基于削峰填谷和用户收益的电动汽车充放电电价优化模型

Optimization Model for Electric Vehicle Charging and Discharging Electricity Prices Based on Load Leveling and User Benefits

摘要: 为解决电动汽车在无引导机制下入网导致的负荷随机性变化问题，凸显电动汽车与电网互动技术(vehicle-to-grid, V2G)的实际价值，以削峰填谷和用户收益为目的，建立了电动汽车充放电电价优化模型。充放电电价作为首要因素，是引导用户参与V2G的关键。由于缺乏价格引导机制，用户难以规律性地参与V2G，随机性较强，严重威胁系统安全和电网的稳定运行。所以，合理的充放电电价策略不仅有利于系统负荷削峰填谷，减少充电系统电网压力，同时降低用户经济成本，使得用户侧和电网侧达到双赢的结果。首先，通过分析电动汽车参数差异以及用户出行习惯，使用蒙特卡洛法建立电动汽车充放电功率模型，提出电动汽车在无序模式下规模化接入电网不利于系统运行；接着，引入价格弹性因子，计算出实施峰谷电价前后的充放电电量。仿真结果表明，所设计的电动汽车峰谷分时充放电电价优化策略不仅显著降低了系统负荷的峰谷差率，实现削峰填谷，同时通过合理分配用户充放电行为的收益与成本，有效保障了用户侧的经济效益。

Abstract: To address the issue of random load fluctuations caused by electric vehicles (EVs) connecting to the grid without a guiding mechanism, and to highlight the practical value of vehicle-to-grid (V2G) interaction technology, an optimized electricity pricing model for EV charging and discharging was established with the goals of load leveling and maximizing user benefits. The charging and discharging electricity price is the primary factor guiding user participation in V2G. Due to the lack of a price guidance mechanism, user participation in V2G tends to be irregular and highly random, posing significant threats to system security and grid stability. Therefore, a reasonable electricity pricing strategy for charging and discharging not only facilitates load leveling, reducing grid pressure, but also lowers the economic costs for users, achieving a win-win outcome for both the user side and the grid side. Firstly, by analyzing the differences in EV parameters and user travel habits, a charging and discharging power model for EVs is established using the Monte Carlo method. It is proposed that the large-scale integration of EVs into the grid in an uncoordinated manner is detrimental to system operation. Subsequently, a price elasticity factor is introduced to calculate the charging and discharging of electricity before and after the implementation of time-of-use (TOU) pricing. Simulation results demonstrate that the proposed TOU pricing optimization strategy for EV charging and discharging significantly reduces the peak-to-valley load difference, achieving effective load leveling. Additionally, by reasonably allocating the benefits and costs of user charging and discharging behaviors, the strategy effectively ensures economic benefits for the user side.