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734. Learning-Assisted Optimization for Transmission Switching
Invited abstract in session MA-19: Learning-assisted Optimization in Energy Problems, stream OR in Energy.
Monday, 8:30-10:00Room: 44 (building: 116)
Authors (first author is the speaker)
| 1. | Salvador Pineda Morente
|
| Electrical Engineering, University of Málaga | |
| 2. | Juan Miguel Morales
|
| Applied Mathematics, University of Málaga | |
| 3. | M. Asuncion Jimenez-Cordero
|
| Statistics and Operations Research, University of Malaga |
Abstract
We propose a novel learning procedure to assist in the solution of a well-known computationally difficult optimization problem in power systems: The Direct Current Optimal Transmission Switching (DC-OTS). This model consists in finding the configuration of the power network that results in the cheapest dispatch of the power generating units. The DC-OTS problem takes the form of a mixed-integer program, which is NP-hard in general. The proposed approach leverages known solutions to past instances of the DC-OTS problem to speed up the mixed-integer optimization of a new unseen model. Although it does not offer optimality guarantees, a series of numerical experiments run on a real-life power system dataset show that it features a very high success rate in identifying the optimal grid topology (especially when compared to alternative competing heuristics), while rendering remarkable speed-up factors.
Keywords
- OR in Energy
- Programming, Mixed-Integer
- Machine Learning
Status: accepted
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