  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.
