A Deep Reinforcement Learning Method for Multi-objective Transmission Switching

TL;DR

This paper introduces a deep reinforcement learning approach using a dueling actor-critic framework to optimize multi-objective transmission switching, balancing cost savings and system reliability in power networks.

Contribution

It presents a novel DRL method that effectively handles the nonlinear, high-dimensional decision space of multi-objective transmission switching, improving solution quality and computational efficiency.

Findings

Outperforms benchmark DRL algorithms in numerical tests

Enhances system reliability while reducing operational costs

Demonstrates scalability on IEEE 118-bus system

Abstract

Transmission switching is a well-established approach primarily applied to minimize operational costs through strategic network reconfiguration. However, exclusive focus on cost reduction can compromise system reliability. While multi-objective transmission switching can balance cost savings with reliability improvements, feasible solutions become exceedingly difficult to obtain as system scale grows, due to the inherent nonlinearity and high computational demands involved. This paper proposes a deep reinforcement learning (DRL) method for multi-objective transmission switching. The method incorporates a dueling-based actor-critic framework to evaluate the relative impact of each line switching decision within the action space, which improves decision quality and enhances both system reliability and cost efficiency. Numerical studies on the IEEE 118-bus system verify the effectiveness and efficiency of the proposed approach compared to two benchmark DRL algorithms.