Learning for DC-OPF: Classifying active sets using neural nets

The optimal power flow is an optimization problem used in power systems operational planning to maximize economic efficiency while satisfying demand and maintaining safety margins. Due to uncertainty and variability in renewable energy generation and demand, the optimal solution needs to be updated in response to observed uncertainty realizations or near real-time forecast updates. To address the challenge of computing such frequent real-time updates to the optimal solution, recent literature has proposed the use of machine learning to learn the mapping between the uncertainty realization and the optimal solution. Further, learning the active set of constraints at optimality, as opposed to directly learning the optimal solution, has been shown to significantly simplify the machine learning task, and the learnt model can be used to predict optimal solutions in real-time. In this paper, we propose the use of classification algorithms to learn the mapping between the uncertainty realization and the active set of constraints at optimality, thus further enhancing the computational efficiency of the real-time prediction. We employ neural net classifiers for this task and demonstrate the excellent performance of this approach on a number of systems in the IEEE PES PGLib-OPF benchmark library.