Reinforcement Learning for Battery Energy Storage Dispatch augmented with Model-based Optimizer

Gayathri Krishnamoorthy; Anamika Dubey

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arXiv · 2109.01659

Reinforcement Learning for Battery Energy Storage Dispatch augmented with Model-based Optimizer

Gayathri Krishnamoorthy·Anamika Dubey

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Abstract

Reinforcement learning has been found useful in solving optimal power flow (OPF) problems in electric power distribution systems. However, the use of largely model-free reinforcement learning algorithms that completely ignore the physics-based modeling of the power grid compromises the optimizer performance and poses scalability challenges. This paper proposes a novel approach to synergistically combine the physics-based models with learning-based algorithms using imitation learning to solve distribution-level OPF problems. Specifically, we propose imitation learning based improvements in deep reinforcement learning (DRL) methods to solve the OPF problem for a specific case of battery storage dispatch in the power distribution systems. The proposed imitation learning algorithm uses the approximate optimal solutions obtained from a linearized model-based OPF solver to provide a good initial policy for the DRL algorithms while improving the training efficiency. The effectiveness of the proposed approach is demonstrated using IEEE 34-bus and 123-bus distribution feeders with numerous distribution-level battery storage systems.

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Gayathri Krishnamoorthy, Anamika Dubey. 2021-09-02. Reinforcement Learning for Battery Energy Storage Dispatch augmented with Model-based Optimizer. https://arxiv.org/abs/2109.01659

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