Learning the Weather-Grid Nexus via Weather-to-Voltage (W2V) Predictive Modeling

TL;DR

This paper introduces a weather-to-voltage (W2V) neural network framework that jointly analyzes weather and grid states to improve weather forecasting and grid operation predictions.

Contribution

The W2V model is a differentiable surrogate that maps high-resolution weather features directly to grid voltages, enhancing weather-informed grid analysis.

Findings

W2V achieves high voltage prediction accuracy and stability.

W2V-based GAWF effectively prioritizes critical weather features.

Numerical tests verify the model's accuracy and generalizability.

Abstract

This paper proposes a weather-to-voltage (W2V) predictive modeling framework to learn the underlying weather-grid nexus. Unlike existing approaches on weather-informed grid operations, our proposed W2V model can achieve the joint analysis of weather and grid states, and further leverage this coupling to enhance grid-aware weather forecasting (GAWF) as a key application. To achieve this end-to-end learning, the W2V model acts as a differentiable surrogate for weather-incorporated power flow analysis by mapping weather features at high spatial resolution directly to grid-wide bus voltages. Thanks to a compact neural network design and principal component analysis based initialization, it achieves high voltage prediction accuracy and numerical stability during training. Building on this capability, W2V-based voltage signals are used to guide the development of GAWF that can account for its downstream voltage prediction performance. Using a 6717-bus Texas synthetic test system with meteorological inputs from 701 weather locations, our numerical tests have verified the excellent accuracy and generalizability of the proposed W2V model. More importantly, the W2V model has enabled the GAWF to effectively prioritize the weather features and conditions that are most critical to grid operations, such as system-wide quick wind drops preceding ramp-ups.