Real-Time Microgrid Energy Scheduling Using Meta-Reinforcement Learning

Huan Shen, Xingfa Shen () and Yiming Chen
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Huan Shen: School of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China
Xingfa Shen: School of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China
Yiming Chen: School of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China

Energies, 2024, vol. 17, issue 10, 1-15

Abstract: With the rapid development of renewable energy and the increasing maturity of energy storage technology, microgrids are quickly becoming popular worldwide. The stochastic scheduling problem of microgrids can increase operational costs and resource wastage. In order to reduce operational costs and optimize resource utilization efficiency, the real-time scheduling of microgrids becomes particularly important. After collecting extensive data, reinforcement learning (RL) can provide good strategies. However, it cannot make quick and rational decisions in different environments. As a method with generalization ability, meta-learning can compensate for this deficiency. Therefore, this paper introduces a microgrid scheduling strategy based on RL and meta-learning. This method can quickly adapt to different environments with a small amount of training data, enabling rapid energy scheduling policy generation in the early stages of microgrid operation. This paper first establishes a microgrid model, including components such as energy storage, load, and distributed generation (DG). Then, we use a meta-reinforcement learning framework to train the initial scheduling strategy, considering the various operational constraints of the microgrid. The experimental results show that the MAML-based RL strategy has advantages in improving energy utilization and reducing operational costs in the early stages of microgrid operation. This research provides a new intelligent solution for microgrids’ efficient, stable, and economical operation in their initial stages.

Keywords: microgrid; energy management; meta-learning; reinforcement learning; online scheduling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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