Turkish cities. Results show that with flexible PV sizing and moderate demand, grid dependency can be reduced by up to 66.7%, while the renewable fraction (RF) can reach 89%. In high-irradiance regions (>4.5 kWh/m2/day), ESS becomes economically viable below $150/kWh, achieving discount payback periods under 10 years. Conversely, in areas with limited space and high demand, RF may drop to 16%, increasing grid reliance and CO2 emissions. Cities like İzmir and Gaziantep demonstrate 23% lower energy costs than less favorable locations like Trabzon, emphasizing climate impacts. The study offers a replicable framework for planning efficient, low-carbon EVCS tailored to regional conditions, supporting policymakers, utilities, and investors in advancing Türkiye’s energy transition and decarbonization strategies.

Suggested Citation

  • Ozturk, Zafer & Demirci, Alpaslan & Terkes, Musa & Yumurtaci, Recep, 2025. "Optimal planning of solar PV-based electric vehicle charging stations empowered by energy storage system: Feasibility and green charge potential," Renewable Energy, Elsevier, vol. 255(C).
    • Handle: RePEc:eee:renene:v:255:y:2025:i:c:s0960148125013771
    DOI: 10.1016/j.renene.2025.123715
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    2. Chou, Jui-Sheng & Krang, Ja & Limantono, Dani Nugraha, 2026. "Regional solar generation prediction with metaheuristically optimized artificial intelligence for sustainable grid management," Renewable Energy, Elsevier, vol. 257(C).

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