Hourly Electricity Price Prediction for Electricity Market with High Proportion of Wind and Solar Power

Yangrui Zhang, Peng Tao, Xiangming Wu, Chenguang Yang, Guang Han, Hui Zhou and Yinlong Hu
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Yangrui Zhang: Marketing Service Center, State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050021, China
Peng Tao: Marketing Service Center, State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050021, China
Xiangming Wu: State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050021, China
Chenguang Yang: State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050021, China
Guang Han: State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050021, China
Hui Zhou: College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Yinlong Hu: College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China

Energies, 2022, vol. 15, issue 4, 1-13

Abstract: In an open electricity market, increased accuracy and real-time availability of electricity price forecasts can help market parties participate effectively in market operations and management. As the penetration of clean energy increases, it brings new challenges to electricity price forecasting. An electricity price forecasting model is constructed in this paper for markets containing a high proportion of wind and solar power, where the scenario with a high coefficient of variation (COV) caused by the high frequency of low electricity prices is particularly concerned. The deep extreme learning machine optimized by the sparrow search algorithm (SSA-DELM) is proposed to make predictions on the model. The results show that wind–load ratio and solar–load ratio are the key input variables for forecasting in power markets with high proportions of wind and solar energy. The SSA-DELM possesses better electricity price forecasting performance in the scenario with a high COV and is more suitable for disordered time series models, which can be confirmed in comparison with LSTM.

Keywords: electricity price forecasting; wind and solar power; SSA; DELM; hourly market (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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