Abstract
Fast charging stations enable the high-powered rapid recharging of electric vehicles. However, these stations also face challenges due to power fluctuations, high peak loads, and low load factors, affecting the reliable and economic operation of charging stations and distribution networks. This paper introduces a battery energy storage system (BESS) for charging load control, which is a more user-friendly approach and is more robust to perturbations. With the goals of peak-shaving, total electricity cost reduction, and minimization of variation in the state-of-charge (SOC) range, a BESS-based bi-level optimization strategy for the charging load regulation of fast charging stations is proposed in this paper. At the first level, a day-ahead optimization strategy generates the optimal planned load curve and the deviation band to be used as a reference for ensuring multiple control objectives through linear programming, and even for avoiding control failure caused by insufficient BESS energy. Based on this day-ahead optimal plan, at a second level, real-time rolling optimization converts the control process to a multistage decision-making problem. The predictive control-based real-time rolling optimization strategy in the proposed model was used to achieve the above control objectives and maintain battery life. Finally, through a horizontal comparison of two control approaches in each case study, and a longitudinal comparison of the control robustness against different degrees of load disturbances in three cases, the results indicated that the proposed control strategy was able to significantly improve the charging load characteristics, even with large disturbances. Meanwhile, the proposed approach ensures the least amount of variation in the range of battery SOC and reduces the total electricity cost, which will be of a considerable benefit to station operators.
Highlights
To address the energy and environmental crises, and as a significant component of sustainable development and the low-carbon economy, transport electrification, including electric vehicles (EVs), is experiencing a worldwide period of rapid development
This study was motivated by problems seen in fast charging stations, in which the charging loads suffer from severe power fluctuations, high peak loads, and low load factors
As the first level of the bi-level optimization strategy, the day-ahead level is implemented in the the energy management system (EMS) of the fast charging station, based on the day-ahead charging energy management system (EMS) of the fast charging station, based on the day-ahead charging load load forecasting data obtained from a load forecasting system and other constraint information
Summary
Graber et al [27] presented a stochastic sizing method to determine the number of different types of EV charging stations in urban parking lots while considering BESS These studies focused on the design and configuration of the charging station rather than real-time operation and load control. We propose a BESS-based bi-level optimization strategy for regulating the charging load of the electric vehicle fast charging station. In the following real-time strategy, since the BESS state has typical Markov properties in real-time operation, model predictive control (MPC) was used to optimize the rolling of the BESS power in a fast charging station During this real-time optimization process, and based on the day-ahead optimal planned load deviation band set at the first level, the variation in the BESS state-of-charge (SOC) range was optimized to improve the expected battery life.
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