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.
Highlights
The electric power industry is constantly reshaping under the changing mechanisms and competition, and the prediction of power demand and price has become one of the most important research topics in electrical engineering [1,2]
A time-sharing electricity price forecasting model is proposed in this paper based on SSA-deep extreme learning machine (DELM), considering the impact of wind and solar power on the price of electricity in the form of wind–load and solar–load ratios
It can be found that the correlation coefficient difference between wind–load ratio and wind–solar ratio is tiny because in the Nordic electricity market, Denmark’s DK1 region, the wind power load is much larger than the solar load, while in the period with more solar output during the day, the higher solar–load ratio will lead to an increase in the fluctuation range of the coefficient of variation of electricity price, for example, from 11:00 to 14:00 in 2020, when the wind–load ratio is similar
Summary
The electric power industry is constantly reshaping under the changing mechanisms and competition, and the prediction of power demand and price has become one of the most important research topics in electrical engineering [1,2]. A variety of time series prediction models based on DL have been widely used, such as the deep multi-layer perceptron (DMLP), recurrent neural network (RNN) [19], long short-term memory network (LSTM) [20,21], deep belief network (DBN), deep extreme learning machine (DELM) [22], etc. A time-sharing electricity price forecasting model is proposed in this paper based on SSA-DELM, considering the impact of wind and solar power on the price of electricity in the form of wind–load and solar–load ratios (wind–load ratio refers to the ratio of wind power to total load, and the same with solar power for solar–load ratio).
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