Reactive power planning in distribution systems using a reinforcement learning method

This work presents a new reinforcement learning (RL) algorithm for capacitor allocation in distribution feeders. The problem formulation considers two distinct objectives related to total cost of power loss and total cost of capacitors including the purchase and installation costs. The formulation is a multi-objective and non-differentiable optimization problem. The proposed method of this article uses RL procedure for sizing and siting of capacitors in radial distribution feeders. The proposed method has been implemented in a software package and its effectiveness has been verified through a 9-bus radial distribution feeder and also a 34-bus radial distribution feeder for the sake of conclusions supports. A comparison has been made between the proposed method of this paper and similar methods in other research works to show its effectiveness for solving optimum capacitor planning problem.