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含有高比例风电的电力市场电价预测
作者:
作者单位:

1.中核核电运行管理有限公司,浙江省嘉兴市 3143001;2.上海大学机电工程与自动化学院,上海市 200444

作者简介:

通讯作者:

基金项目:

国家自然科学基金资助项目(61876105);国家电网公司科技项目“电力金融产品设计与定价技术研究”。


Electricity Price Prediction for Electricity Market with High Proportion of Wind Power
Author:
Affiliation:

1.School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China;2.CNNC Operation and Management Co., Ltd., Jiaxing 314300, China

Fund Project:

This work is supported by National Natural Science Foundation of China (No. 61876105) and State Grid Corporation of China.

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    摘要:

    在解除管制的电力市场中,精确预测电价有助于市场各方有效参与市场运营与管理。清洁能源渗透率的提高,给电价预测精度带来了新的挑战。文中选择不同的输入特征变量并结合长短期记忆(LSTM)网络的特点,构建含高比例风电的电力市场电价预测模型对含有风电的电力市场电价进行预测。研究表明,风能和负荷的比值是含高比例风电的电力市场风电电价预测的关键输入参数。LSTM具备时间延迟记忆特点,拥有较好的电力市场时间序列电价预测能力。以北欧市场中DK1电力市场实际数据为基础,采用3种模型进行对比分析,结果表明含有风能和负荷的比值且考虑多时刻信息输入的LSTM模型可以较大地提高低谷时段的电价预测精度。

    Abstract:

    In the deregulated electricity market, the accurate forecasting of electricity price is helpful for all parties to participate in the market operation and management. The increase of clean energy penetration rate brings new challenges to the accuracy of electricity price prediction. By choosing different input characteristic variables and combined with the characteristics of long-short term memory (LSTM) network, the electricity price prediction model for the electricity market with high proportion of wind power is built to predict the electricity price of the electricity market with wind power. The results show that the ratio of wind power to load is the key input parameter of the electricity price prediction in the electricity market with high proportion of wind power. LSTM has the characteristic of time delay memory, so it has better ability to predict the electricity market price in time series. Based on the actual data of DK1 electricity market in the Nordic market, three models are used for the comparative analysis. The results show that the LSTM model with the ratio of wind power to load and considering multi-time information input can greatly improve the prediction accuracy of electricity price duing slack time.

    表 1 训练集电价统计值Table 1 Electricity price statistics of training set
    表 5 Table 5
    表 2 测试集误差Table 2 Error of test set
    表 4 Table 4
    表 3 DK2电力市场的实验测试集误差Table 3 Error of test set in DK2 electricity market
    图1 训练集电价分布Fig.1 Electricity price distribution of training set
    图2 当前时刻电价与前168时刻电价的相关系数Fig.2 Correlation coefficient between the current electricity price up to the value of hour t-168
    图3 LSTM单元内部拓扑图Fig.3 Internal topology of LSTM unit
    图4 时间序列LSTM模型拓扑Fig.4 Topology of LSTM with time series model
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引用本文

姚子麟,张亮,邹斌,等.含有高比例风电的电力市场电价预测[J/OL].电力系统自动化,http://doi.org/10.7500/AEPS20190614002.
YAO Zilin,ZHANG Liang,ZOU Bin,et al.Electricity Price Prediction for Electricity Market with High Proportion of Wind Power[J/OL].Automation of Electric Power Systems,http://doi.org/10.7500/AEPS20190614002.

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历史
  • 收稿日期:2019-06-14
  • 最后修改日期:2020-04-17
  • 录用日期:2020-01-31
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