Optimal Tap Setting of Voltage Regulation Transformers Using Batch Reinforcement Learning

TL;DR

This paper introduces a batch reinforcement learning approach for optimal tap setting of voltage regulation transformers in power distribution systems, effectively managing voltage deviations under uncertain load conditions.

Contribution

It presents a novel RL-based method that uses a linearized power flow model and sequential learning to optimize tap settings without affecting system operation.

Findings

Successfully minimizes voltage deviations in test feeders

Efficiently handles large state and action spaces

Demonstrates effectiveness through IEEE test system simulations

Abstract

In this paper, we address the problem of setting the tap positions of load tap changers (LTCs) for voltage regulation in radial power distribution systems under uncertain load dynamics. The objective is to find a policy to determine the tap positions that only uses measurements of voltage magnitudes and topology information so as to minimize the voltage deviation across the system. We formulate this problem as a Markov decision process (MDP), and propose a batch reinforcement learning (RL) algorithm to solve it. By taking advantage of a linearized power flow model, we propose an effective 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. To circumvent the "curse of dimensionality" resulted 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. The effectiveness of the proposed algorithm is validated via numerical simulations on the IEEE 13-bus and 123-bus distribution test feeders.