Ms. M. Jin Profile

M. Jin

at Kunming Electric Power Trading Ctr. Co., Ltd.

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Proceedings Article | 23 November 2022 Paper

Electricity price forecasting of coupled carbon price

M. Jin, Y. Xing, B. Wang, X. Yang, X. Liu

Proceedings Volume 12302, 1230248 (2022) https://doi.org/10.1117/12.2645698

KEYWORDS: Carbon, Neural networks, Signal processing, Time-frequency analysis, Neurons, Machine learning, Integration, Factor analysis, Calibration

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Under the background of "dual carbon" targets, China's carbon market has put into operation. Bargaining transaction in the carbon market will make carbon price one of the costs of power plant. However, the key factor, carbon price, was not included in many previous studies and predictions on China's electricity price. Therefore, with the gradual popularization of carbon market in the future, it’s crucial to incorporate carbon price into the cost of power enterprises, re-forecasting the electricity price. In this paper, the electricity price in power market is discomposed into Intrinsic Mode Functions (IMF) by means of coupled carbon price and Ensemble Empirical Mode Decomposition (EEMD) method. Secondly, the IMF sequence is classified, and each IMF component possesses its corresponding GRU neural network for training. Finally, in combination with carbon neutral strategies, the CGE modal is adopted to predict the long-term trend of carbon price, so as to reconstruct the electricity price and establish the electricity price prediction model coupled with the carbon price.

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