Learning Optimal Solutions for Extremely Fast AC Optimal Power Flow

Ahmed Zamzam, Kyri Baker

Research output: Contribution to conferencePaperpeer-review

73 Scopus Citations

Abstract

We develop, in this paper, a machine learning approach to optimize the real-time operation of electric power grids. In particular, we learn feasible solutions to the AC optimal power flow (OPF) problem with negligible optimality gaps. The AC OPF problem aims at identifying optimal operational conditions of the power grids that minimize power losses and/or generation costs. Due to the computational challenges with solving this nonconvex problem, many efforts have focused on linearizing or approximating the problem in order to solve the AC OPF on faster timescales. However, many of these approximations can be fairly poor representations of the actual system state and still require solving an optimization problem, which can be time consuming for large networks. In this work, we learn a mapping between the system loading and optimal generation values, enabling us to find near-optimal and feasible AC OPF solutions. This allows us to bypass solving the traditionally nonconvex AC OPF problem, resulting in a significant decrease in computational burden for grid operators.

Conference

Conference2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2020
Country/TerritoryUnited States
CityTempe
Period11/11/2013/11/20

Bibliographical note

See NREL/CP-5D00-77736 for preprint

NREL Publication Number

  • NREL/CP-5D00-79159

Keywords

  • machine learning
  • neural networks
  • optimal power flow

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