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Imitation Learning Based Real-time decision-making of Microgrid Economic Dispatch Under Multiple Uncertainties

Journal of Modern Power Systems and Clean Energy 2024年第4期12卷 1183-1193页

作者： Wei Dong Fan Zhang Meng Li Xiaolun Fang Qiang Yang School of Automation Hangzhou Dianzi UniversityHangzhou 310018China College of Electrical Engineering Zhejiang UniversityHangzhou 310027China

The intermittency of renewable energy generation,variability of load demand,and stochasticity of market price bring about direct challenges to optimal energy management of *** cope with these different forms of operation uncertainties,an imitation learning based real-time decision-mak-ing solution for microgrid economic dispatch is *** this solution,the optimal dispatch trajectories obtained by solving the optimal problem using historical deterministic operation patterns are demonstrated as the expert samples for imitation *** improve the generalization performance of imitation learning and the expressive ability of uncertain variables,a hybrid model combining the unsupervised and supervised learning is *** denoising autoencoder based unsupervised learning model is adopted to enhance the feature extraction of operation ***,the long short-term memory network based supervised learning model is used to efficiently characterize the mapping between the input space composed of the extracted operation patterns and system state variables and the output space composed of the optimal dispatch *** numerical simulation results demonstrate that under various operation uncertainties,the operation cost achieved by the proposed solution is close to the minimum theoretical *** with the traditional model predictive control method and basic clone imitation learning method,the operation cost of the proposed solution is reduced by 6.3% and 2.8%,respectively,overa test period of three months.

关键词： Energy management imitation learning datadriven decision economic dispatch

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