Scalable Bilevel Optimization for Generating Maximally Representative OPF Datasets

TL;DR

This paper presents RAMBO, a bilevel optimization method that efficiently generates diverse and boundary-adjacent OPF datasets to improve data-driven power system tools, especially under high renewable integration.

Contribution

It introduces RAMBO, a novel bilevel optimization routine for generating representative OPF datasets that better capture system limits compared to random sampling.

Findings

RAMBO outperforms random sampling in capturing boundary data.

Test results on PGLib cases demonstrate improved dataset quality.

Method enhances training data for machine learning in power systems.

Abstract

New generations of power systems, containing high shares of renewable energy resources, require improved data-driven tools which can swiftly adapt to changes in system operation. Many of these tools, such as ones using machine learning, rely on high-quality training datasets to construct probabilistic models. Such models should be able to accurately represent the system when operating at its limits (i.e., operating with a high degree of ``active constraints"). However, generating training datasets that accurately represent the many possible combinations of these active constraints is a particularly challenging task, especially within the realm of nonlinear AC Optimal Power Flow (OPF), since most active constraints cannot be enforced explicitly. Using bilevel optimization, this paper introduces a data collection routine that sequentially solves for OPF solutions which are ``optimally far" from previously acquired voltage, power, and load profile data points. The routine, termed RAMBO, samples critical data close to a system's boundaries much more effectively than a random sampling benchmark. Simulated test results are collected on the 30-, 57-, and 118-bus PGLib test cases.