首页|Confidence Estimation Transformer for Long-term Renewable Energy Forecasting in Reinforcement Learning-based Power Grid Dispatching

Confidence Estimation Transformer for Long-term Renewable Energy Forecasting in Reinforcement Learning-based Power Grid Dispatching

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Expansion of renewable energy could help realize the goals of peaking carbon dioxide emissions and carbon neutralization.Some existing grid dispatching methods integrat-ing short-term renewable energy prediction and reinforcement learning(RL)have been proven to alleviate the adverse impact of energy fluctuations risk.However,these methods omit long-term output prediction,which leads to stability and security prob-lems on optimal power flow.This paper proposes a confidence estimation Transformer for long-term renewable energy fore-casting in reinforcement learning-based power grid dispatching(Conformer-RLpatching).Conformer-RLpatching predicts long-term active output of each renewable energy generator with an enhanced Transformer to ensure stable operation of the hybrid energy grid and improve the utilization rate of renewable energy,thus boosting dispatching performance.Furthermore,a confi-dence estimation method is proposed to reduce the prediction error of renewable energy.Meanwhile,a dispatching necessity evaluation mechanism is put forward to decide whether the active output of a generator needs to be adjusted.Experiments carried out on the SG-126 power grid simulator show that Conformer-RLpatching achieves great improvement over the second best algorithm DDPG in security score by 25.8％and achieves a better total reward compared with the golden medal team in the power grid dispatching competition sponsored by State Grid Corporation of China under the same simulation environment.Codes are outsourced in https://github.com/BUPT-ANTlab/Conformer-RLpatching.

Conformer-RLpatchingoptimal power flowreinforcement learningrenewable energy prediction

Xinhang Li、Nan Yang、Zihao Li、Yupeng Huang、Zheng Yuan、Xuri Song、Lei Li、Lin Zhang

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Beijing University of Posts and Telecommunications,Beijing 100876,China

Beijing Key Laboratory of Research and System Evaluation of Power Dispatching Automation Technol-ogy

State Key Program of National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaOpen Fund of Beijing Key Laboratory of Research and System Evaluation of Power Dispatching Automation Technology(China Electric

U186621062176024DZB51202101268

2024

10.17775/CSEEJPES.2022.02050

中国电机工程学会电力与能源系统学报(英文版)
中国电机工程学会

中国电机工程学会电力与能源系统学报(英文版)

CSTPCDEI
ISSN：2096-0042
年,卷(期)：2024.10(4)