基于电动汽车无序充电行为的可再生能源系统综合评估及调控策略研究

doi:10.19799/j.cnki.2095-4239.2025.0015

摘要/Abstract

摘要：

随着电动汽车渗透率的持续增长，大规模电动汽车接入会对电网稳定性造成一定影响。为了更好地消纳波动性较强的可再生能源，同时满足不确定的电动汽车充电需求，本研究将储能技术与风力、光伏、电网技术相结合，并构建了一套可再生能源系统。通过蒙特卡洛抽样方法刻画电动汽车的无序充电行为，将MATLAB与TRNSYS进行耦合并构建系统动态仿真模型。提出了衡量系统性能的评估框架，评估了系统在不同时间跨度、不同技术形式、不同调控策略下的综合性能。最后基于3种系统调控策略进行了分析。结果表明，在引入储能技术后，系统全年的能量匹配性指标最大提升48.20%，灵活性指标最大降低37.77%，环境效益指标最大降低6.59%。通过有序充电调控，系统的现场能量比(OEF)为66.71%，现场能量匹配(OEM)为73.20%，电网集成水平(GIL)为33.29%，电网净交互水平(NIL)为52.63%。该技术形式可有效实现负荷调控并缓解电网压力，改善电动汽车负荷扰动所带来的供需不匹配问题，降低了需求侧对电网的依赖程度。

关键词: 电动汽车, 可再生能源, 蒙特卡洛, 储能, TRNSYS

Abstract:

As the penetration rate of electric vehicles (EVs) increases, their widespread integration significantly affects the power grid stability. To better absorb highly volatile renewable energy and manage uncertain EV charging demands, this study proposed a renewable energy system. The proposed system combines energy storage technology with wind power, photovoltaic power, and grid technology to create a renewable energy system. The disordered charging behavior of EVs was characterized using the Monte Carlo sampling method, and MATLAB was coupled with TRNSYS to build a dynamic system simulation model. An evaluation framework was proposed to comprehensively evaluate system performance across different time scales, technical forms, and control strategies. The analysis was based on three system control strategies. The results demonstrate that the system's annual energy matching index increased by up to 48.20%, its flexibility index decreased by up to 37.77%, and the environmental benefit index decreased by up to 6.59% after the introduction of energy storage technology. Through an orderly charging strategy, the system demonstrated strong performance metrics: an on-site energy fraction of 66.71%, on-site energy matching of 73.20%, grid integration level of 33.29%, and net interaction level of 52.63%. The proposed system effectively regulates loads, relieves pressure on the power grid, improves supply-demand mismatches due to EV loads, and decreases demand-side dependence on the power grid.

Key words: electric vehicles, renewable energy, Monte Carlo, energy storage, TRNSYS

中图分类号:

TK 519

刘彦廷, 冯国会, 常莎莎, 程昱茜, 丁雨鸣. 基于电动汽车无序充电行为的可再生能源系统综合评估及调控策略研究[J]. 储能科学与技术, 2025, 14(7): 2772-2781.

Yanting LIU, Guohui FENG, Shasha CHANG, Yuqian CHENG, Yuming DING. Research on comprehensive evaluation and regulation strategy of renewable energy system based on disordered charging behavior of electric vehicles[J]. Energy Storage Science and Technology, 2025, 14(7): 2772-2781.

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结构类型	传热系数/[W/(m²·K)]
外墙	0.10
屋顶	0.10
地面	0.12
外窗	1.00

部件	参数设置
光伏阵列	峰值电压40.9 V，峰值电流13.45 A，光伏板数量7×8块，峰值功率550 W
风力发电机	额定功率20 kW
储能系统	蓄电池电量132.46 kWh，储能水箱容量7 m³
热泵机组	额定制冷功率19.76 kW，额定制热功率27.65 kW

策略	OEF/%	OEM/%	GIL/%	NIL/%
STR1	62.21	72.00	37.79	65.85
STR2	52.76	63.31	47.24	83.76
STR3	66.71	73.20	33.29	52.63