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Transmission switching is widely used in the electric power industry for both preventive and corrective purposes. Optimal transmission switching (OTS) problems are usually formulated based on optimal power flow (OPF) problems. OTS problems are originally nonlinear optimization problems with binary integer variables indicating whether a transmission line is in or out of service, however, they can be linearized into mixed-integer linear programs (MILP) through the big-M method. In such big-M-based MILP problems, the value of M can significantly affect their computational efficiency. This paper proposes a method to find the optimal big-M values for OTS problems and studies the impact of big-M values on the computational efficiency of OTS problems. The model was implemented on a modified RTS-96 test system, and the results show that the proposed model can effectively reduce the computational time by finding an optimal big-M value which ensures optimal switching solutions while maintaining numerical stability.
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This content will become publicly available on November 1, 2026
AC-Informed DC Optimal Transmission Switching Problems via Parameter Optimization
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. Numeric 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.
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- Award ID(s):
- 2145564
- PAR ID:
- 10649361
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Transactions on Power Systems
- Volume:
- 40
- Issue:
- 6
- ISSN:
- 0885-8950
- Page Range / eLocation ID:
- 5422 to 5433
- Subject(s) / Keyword(s):
- Optimal transmission switching (OTS), DC optimal power flow (DC-OPF), AC-OPF, parameter optimization.
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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