基于用户信用指数的园区电动汽车充放电调度机制

doi:10.16183/j.cnki.jsjtu.2023.196

摘要/Abstract

摘要：

电动汽车用户人为提前结束充电的违约行为易对负荷聚集商及用户自身造成违约损失.因此,考虑违约行为的充放电调度机制至关重要.基于吴氏三维信用理论量化电动汽车用户信用,并从放电深度和充电优先级两个维度修正电动汽车充放电计划;同时,建立考虑区域配电网负荷波动和用户成本的多目标优化模型.以园区为例进行仿真计算,对比分析多种场景下仿真结果及不同违约规模和电动汽车渗透率对所提充放电调度机制的影响.结果表明:基于用户信用指数的充放电调度机制在平抑园区配电网负荷波动和提高用户充电体验上都具有优势,相比未考虑用户执行情况的有序充放电策略,该机制能够更好地适应电动汽车渗透率升高及违约规模扩大的场景.

关键词: 电动汽车, 有序放充电, 用户信用指数, 充电优先级, 三维信用理论

Abstract:

The default behavior of electric vehicle (EV) users to end charging in advance is easy to cause default losses to load aggregators and users themselves. Therefore,it is crucial to dispatch charging behavior rationally considering default behavior. This paper quantifies EV user credit based on Wu’s three-dimensional credit theory, and revises EV charging and discharging plan in the dimensions of discharge depth and charging priority. In addition, it establishes a multi-objective optimization model considering the load fluctuation of regional distribution network and the user cost. Using a park as an example for simulation, it compares and analyzes the effects of different user default rates and EV permeability on the proposed multi-dimensional modified charging and discharging strategy. The results show that the charging and discharging scheduling mechanism based on the user credit index has advantages in calming the load fluctuation of the distribution network in the park and improving the charging experience of users. Compared with the orderly charging and discharging strategy which does not consider the charging and discharging execution of users, the proposed mechanism can better adapt to the scenario of increasing penetration rate of EV and expanding default scale.

Key words: electric vehicle (EV), orderly charging and discharging, user credit index, charging priority, three dimensional credit theory

中图分类号:

TM732
U491.8

孙欣, 江海林, 谢敬东, 王思敏, 王森. 基于用户信用指数的园区电动汽车充放电调度机制[J]. 上海交通大学学报, 2025, 59(2): 200-207.

SUN Xin, JIANG Hailin, XIE Jingdong, WANG Simin, WANG Sen. Charging and Discharging Scheduling Mechanism of Electric Vehicles in Park Based on User Credit Index[J]. Journal of Shanghai Jiao Tong University, 2025, 59(2): 200-207.

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参数	场景1	场景2	场景3
负荷均方差/kW	155.78	83.22	83.41
充电费用/元	1170.20	1047.79	1064.91
放电收入/元	0	37.25	35.15
总费用/元	1170.20	1010.54	1029.76
应充电量/(kW·h)	1332.20	1332.20	1332.20
未充电量/(kW·h)	0	243.04	111.53

违约率/%	充电满足率/%
违约率/%	场景2	场景3
5	96.33	97.41
20	81.76	91.63
30	75.11	89.68

用户	充电满足率/%	预计放电收入/元	实际放电收入/元
违约	40.84	11.18	4.37
未违约	100	30.78	30.78