Enhanced Optimal Power Flow Using a Trained Neural Network Surrogate for Distribution Grid Constraints
This work addresses the need for computationally efficient and exact OPF solutions in distribution grids with high DER penetration, offering a practical alternative to traditional nonlinear or relaxed formulations.
The paper embeds a trained neural network surrogate as an exact mixed-integer linear constraint in optimal power flow to replace the nonlinear power-flow-to-voltage mapping, achieving voltage accuracy within 1.0 V and reduced computation time compared to nonlinear OPF models.
The growing penetration of distributed energy resources (DERs), electric vehicles (EVs), and heat pumps (HPs) in distribution networks underscores the need for secure, computationally efficient optimal power flow (OPF) solutions. Traditional OPF formulations often suffer from scalability limitations and may rely on relaxations/approximations whose exactness is not guaranteed. This paper proposes a framework in which a trained neural network (NN) surrogate is embedded directly within the OPF as a constraint replacement. Specifically, the nonlinear power-flow-to-voltage mapping is replaced by an exact mixed-integer linear encoding of the NN (i.e., the NN input-output map is represented without approximation), while all remaining OPF constraints are preserved. Using a realistic low-voltage network with integrated PV, EVs, and HPs, the proposed method achieves high voltage accuracy during post-solution AC power flow validation, with maximum deviations of less than 1.0 V in the examined test cases. The resulting NN-OPF problems are solved to global optimality within the MILP solver tolerance, and numerical results demonstrate substantially reduced computation time compared to nonlinear OPF models, with performance competitive with SOCP-based DistFlow formulations.