Modeling to Generate Alternatives for Robustness of Mixed Integer DC Optimal Power Flow

TL;DR

This paper introduces a modeling to generate alternatives (MGA) approach to improve the robustness of mixed-integer DC optimal power flow solutions, ensuring feasible solutions in challenging cases.

Contribution

It proposes using MGA to find alternative solutions for linearized OPF problems, addressing infeasibility issues in mixed-integer scenarios.

Findings

MGA finds feasible solutions in all tested networks.

Search criteria significantly impact solution quality.

Approach enhances robustness of linearized OPF solutions.

Abstract

Transmission system operators face a variety of discrete operational decisions, such as switching of branches and/or devices. Incorporating these decisions into optimal power flow (OPF) results in mixed-integer non-linear programming problems (MINLPs), which can't presently be solved at scale in the required time. Various linearizations of the OPF exist, most famously the DC-OPF, which can be leveraged to find integer decisions. However, these linearizations can yield very poor integer solutions in some edge cases, making them challenging to incorporate into control rooms. This paper introduces the use of modeling to generate alternatives (MGA) to find alternative solutions to the linearized problems, reducing the chance of finding no AC feasible solutions. We test this approach using 13 networks where the DC linearization results in infeasible integer decisions, and MGA finds a solution in all cases. The MGA search criteria selected drastically affects the number and quality of solutions found, so network specific search functions may be necessary.