| 引用本文: | 牛牧童,廖 凯,杨健维,等.考虑季节特性的多时间尺度电动汽车负荷预测模型[J].电力系统保护与控制,2022,50(5):74-85.[点击复制] |
| NIU Mutong,LIAO Kai,YANG Jianwei,et al.Multi-time-scale electric vehicle load forecasting model considering seasonal characteristics[J].Power System Protection and Control,2022,50(5):74-85[点击复制] |
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| 摘要: |
| 当前对电动汽车(Electric Vehicle, EV)充电负荷预测的研究大多集中在短期单一时间尺度，且鲜有考虑在较长时间尺度下不同季节电动汽车充电负荷存在的差异。基于此，提出一种考虑季节特性的多时间尺度电动汽车负荷预测模型。首先，考虑季节特性对EV的电池最大载电量、里程耗电量和空调耗电量的影响，结合时空分布规律建立短期日内的电动汽车充电负荷预测模型。其次，为了展现从短期(短时间尺度)到中长期(长时间尺度)的多时间尺度特性，建立考虑多种因素影响的Bass修正模型预测未来不同年份的EV保有量。结合短期EV充电负荷预测模型，可延展至中长期EV充电负荷的预测，从而实现综合短期、中长期的多时间尺度EV负荷预测。最后，采用上海市气温信息及行车数据进行仿真验证。结果表明，所提模型可以有效地预测未来数年EV发展趋势以及考虑季节特性的多时间尺度EV充电负荷。 |
| 关键词: 电动汽车 负荷预测 Bass模型 多时间尺度 季节特性 |
| DOI：DOI: 10.19783/j.cnki.pspc.210628 |
| 投稿时间：2021-05-25修订日期：2021-08-12 |
| 基金项目:国家自然科学基金项目资助(51977180) |
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| Multi-time-scale electric vehicle load forecasting model considering seasonal characteristics |
| NIU Mutong,LIAO Kai,YANG Jianwei,XIANG Yueping |
| (School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China) |
| Abstract: |
| Current research on electric vehicle (EV) charging load forecasting is mostly focused on a short-term single time-scale, and few consider the differences of electric vehicle charging load in different seasons on a longer time scale. Therefore, a multi-time-scale electric vehicle load forecasting method considering seasonal characteristics is proposed. First, a short-term day-to-day electric vehicle charging load prediction model is established taking into account the influence of seasonal characteristics on the initial battery power, mileage power and air-conditioning power consumption of the EV, and combining time and space distribution rules. Secondly, in order to show the characteristics of multiple time scales from short-term (within a day) to medium-and long-term (years), a modified Bass model that takes into account the influence of multiple factors is built to predict the EV holdings in different years in the future. Combined with short-term EV, the charging load forecasting model can be extended to mid-to-long-term EV charging load forecasting, thereby achieving multi-time-scale EV load forecasting and integrating short-term and mid-to-long-term. Finally, through simulation verification with the temperature information and driving data of Shanghai, the results demonstrate that the proposed model is able to effectively predict the EV development trend over the next few years and the EV charging load under multiple time scales considering seasonal characteristics.
This work is supported by the National Natural Science Foundation of China (No. 51977180). |
| Key words: electric vehicle load forecasting Bass model multi-time-scale seasonal characteristics |
