Neural Networks for Encoding Dynamic Security-Constrained Optimal Power Flow
arXiv:2003.07939
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.
arXiv commentIn proceedings of the 11th Bulk Power Systems Dynamics and Control Symposium (IREP 2022), July 25-30, 2022, Banff, Canada
References in corpus (6)
- TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
- Evaluating Robustness of Neural Networks with Mixed Integer Programming
- Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability
- Verification of Neural Network Behaviour: Formal Guarantees for Power System Applications
- Introducing machine learning for power system operation support
- Learning to Solve Network Flow Problems via Neural Decoding