AC-Network-Informed DC Optimal Power Flow for Electricity Markets

TL;DR

This paper introduces a data-driven, parametric quadratic approximation of the AC optimal power flow problem, enabling real-time electricity market applications by predicting near-optimal solutions using supervised learning.

Contribution

It develops a physics-informed, demand-dependent parametric model for AC-OPF and trains it with a bilevel optimization approach to ensure market property compliance.

Findings

Accurately approximates AC-OPF costs and dispatches

Predicts LMPs and market properties effectively

Demonstrates robustness across different network topologies

Abstract

This paper presents a parametric quadratic approximation of the AC optimal power flow (AC-OPF) problem for time-sensitive and market-based applications. The parametric approximation preserves the physics-based but simple representation provided by the DC-OPF model and leverages market and physics information encoded in the data-driven demand-dependent parameters. To enable the deployment of the proposed model for real-time applications, we propose a supervised learning approach to predict near-optimal parameters, given a certain metric concerning the dispatch quantities and locational marginal prices (LMPs). The training dataset is generated based on the solution of the accurate AC-OPF problem and a bilevel optimization problem, which calibrates parameters satisfying two market properties: cost recovery and revenue adequacy. We show the proposed approach's performance in various test systems in terms of cost and dispatch approximation errors, LMPs, market properties satisfaction, dispatch feasibility, and generalizability with respect to N-1 network topologies.