High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow
This addresses the need for efficient and accurate power system optimization in the face of increased renewable energy integration, though it is incremental as it builds on existing deep learning and Lagrangian duality methods.
The paper tackled the computationally challenging AC Optimal Power Flow problem for large-scale power systems by proposing a deep learning approach, achieving solutions within 0.01% of optimality and generating them in milliseconds.
The AC Optimal Power Flow (AC-OPF) is a key building block in many power system applications. It determines generator setpoints at minimal cost that meet the power demands while satisfying the underlying physical and operational constraints. It is non-convex and NP-hard, and computationally challenging for large-scale power systems. Motivated by the increased stochasticity in generation schedules and increasing penetration of renewable sources, this paper explores a deep learning approach to deliver highly efficient and accurate approximations to the AC-OPF. In particular, the paper proposes an integration of deep neural networks and Lagrangian duality to capture the physical and operational constraints. The resulting model, called OPF-DNN, is evaluated on real case studies from the French transmission system, with up to 3,400 buses and 4,500 lines. Computational results show that OPF-DNN produces highly accurate AC-OPF approximations whose costs are within 0.01% of optimality. OPF-DNN generates, in milliseconds, solutions that capture the problem constraints with high fidelity.