Learning Optimal Solutions for Extremely Fast AC Optimal Power Flow
Open accessAbstract
We develop, in this paper, a machine learning approach to optimize the real-time operation of electric power grids. In particular, we learn feasible solutions to the AC optimal power flow (OPF) problem with negligible optimality gaps. The AC OPF problem aims at identifying optimal operational conditions of the power grids that minimize power losses and/or generation costs. Due to the computational challenges with solving this nonconvex problem, many efforts have focused on linearizing or approximating the problem in order to solve the AC OPF on faster timescales. However, many of these approximations can be fairly poor representations of the actual system state and still require solving an optimization problem, which can be time consuming for large networks. In this work, we learn a mapping between the system loading and optimal generation values, enabling us to find near-optimal and feasible AC OPF solutions. This allows us to bypass solving the traditionally nonconvex AC OPF problem, resulting in a significant decrease in computational burden for grid operators.
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ML can be used to approximate or simplify existing optimization problems, find good starting points for optimization, identify redundant constraints, learn from the actions of power system control engineers, or do some combination of these.
“ML can be used to approximate or simplify existing optimization problems [75,242,311,871], find good starting points for optimization [52,196,382], identify redundant constraints [541], learn from the actions of power system control engineers [197], or do some combination of these [858].”
From Tackling climate change with machine learning (2023) · cites this paper as [871]
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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
- Authors
- Zamzam, A. S., & Baker, K.
- Affiliations
- National Laboratory of the Rockies (US)
- University of Colorado Boulder (US)
- Venue
- 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)
- Publisher
- IEEE
- Published
- 2020-11-11
- Pages
- 1-6
- OpenAlex
- W2977379144
- Cited by
- 206
- References
- 34
- Citations by year
- 2026: 16 · 2025: 33 · 2024: 45 · 2023: 30 · 2022: 37 · 2021: 21 · 2020: 21 · 2019: 3
- FWCI
- 38.32
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Renewable Energy Laboratory
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Optimal Power Flow Distribution
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Power System Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Power System Optimization and Stability
- Keywords
- AC optimal power flow, non-convex optimization, power loss reduction, generation cost minimization, computational burden reduction
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