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Deep Learning Power System Carbon Emission Regulation and Intelligent Modeling of Elastic Load
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Author(s): Xu Zhao (Yunnan Power Dispatching and Control Center, China), Yang Wu (Yunnan Power Dispatching and Control Center, China), Wentao Zou (Beijing Tsintergy Technology Co., Ltd., China)and Xuhui Wang (Yunnan Power Dispatching and Control Center, China)
Copyright: 2026
Volume: 18
Issue: 1
Pages: 21
Source title:
International Journal of Grid and High Performance Computing (IJGHPC)
Editor(s)-in-Chief: Emmanuel Udoh (Sullivan University, USA)and Ching-Hsien Hsu (Asia University, Taiwan)
DOI: 10.4018/IJGHPC.408425
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Abstract
Against the background of China's double-carbon strategy, the authors put forward a double-loop Transformer–Bi-directional (Bi)–long short-term memory–actor–critic framework that realizes the closed-loop coordination of carbon emission prediction and load control through carbon-load coupling loss and a self-calibration feedback mechanism. The experimental results, which are based on the 15-min resolution dispatching data from six provinces in East China, showed that the carbon emission mean squared error of the framework was 0.038 total carbon dioxide (tCO2), the peak–valley load difference was reduced by 8.7%, and the peak-shaving benefit was increased by 5.4%. In addition, the sensitivity analysis results showed that when the carbon emission weight exceeded 0.6 the system will give priority to reducing carbon emissions. The framework successfully realizes the end-to-end collaborative modeling of carbon emissions and elastic loads and provides an extensible solution for low-carbon scheduling and demand side management.
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