Optimal Infrastructure Design and Power Management for a Photovoltaic and Battery Assisted Electric Vehicle Charging Station in Southern California

Abstract

This paper presents a framework for the optimal design of a solar and battery assisted electric vehicle (EV) charging station in southern California, with a focus on maximizing long-term profits while addressing operational uncertainties. The problem is conceptualized as an iterative two-stage decision process. In Stage I, the sampled designs of station infrastructure, including the number of chargers, the size of photovoltaic (PV) array, and capacity of the battery energy storage system (BESS), are specified. In Stage II, the EV charging rule is designed and simulated based on the charging request and solar power datasets. A model predictive controller and an empirical rule-based approach with incoming car forecasts are developed and compared for the vehicle charging management. The simulated annual operational profit and infrastructure investment with the consideration of long-term battery degradation is synthesized to build response surface for better design exploration in Stage I. Our results show that the proposed rule-based approach is computationally more efficient and suitable to integrate with response surface methodology (RSM) for design optimization. In addition, RSM is compared with adaptive particle swarm optimization (PSO) with multiple trials to demonstrate its superiority in high-profit design.

Keywords

• Battery energy storage system
• model predictive control
• photovoltaic systems
• response surface methodology