Abstract
This paper introduces a framework to capture previously intractable
optimization constraints and transform them to a mixed-integer linear program,
through the use of neural networks. We encode the feasible space of
optimization problems characterized by both tractable and intractable
constraints, e.g. differential equations, to a neural network. Leveraging an
exact mixed-integer reformulation of neural networks, we solve mixed-integer
linear programs that accurately approximate solutions to the originally
intractable non-linear optimization problem. We apply our methods to the AC
optimal power flow problem (AC-OPF), where directly including dynamic security
constraints renders the AC-OPF intractable. Our proposed approach has the
potential to be significantly more scalable than traditional approaches. We
demonstrate our approach for power system operation considering N-1 security
and small-signal stability, showing how it can efficiently obtain cost-optimal
solutions which at the same time satisfy both static and dynamic security
constraints.
Original language | English |
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Title of host publication | Proceedings of 11th Bulk Power Systems Dynamics and Control Sympositum 2022 |
Number of pages | 8 |
Publication date | 2022 |
Publication status | Published - 2022 |
Event | 11th Bulk Power Systems Dynamics and Control Symposium - Banff, Canada Duration: 25 Jul 2022 → 30 Jul 2022 Conference number: 11 |
Conference
Conference | 11th Bulk Power Systems Dynamics and Control Symposium |
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Number | 11 |
Country/Territory | Canada |
City | Banff |
Period | 25/07/2022 → 30/07/2022 |
Keywords
- Neural networks
- Mixed-integer linear programming
- Optimal power flow
- Power system security