Neural Networks for Encoding Dynamic Security-Constrained Optimal Power Flow
This addresses the challenge of including dynamic security constraints in power grid optimization, which is incremental as it applies existing neural network reformulation techniques to a specific domain problem.
The paper tackles the intractability of dynamic security constraints in AC optimal power flow problems by encoding the feasible space of optimization problems with neural networks and transforming them into mixed-integer linear programs. The result is a scalable approach that efficiently obtains cost-optimal solutions satisfying both static and dynamic security constraints for power system operation.
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.