基于风电功率预测的电动汽车调价策略
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彭曙蓉(1975),女,博士,副教授,主要从事智能信息处理研究;Email:173764138@qq.com

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TM863

基金项目:

湖南省教育厅创新平台开放基金(17K001)


Wind Power Prediction Based on the Pricing Strategy of Electric Vehicle Charging
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    摘要:

    为了提高风电入网的稳定性,提出一种基于风电功率预测的电动汽车双阶段调价策略。该策略通过预测风电和调节电动汽车价格来提高电网对风电的消纳能力。预测阶段,采用对时间序列有记忆能力的LSTM神经网络来预测风电功率,并与时间序列预测做对比。定价阶段,以预测风电功率曲线与充电负荷曲线相似度高、充电成本小为目标函数建立调价优化模型,通过预测的风电功率制定价格,用价格调节负荷,让充电负荷量随时间贴近风电功率。最后,通过模拟得到电动汽车原始充电负荷曲线,求解调价优化模型后,将优化前后的充电负荷对比,后者更加贴近预测风电功率,证明了该策略的有效性。

    Abstract:

    In order to improve the power network stability involving the wind power, a twostage price adjustment strategy is proposed for electric vehicles based on the wind power prediction. This strategy promotes the wind power accommodation by predicting wind power and then regulating the price of electric vehicles. In the prediction stage, the LSTM neural network with a memory ability of time series is utilized to predict the wind power. At the pricing stage, an optimization model of price adjustment is established with an objective function of the high similarity between the predicted wind power curve and the charging load curve, and the small charging cost. The price is set based on the forecast wind power and then it is utilized to adjust the load so that the charging load is close to the wind power over time. Finally, a simulation is included to verify the effectiveness of the strategy. The charging load before and after optimization is compared. It is shown that the latter is closer to the prediction of wind power.

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彭曙蓉,黄士峻,李 彬,等.基于风电功率预测的电动汽车调价策略[J].电力科学与技术学报,2020,35(3):114-119.
PENG Shurong, HUANG Shijun, LI Bin, et al. Wind Power Prediction Based on the Pricing Strategy of Electric Vehicle Charging[J]. Journal of Electric Power Science and Technology,2020,35(3):114-119.

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  • 在线发布日期: 2020-09-14
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