Deep Reinforcement Learning for Microgrid Cost Optimization Considering Load Flexibility

This paper proposes a novel Soft-Actor-Critic (SAC) based Deep Reinforcement Learning (DRL) method for optimizing the cost of microgrid operation by leveraging load flexibility. The proposed SAC-DRL method is designed to coordinate the control of distributed energy resources (DERs) and flexible load...

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Bibliographic Details
Main Authors: Pei, Yansong, Yao, Yiyun, Zhao, Junbo, Ding, Fei, Wang, Jiyu
Format: Conference Proceeding
Language:English
Subjects:
Adaptation models
Costs
Deep reinforcement learning
Distributed power generation
Load modeling
microgrid
Microgrids
Optimization
peak load management
Power distribution
Power supplies
Reliability
voltage regulation
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Summary:This paper proposes a novel Soft-Actor-Critic (SAC) based Deep Reinforcement Learning (DRL) method for optimizing the cost of microgrid operation by leveraging load flexibility. The proposed SAC-DRL method is designed to coordinate the control of distributed energy resources (DERs) and flexible load, addressing practical energy billing formation by power distribution utilities. Key contributions include an innovative reward function to mitigate sparse reward challenges and a mixed control strategy for discrete and continuous variables, ensuring radial network topology and minimizing power loss. We evaluate the proposed method on the model of a real microgrid located in Southern California, U.S.. The SAC-DRL model is tested to demonstrate its efficacy in reducing grid dependence, optimizing resource use, and minimizing costs. The results highlight the potential of DRL in modern energy systems, offering a sustainable and economically efficient solution for energy management in microgrids.
ISSN:1944-9933
DOI:10.1109/PESGM51994.2024.10688837