Reinforcement Learning for Battery Energy Storage Dispatch augmented with Model-based Optimizer
This work addresses scalability and performance issues in reinforcement learning for power grid optimization, offering a domain-specific incremental improvement.
The paper tackles the problem of optimal power flow for battery storage dispatch in electric power distribution systems by combining physics-based models with learning-based algorithms using imitation learning, resulting in improved training efficiency and demonstrated effectiveness on IEEE 34-bus and 123-bus feeders.
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.