Stochastic AC Optimal Power Flow: A Data-Driven Approach

TL;DR

This paper introduces a scalable, data-driven stochastic AC-OPF algorithm that significantly reduces sample requirements and improves computational efficiency for reliable power grid operations under uncertainty.

Contribution

The paper proposes a novel iterative scenario design method combining constraint violation data with sparse regression, enabling low-sample, scalable stochastic AC-OPF solutions.

Findings

Achieves feasible solutions with fewer samples than traditional methods.

Demonstrates computational efficiency improvements on benchmark systems.

Supports parallelization and data-driven adaptation for practical applications.

Abstract

There is an emerging need for efficient solutions to stochastic AC Optimal Power Flow ({AC-}OPF) to ensure optimal and reliable grid operations in the presence of increasing demand and generation uncertainty. This paper presents a highly scalable data-driven algorithm for stochastic AC-OPF that has extremely low sample requirement. The novelty behind the algorithm's performance involves an iterative scenario design approach that merges information regarding constraint violations in the system with data-driven sparse regression. Compared to conventional methods with random scenario sampling, our approach is able to provide feasible operating points for realistic systems with much lower sample requirements. Furthermore, multiple sub-tasks in our approach can be easily paralleled and based on historical data to enhance its performance and application. We demonstrate the computational improvements of our approach through simulations on different test cases in the IEEE PES PGLib-OPF benchmark library.