AC-Informed DC Optimal Transmission Switching Problems via Parameter Optimization

TL;DR

This paper enhances the DC Optimal Transmission Switching model by optimizing its parameters using machine learning to better approximate AC power flow, resulting in more accurate switching decisions and significant cost savings.

Contribution

It introduces a machine learning-based parameter optimization for DC-OTS that aligns it more closely with AC power flow behavior, improving decision accuracy.

Findings

Up to 44% cost reduction compared to traditional methods.

Improved alignment of DC-OTS with AC power flow results.

Enhanced decision accuracy in transmission switching.

Abstract

Optimal Transmission Switching (OTS) problems minimize operational costs while treating both the transmission line energization statuses and generator setpoints as decision variables. The combination of nonlinearities from an AC power flow model and discrete variables associated with line statuses makes AC-OTS a computationally challenging Mixed-Integer Nonlinear Program (MINLP). To address these challenges, the DC power flow approximation is often used to obtain a DC-OTS formulation expressed as a Mixed-Integer Linear Program (MILP). However, this approximation often leads to suboptimal or infeasible switching decisions when evaluated with an AC power flow model. This paper proposes an enhanced DC-OTS formulation that leverages techniques for training machine learning models to optimize the DC power flow model's parameters. By optimally selecting parameter values that align flows in the DC power flow model with apparent power flows -- incorporating both real and reactive components -- from AC Optimal Power Flow (OPF) solutions, our method more accurately captures line congestion behavior. Integrating these optimized parameters into the DC-OTS formulation significantly improves the accuracy of switching decisions and reduces discrepancies between DC-OTS and AC-OTS solutions. We compare our optimized DC-OTS model against traditional OTS approaches, including DC-OTS, Linear Programming AC (LPAC)-OTS, and Quadratic Convex (QC)-OTS. Numerical results show that switching decisions from our model yield better performance when evaluated using an AC power flow model, with up to $44\%$ cost reductions in some cases.