Neural Networks for Encoding Dynamic Security-Constrained Optimal Power Flow

• URL: http://arxiv.org/abs/2003.07939v5
• Date: Thu, 14 Jul 2022 13:26:47 GMT
• Title: Neural Networks for Encoding Dynamic Security-Constrained Optimal Power Flow
• Authors: Ilgiz Murzakhanov, Andreas Venzke, George S. Misyris, Spyros Chatzivasileiadis
• Abstract summary: This paper introduces a framework to capture previously intractable optimization constraints and transform them to a mixed-integer linear program. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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