A Modified Maximum Entropy Inverse Reinforcement Learning Approach for Microgrid Energy Scheduling

Conference · Mon Sep 25 04:00:00 EDT 2023

DOI:https://doi.org/10.1109/PESGM52003.2023.10252933· OSTI ID:2228804

Lin, Yanbin ^[1]; Das, Avijit ^[2]; Ni, Zhen ^[1]

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1. Florida Atlantic University
2. BATTELLE (PACIFIC NW LAB)

Increasing popularity of integrating distributed energy resources (DERs) into the power system brings a challenge to optimize the microgrid dispatch policy. The reinforcement learning methods suffer from a long-time problem with the theoretical assumption of the objective/reward function for the microgrid system. Although the traditional inverse reinforcement learning (IRL) approaches can solve this problem to some extent, they encounter a limitation of complex computations for state visitation frequency in the large and continuous state space. To alleviate this limitation, we propose a modified maximum entropy IRL (MMIRL) method to extract the reward function from the expert demonstrations for solving the microgrid energy scheduling problem. The proposed MMIRL algorithm is promising in recovering the reward function and learning the dispatch policy compared to conventional approaches. Case studies are performed in an energy arbitrage problem and a microgrid system with DERs. Results substantiate that the proposed MMIRL approach can learn the dispatch policy with more than 99% efficiency and outperforms other comparative methods.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 2228804
• Report Number(s): PNNL-SA-179906
• Country of Publication: United States
• Language: English

Related Subjects

artificial intelligence
reinforcement learning
microgrid