Optimal Design of Fast Charging Stations for Multi-Vehicles Low-Density Housing

This paper proposes a new multi-objective optimal design and energy management framework for low density housing Fast Charging Stations (FCS) in multi-EV households. The system integrates Renewable Energy Sources (RES) in the form of Photovoltaic (PV) and Hybrid Battery-Supercapacitor Energy Storage Systems (HBSC ESS) to reduce both annual Capital (CAPEX) and Operational Expenditures (OPEX), while ensuring Electric Vehicle (EV) user convenience. The proposed framework is formulated as a Mixed-Integer Nonlinear Programming (MINLP) problem, and incorporates several critical and practical system constraints including 1) power ramp limits of distribution transformers and ESS, 2) EVs fast-charging behavior modeled through State-of-Power (SoP) versus State-of-Charge (SoC) curves, and 3) transformer thermal capacity constraints based on IEEE C57.91 standard. The framework leverages high-resolution time intervals of 10-sec to capture rapid power variations in generation and load profiles, enabling accurate converter and SC sizing. A detailed sensitivity analysis was conducted across various factors, including daily driven mileage, SoP-SoC characteristics, number of EVs per household, SoC mismatch penalties, transformer thermal overload limits, and different time interval resolution. Results show that neglecting the SoP-SoC relation leads to innacurate design and overestimation of achievable departing SoC, adversely affecting scheduling decisions. Additionally, utilizing transformer thermal capacity allowed a 3.5–4.5% reduction in transformer rating without compromising performance. The number of EVs significantly influenced system design, where coordination becomes critical as the household EV count increases. Additionally, utilizing dual objective allows to maximize user convenience while prevents oversizing the FCS, distribution transformer and HBSC ESS.