Learning to Optimally Dispatch Power: Performance on a Nation-Wide Real-World Dataset

TL;DR

This paper introduces a real-world power system dataset from Uruguay and evaluates how machine learning models for optimal reactive power dispatch perform under actual grid conditions, revealing significant challenges and limitations.

Contribution

It provides a new publicly available dataset with real operational data and assesses the impact of real-world conditions on learning-based ORPD solutions.

Findings

Prediction errors increase with real-world data

Existing models struggle with complex real grid statistics

Need for more expressive architectures in models

Abstract

The Optimal Reactive Power Dispatch (ORPD) problem plays a crucial role in power system operations, ensuring voltage stability and minimizing power losses. Recent advances in machine learning, particularly within the ``learning to optimize'' framework, have enabled fast and efficient approximations of ORPD solutions, typically by training models on precomputed optimization results. While these approaches have demonstrated promising performance on synthetic datasets, their effectiveness under real-world grid conditions remains largely unexplored. This paper makes two key contributions. First, we introduce a publicly available power system dataset that includes both the structural characteristics of Uruguay's electrical grid and nearly two years of real-world operational data, encompassing actual demand and generation profiles. Given Uruguay's high penetration of renewable energy, the ORPD problem has become the primary optimization challenge in its power network. Second, we assess the impact of real-world data on learning-based ORPD solutions, revealing a significant increase in prediction errors when transitioning from synthetic to actual demand and generation inputs. Our results highlight the limitations of existing models in learning under the complex statistical properties of real grid conditions and emphasize the need for more expressive architectures. By providing this dataset, we aim to facilitate further research into robust learning-based optimization techniques for power system management.