| 引用本文: | 王浩林,张勇军,毛海鹏.基于时刻充电概率的电动汽车充电负荷预测方法[J].电力自动化设备,2019,39(3): |
| WANG Haolin,ZHANG Yongjun,MAO Haipeng.Charging load forecasting method based on instantaneous charging probability for electric vehicles[J].Electric Power Automation Equipment,2019,39(3): |
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| 摘要: |
| 电动汽车的充电负荷预测在电动汽车的推广过程中发挥着重要的作用。为了克服现有方法中部分参数设置主观、预测模型与用户随机性驾驶行为匹配欠缺的不足，将电动汽车进行细致分类，通过建立充电负荷预测影响因素的概率模型，利用概率统计学和蒙特卡洛模拟方法提出了基于时刻充电概率的负荷预测模型。利用科学分析得到的日行驶里程代替主观给定的起始电荷状态(SOC)以推导充电时长，利用更具随机性的时刻充电概率代替计算得到的充电时段来确定充电负荷。以某市为例，预测了相关电动汽车的日负荷曲线，并与常用负荷预测方法的结果进行对比，验证了所提负荷预测方法能够科学地预测用户的充电负荷，能够为电网及用户的电能管理策略提供可靠的依据。 |
| 关键词: 电动汽车 充电负荷预测 概率模型 蒙特卡洛方法 时刻 模型 |
| DOI：10.16081/j.issn.1006-6047.2019.03.033 |
| 分类号:U469.72;TM714 |
| 基金项目:国家自然科学基金资助项目(51777077)；广东省自然科学基金资助项目(2017A030313304) |
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| Charging load forecasting method based on instantaneous charging probability for electric vehicles |
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WANG Haolin, ZHANG Yongjun, MAO Haipeng
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School of Electric Power, South China University of Technology, Guangzhou 510640, China
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| Abstract: |
| The charging load forecasting of EVs(Electric Vehicles) plays an important role in the promotion of EVs. In order to overcome the shortcomings of subjective setting of some parameters and lack of matching between the forecasting model and the random driving behaviors of EV users, the EVs are particularly classified, the probability model of influencing factor for charging load forecasting is established, and the charging load forecasting method based on instantaneous charging probability is proposed by using the probability statistics and Monte Carlo simulation method. The charging duration is derived by the daily mileage obtained by scientific analysis instead of the subjectively given initial SOC(State Of Charge) and the charging load is determined by using the more random instantaneous charging probability instead of the calculated charging period. Taking a city as the example, the daily charging load curve of related EVs is forecasted and compared with the results of common load forecasting methods, which verifies that the proposed load forecasting method can scientifically forecast the EV users’ charging load and provide a reliable basis for power management strategy for power grid and users. |
| Key words: electric vehicles charging load forecasting probability model Monte Carlo method time models |
