In this paper, we address the problem of setting the tap positions of voltage regulation transformers---also referred to as the load tap changers (LTCs)---in radial power distribution systems under uncertain load dynamics. The objective is to find a policy that can determine the optimal tap positions that minimize the voltage deviation across the system, based only on the voltage magnitude measurements and the topology information. To this end, 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 space freely offline without impacting the real power system. To circumvent the "curse of dimensionality" resulted from the large state and action space, we propose a least squares policy iteration based sequential learning algorithm to learn an action value function for each LTC, based on which the optimal tap positions can be directly determined. Numerical simulations on the IEEE 13-bus and 123-bus distribution test feeders validated the effectiveness of the proposed algorithm.

本文研究如何使用改变负载开关的接线点以调节电压，提出基于Markov决策过程和强化学习的算法，利用电流线性模型估计电压，以解决大型状态和动作空间带来的挑战，并在IEEE 13位和123位分配测试馈线上进行模拟验证。

使用批量强化学习实现电压调节变压器的最佳分接设置