Stochastic optimal scheduling strategy for a campus-isolated microgrid energy management system considering dependencies

• Published in: Energy conversion and management Vol. 292; p. 117341
• Main Authors: Dong, Weichao, Sun, Hexu, Mei, Chunxiao, Li, Zheng, Zhang, Jingxuan, Yang, Huifang, Ding, Yinan
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.09.2023
• Subjects: Campus-isolated microgrid; Improved episodic deep reinforcement learning; Latin hypercube sampling method; Multilayer hybrid forecasting model; Regular vine copula; Stochastic optimal scheduling strategy; Stochastic optimal scheduling strategy; Regular vine copula; Campus-isolated microgrid; Multilayer hybrid forecasting model; Latin hypercube sampling method; Improved episodic deep reinforcement learning
• ISSN: 0196-8904; 1879-2227
• DOI: 10.1016/j.enconman.2023.117341

•A stochastic optimal scheduling strategy that considered dependencies was proposed.•A multilayer hybrid forecasting model achieves high wind and load forecast accuracy.•Improved episodic deep RL was used to build an EMS stochastic optimal scheduling strategy.•The uncertainties of the forecasting process were analyzed using LHS and regular vine copula. Isolated microgrids have been widely used on campuses, becoming an important part of their power-supply infrastructure. In this study, a stochastic optimal scheduling strategy that considered dependencies was proposed for the energy management system (EMS) of an isolated microgrid on campus, which consists of three main components: an accurate forecasting model on both the demand and supply sides, an excellent core scheduling strategy, and an effective uncertainty analysis. First, the forecasting model involved data correction, feature selection, core forecasting, and error analyses to obtain consistently good forecasting results. A multilayer hybrid forecasting model was developed, including a stacked multilevel denoising autoencoder, mutual information, bidirectional long short-term memory network (LSTM), and LSTM, corresponding to the above four model elements. Based on the forecasting results, improved episodic deep reinforcement learning was used to build a stochastic optimal scheduling strategy for the EMS, which could realize rapid propagation of reward values and achieve stable updating of episodic memory, effectively improving sample efficiency. The objective of optimizing the scheduling was to obtain the lowest operational cost. Finally, the uncertainties in the forecasting process were analyzed using the Latin hypercube sampling method, and a regular vine copula was used to determine the dependence of the uncertainty analysis between buildings. To the best of our knowledge, this is the first study to consider dependence in the uncertainty analysis of a campus microgrid. The performance of the proposed method was evaluated by comparing it with other existing models, aiming to obtain more efficient and stable scheduling results.