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Advancing sustainable EV charging infrastructure: A hybrid solar-wind fast charging station with demand response

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  • He, Li
  • Wu, Zhixin

Abstract

This study aims to design an efficient hybrid solar-wind fast charging station with an energy storage system (ESS) to maximize station efficiency and reduce grid dependence. The research employs Monte Carlo simulation to capture renewable energy source (RES) uncertainties, models stochastic electric vehicles (EVs) driver behavior and fleet diversity, and utilizes an Erlang B queuing model for EV load demand estimation. A hybrid optimization approach combining the Binary-Gravitational Search Optimization Algorithm (B-GOA) and the Non-dominated Crowding Sort Optimization Algorithm (NCSOA) is implemented for efficient optimization in a combined binary-continuous solution space. Results demonstrate superior performance of the proposed approach compared to existing methods, with high-RES penetration significantly reducing grid reliance. This study contributes to advancing sustainable EV charging infrastructure development and enhancing overall grid stability through improved load flexibility and demand response management. The analysis reveals Scenario IV as the most economically viable, with the highest Net Present Value of 1,025,895.32€. Most scenarios favor 5 chargers (44–46 kW) and 4 Type 3 wind generators. Battery capacity varies widely (108–372 kWh), as does grid connection (0–292 kW). Despite varying initial investments, Scenarios II-VII show a consistent 4-year Internal Rate of Return, indicating good economic potential across different configurations.

Suggested Citation

  • He, Li & Wu, Zhixin, 2024. "Advancing sustainable EV charging infrastructure: A hybrid solar-wind fast charging station with demand response," Renewable Energy, Elsevier, vol. 237(PC).
    • Handle: RePEc:eee:renene:v:237:y:2024:i:pc:s0960148124019116
    DOI: 10.1016/j.renene.2024.121843
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