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Microgrid control under uncertainty
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DOI：10.1016/j.engappai.2024.109360.png)
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Abstract
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Microgrids - decentralized electrical grids that can function both in conjunction with wide area macrogrids and without - are a powerful tool to address energy resiliency and climate change mitigation. Microgrid control, however, remains a challenge; their bespoke nature and the existence of multiple sources of uncertainty lead to a control problem that traditional grid modeling and control techniques are ill-suited to handle. We build a microgrid interface to simulate microgrids under uncertainty and devise off-policy reinforcement learning algorithms to control microgrids. Our algorithms, which incorporate domain randomization and random network distillation for exploration and computational efficiency, achieve performance better than model predictive control and rule based control benchmarks under battery model uncertainty on seven of ten tested scenarios.
Keywords:
Microgrids
Control
Uncertainty
Reinforcement learning
Model predictive control
Rule based control
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