基于风电功率预测的电动汽车调价策略
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作者:
通讯作者:

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

中图分类号:

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.

    参考文献
    [1] 钱政,裴岩,曹利宵,等.风电功率预测方法综述[J].高电压技术,2016,42(4):10471060.QIAN Zheng,PEI Yan,CAO Lixiao,et al.Review of wind power forecasting method[J].High Voltage Engineering.2016,42(4):10471060.
    [2] 陈昊,张建忠,许超,等.基于多重离群点平滑转换自回归模型的短期风电功率预测[J].电力系统保护与控制,2019,47(1):7379.CHEN Hao,ZHANG Jianzhong,XU Chao,et al.Shortterm wind power forecast based on MOSTAR model[J].Power System Protection and Control,2019,47(1):7379.
    [3] 刘强,胡志强,周宇,等.基于CEEMD和随机森林算法的短期风电功率预测[J].智慧电力,2019,47(6):7176+94.LIU Qiang,HU Zhiqiang,ZHOU Yu,et al.Shortterm wind power forecasting based on CEEMD and random forest algorithm[J].Smart Power,2019,47(6):7176+94.
    [4] 琚垚,祁林,刘帅.基于改进乌鸦算法和ESN神经网络的短期风电功率预测[J].电力系统保护与控制,2019,47(4):5864.JU Yao,QI Lin,LIU Shuai.Shortterm wind power forecasting based on improved crow search algorithm and ESN neural network[J].Power System Protection and Control,2019,47(4):5864.
    [5] 李应求,安勃,李恒通.基于NARX及混沌支持向量机的短期风速预测[J].电力系统保护与控制,2019,47(23):6573.LI Yingqiu,AN Bo,LI Hengtong.Shortterm wind speed prediction based on NARX and chaossupport vector machine[J].Power System Protection and Control,2019,47(23):6573.
    [6] Wan C,Xu C,Pinson P,et al.Probabilistic forecasting of wind power generation using extreme learning machine[J].IEEE Trans.Power Systems 2014,29(3):10331044.
    [7] 朱抗,杨洪明,孟科.基于极限学习机的短期风力发电预测[J].电力科学与技术学报,2019,34(2):106111.ZHU Kang,YANG Hongming,MENG Ke.Shortterm wind power forecast using extreme learning machine[J].Journal of Electric Power Science and Technology,2019,34(2):106111.
    [8] 张宇航,邱才明,贺兴,等.一种基于LSTM神经网络的短期用电负荷预测方法[J].电力信息与通信技术.2017,15(9):1925.ZHANG Yuhang,QIU Caiming,HE Xing,et al.A shortterm load forecasting based on LSTM neural network[J].Electric Power Information and Communication Technology.2017,15(9):1925.
    [9] 高赐威,张亮.电动汽车充电对电网影响的综述[J].电网技术,2011,35(2):127131.Gao Ciwei,Zhang Liang . A survey of influence of electric vehicle charging on power grid[J].Power System Technology,2011,35(2):127131.
    [10] 刘卓然,陈健,林凯,等.国内外电动汽车发展现状与趋势[J].电力建设,2015,36(7):2532.LIU Zhuoran,CHEN Jian,LIN Kai,et al.Domestic and foreign present situation and the tendency of electric vehicles[J].Electric Power Construction,2015,36(7):2532.
    [11] 杨亚雄,杨洪明,张俊.电动汽车代理商电力市场竞价策略研究[J].电力科学与技术学报,2015,30(2):104110.YANG Yaxiong,YANG Hongming,ZHANG Jun.Research on bidding strategy of electricity market for electric vehicle aggregators[J].Journal of Electric Power Science and Technology,2015,30(2):104110.
    [12] 徐智威,胡泽春,宋永华,等.基于动态分时电价的电动汽车充电站有序充电策略[J].中国电机工程学报,2014,34(22):36383646.XU Zhiwei,HU Zechun,SONG Yonghua,et al.Coordinated charging strategy for PEV charging stations based on dynamic timeofuse tariffs[J].Proceedings of the CSEE,2014,34(22):36383646.
    [13] 刘继东,韩学山,韩伟吉,等.分时电价下用户响应行为的模型与算法[J].电网技术,2013,37(10):29732978.LIU Jidong,HAN Xueshan,HAN Weiji,et al.Model and algorithm of customers’ responsive behavior under timeofUse price[J].Power System Technology,2013,37(10):29732978.
    [14] 阮文骏,王蓓蓓,李扬,等.峰谷分时电价下的用户响应行为研究[J].电网技术,2012,36(7):8693.RUAN Wenjun,WANG Beibei,LI Yang,et al.Customer response behavior in timeofuse price[J].Power System Technology,2012,36(7):8693.
    [15] Li X G,Wu X H.Constructing long shortterm memory based deep recurrent neural networks for large vocabulary speech recognition[C]//2015 IEEE International Conference on Acoustics,Speech and Signal Processing(ICASSP),Brisbane,QLD,Australia:IEEE,2015.
    [16] 秦祯芳,岳顺民,余贻鑫.零售端电力市场中的电量电价弹性矩阵[J].电力系统自动化,2004,28(5):1619+24.QIN Zhenfang,YUE Shunmin,YU Yixin.Price elasticity matrix of demand in current retail power market[J].Automation of Electric Power Systems,2004,28(5):1619+24.
    [17] 刘青,戚中译.基于蒙特卡洛法的电动汽车负荷预测建模[J].电力科学与工程,2014,30(10):1419.LIU Qing,QI Zhongyi.Electric vehicles load forecasting model based on monte carlo simulation[J].Electric Power Science and Engineering,2014,30(10):1419.
    [18] 江卓翰,何禹清,曹丽璐,等.基于改进遗传算法的含分布式电源和储能装置配电系统网络重构[J].电力系统保护与控制,2018,46(5):6872.JIANG Zhuohan,HE Yuqing,CAO Lilu,et al.Reconfiguration of distribution network with distributed generations and energy storing devices based on improved genetic algorithm[J].Power System Protection and Control,2018,46(5):6872.
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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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