Explicit Ensemble Learning Surrogate for Joint Chance-Constrained Optimal Power Flow

TL;DR

This paper introduces an ensemble support vector machine surrogate model for joint chance-constrained optimal power flow, improving computational efficiency and interpretability in risk-aware power system optimization under renewable uncertainty.

Contribution

It develops a novel ensemble SVM surrogate that approximates probabilistic constraints with hyperplanes, enabling scalable and transparent risk-aware power flow optimization.

Findings

Achieves near-optimal costs with only 0.03% average error.

Provides a scalable and interpretable polyhedral approximation.

Demonstrates effectiveness on IEEE 118-bus system.

Abstract

The increasing penetration of renewable generation introduces uncertainty into power systems, challenging traditional deterministic optimization methods. Chance-constrained optimization offers an approach to balancing cost and risk; however, incorporating joint chance constraints introduces computational challenges. This paper presents an ensemble support vector machine surrogate for joint chance constraint optimal power flow, where multiple linear classifiers are trained on simulated optimal power flow data and embedded as tractable hyperplane constraints via Big--M reformulations. The surrogate yields a polyhedral approximation of probabilistic line flow limits that preserves interpretability and scalability. Numerical experiments on the IEEE 118-bus system show that the proposed method achieves near-optimal costs with a negligible average error of $0.03\%$. These results demonstrate the promise of ensemble surrogates as efficient and transparent tools for risk-aware optimization of power systems.