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Analysis on Impact of Renewable Energy Generation on Real-time Electricity Price: Data Empirical Research on Electricity Spot Market of Germany
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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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    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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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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History
  • Received:January 17,2019
  • Revised:October 08,2019
  • Adopted:October 09,2019
  • Online: February 25,2020
  • Published: