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可再生能源发电对实时电价的影响分析——德国电力现货市场的数据实证
作者:
作者单位:

1.华北电力大学经济与管理学院,北京市 102206;2.新能源电力与低碳发展研究北京市重点实验室(华北电力大学),北京市 102206

作者简介:

刘定(1993—),男,通信作者,博士研究生,主要研究方向:电力系统风险管理、人工智能。E-mail:liuding@163.com
赵德福(1994—),男,硕士研究生,主要研究方向:电力系统风险管理、电力大数据分析。E-mail:zdf737477@163.com
白木仁(1994—),男,硕士研究生,主要研究方向:电力系统风险管理、电力物理信息融合系统。E-mail:baimuren612 @163.com

通讯作者:

基金项目:

国家自然科学基金资助项目(71671065);国家电网公司科技项目(5204BB1600CN)。


Analysis on Impact of Renewable Energy Generation on Real-time Electricity Price: Data Empirical Research on Electricity Spot Market of Germany
Author:
Affiliation:

1.School of Economics and Management, North China Electric Power University, Beijing 102206, China;2.Beijing Key Laboratory of New Energy and Low-Carbon Development;(North China Electric Power University), Beijing 102206, China

Fund Project:

This work is supported by National Natural Science Foundation of China (No. 71671065) and State Grid Corporation of China (No. 5204BB1600CN).

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    摘要:

    电力现货市场实时交易可充分发挥市场调节作用,促进可再生能源消纳。基于数据实证分析可再生能源发电对实时电价的影响,对理解现货市场运行规律、开展市场成熟度评价等具有重要参考价值。选取德国电力现货市场开展数据实证,收集发电量、负荷量、预测误差、价格等多因素数据,基于时间序列特征表示方法,研究可再生能源发电对实时电价的影响。首先,使用特征表示方法将时间序列时域模型转化为特征向量。然后,采用贪婪向前特征选择算法提取关键特征,最大化因素间差异。接着,分别基于全部特征和关键特征讨论了多因素间的相关性,并构建了影响机理网络图。实证结果表明德国电力现货市场实时电价主要受到风力发电量预测误差影响,因素间相关性主要来自时间序列的傅里叶变换、小波变换、离散符号化等特征。最后,通过中德电力现货市场的定量对比,指出中国广东电力市场实时电价更易受新能源发电量而非预测误差的影响。

    Abstract:

    Real-time electricity trading in electricity spot market can give full play to market regulation and promote the accommodation of renewable energy. Data empirical research on the impact of renewable energy generation on real-time electricity price has an essential reference value for understanding the operation rules of the spot market and evaluating market maturity. Electricity spot market of Germany is selected to conduct data empirical research. Data of power generation, load, prediction error, price and other factors are collected. The impact analysis of renewable energy generation on real-time electricity price is studied based on feature representation method for time series. Firstly, the time domain models of time series are transformed into feature vectors by using the feature representation method. Secondly, the greedy forward feature selection algorithm is used to extract key features to maximize the differences between factors. Thirdly, the correlation among multiple factors is discussed based on the overall features and key features respectively, and the network of influence mechanism is constructed. The empirical results show that the real-time electricity price in electricity spot market of Germany is mainly affected by prediction error of wind power generation, and the correlation between factors mainly comes from features such as Fourier transformation, wavelet transformation and discrete symbolization. Finally, by the simple comparison between China and Germany, it is pointed out that the real-time electricity price in Guangdong electricity market of China is more affected by the randomness of renewable energy generation than by the prediction error.

    表 3 Table 3
    表 2 Table 2
    图1 方法体系示意图Fig.1 Schematic diagram of the method framework
    图2 基于全部特征分类的混淆矩阵Fig.2 Confusion matrix based on all features
    图3 12条时间序列的低维描述Fig.3 Low-dimensional description of the twelve pieces of time series
    图4 2种聚类方式的演化过程Fig.4 Evolution process of the two clustering methods
    图5 影响机理网络图Fig.5 Network diagram of influence mechanism
    图6 中德电力现货市场多因素相关分析对比Fig.6 Comparison of multi-factor correlation analysis on electricity spot market of China and Germany
    表 1 实时电价与其他因素间的距离和相关性Table 1 Distance and correlation between real-time electricity price and other factors
    图 特征6536的分类效果Fig. Classification Effect of Feature 6536
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引用本文

刘定,赵德福,白木仁,等.可再生能源发电对实时电价的影响分析——德国电力现货市场的数据实证[J].电力系统自动化,2020,44(4):126-133. DOI:10.7500/AEPS20190117001.
LIU Ding,ZHAO Defu,BAI Muren,et al.Analysis on Impact of Renewable Energy Generation on Real-time Electricity Price: Data Empirical Research on Electricity Spot Market of Germany[J].Automation of Electric Power Systems,2020,44(4):126-133. DOI:10.7500/AEPS20190117001.

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  • 收稿日期:2019-01-17
  • 最后修改日期:2019-10-08
  • 录用日期:2019-10-09
  • 在线发布日期: 2020-02-16
  • 出版日期: