Accurate Data-Based State Estimation from Power Loads Inference in Electric Power Grids

TL;DR

This paper presents a data-driven linear regression method to accurately infer missing power load data in large-scale electric power grids, improving state estimation reliability.

Contribution

It introduces a simple, effective regression-based approach for missing load inference, validated on synthetic and real power grid data, enhancing state estimation accuracy.

Findings

High accuracy in reconstructing missing demands across test systems

Effective inference of power loads from limited observations in real data

Inferred loads lead to reliable power flow and contingency analysis

Abstract

Accurate state estimation is a crucial requirement for the reliable operation and control of electric power systems. Here, we construct a data-driven, numerical method to infer missing power load values in large-scale power grids. Given partial observations of power demands, the method estimates the operational state using a linear regression algorithm, exploiting statistical correlations within synthetic training datasets. We evaluate the performance of the method on three synthetic transmission grid test systems. Numerical experiments demonstrate the high accuracy achieved by the method in reconstructing missing demand values under various operating conditions. We further apply the method to real data for the transmission power grid of Switzerland. Despite the restricted number of observations in this dataset, the method infers missing power loads rather accurately. Furthermore, Newton-Raphson power flow solutions show that deviations between true and inferred values for power loads result in smaller deviations between true and inferred values for flows on power lines. This ensures that the estimated operational state correctly captures potential line contingencies. Overall, our results indicate that simple data-based regression techniques can provide an efficient and reliable alternative for state estimation in modern power grids.