研究目的
To propose a framework for the operational management of a shared EV fleet, integrating local PV generation and battery storage, to minimize electricity costs, satisfy reservations, and comply with grid limits.
研究成果
The proposed framework effectively coordinates EV charging and reservation assignments with local PV and battery storage, minimizing costs and ensuring grid compliance. Monte Carlo simulations show that the utilization rate decreases with more EVs, providing insights for investment decisions in EV sharing communities. Dynamic assignment allows for adjusting reservations to serve more users, and the integration enhances the use of renewable energy.
研究不足
The study assumes specific data profiles (e.g., building load, PV generation, electricity prices) based on Swedish conditions, which may not be generalizable. The simulation uses simplified models for EV and battery behavior, and real-world uncertainties (e.g., weather variations, grid dynamics) are not fully captured. The framework relies on accurate real-time information, which could be challenging in practice.
1：Experimental Design and Method Selection:
The study uses a mixed integer linear programming (MILP) model to optimize charging schedules and reservation assignments for a shared EV fleet. Monte Carlo simulation is employed to handle stochastic travel demands.
2：Sample Selection and Data Sources:
Data includes Swedish travel patterns from surveys (e.g., SIKA and SCB reports), with travel types categorized by purpose and probability. Building load, PV generation, and electricity prices are assumed based on typical profiles.
3：List of Experimental Equipment and Materials:
EVs with 18.7 kWh batteries and 3.6 kW charging rates, a local battery with 28.8 kWh capacity and 24 kW charging/discharging rates, and simulation software for optimization and Monte Carlo methods.
4：7 kWh batteries and 6 kW charging rates, a local battery with 8 kWh capacity and 24 kW charging/discharging rates, and simulation software for optimization and Monte Carlo methods.
Experimental Procedures and Operational Workflow:
4. Experimental Procedures and Operational Workflow: The framework involves running the optimization algorithm over a 24-hour planning horizon with 15-minute time steps. For each reservation inquiry, the booking algorithm determines acceptance, and the charging schedule is updated dynamically.
5：Data Analysis Methods:
Results are analyzed through Monte Carlo simulations (10 trials with 100 samples each) to estimate the number of accepted reservations and utilization rates for different fleet sizes. Charging and discharging powers are plotted to demonstrate coordination.
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