Optimal Tap Setting of Voltage Regulation Transformers Using Batch Reinforcement Learning

In this paper, we address the problem of setting the tap positions of load tap changers (LTCs) for voltage regulation in power distribution systems. The objective is to find a policy that maps measurements of voltage magnitudes and topology information to LTC tap ratio changes so as to minimize the voltage deviation across the system. We formulate this problem as a Markov decision process (MDP), and propose a data and computationally efficient batch reinforcement learning (RL) algorithm to solve it. To circumvent the "curse of dimensionality" resulting from the large state and action spaces, we propose a sequential learning algorithm to learn an action-value function for each LTC, based on which the optimal tap positions can be directly determined. By taking advantage of a linearized power flow model, we propose an algorithm to estimate the voltage magnitudes under different tap settings, which allows the RL algorithm to explore the state and action spaces freely offline without impacting the system operation. The effectiveness of the proposed algorithm is validated via numerical simulations on the IEEE 13-bus and 123-bus distribution test feeders.