Integrating Forecasting Models Within Steady-State Analysis and Optimization

TL;DR

This paper introduces a novel methodology that integrates machine learning-based weather and load forecasts directly into power grid analysis and optimization, enhancing robustness against uncertainties and extreme weather events.

Contribution

It presents a generalized approach coupling ML forecasting models with physics-based power flow and optimization tools, enabling direct sensitivity analysis without surrogate models.

Findings

Improved sensitivity calculations for forecasted devices.

Enhanced robustness in power dispatch under stochastic weather conditions.

Better grid reliability through integrated forecasting and optimization.

Abstract

Extreme weather variations and the increasing unpredictability of load behavior make it difficult to determine power grid dispatches that are robust to uncertainties. While machine learning (ML) methods have improved the ability to model uncertainty caused by loads and renewables, accurately integrating these forecasts and their sensitivities into steady-state analyses and decision-making strategies remains an open challenge. Toward this goal, we present a generalized methodology that seamlessly embeds ML-based forecasting engines within physics-based power flow and grid optimization tools. By coupling physics-based grid modeling with black-box ML methods, we accurately capture the behavior and sensitivity of loads and weather events by directly integrating the inputs and outputs of trained ML forecasting models into the numerical methods of power flow and grid optimization. Without fitting surrogate load models, our approach obtains the sensitivities directly from data to accurately predict the response of forecasted devices to changes in the grid. Our approach combines the sensitivities of forecasted devices attained via backpropagation and the sensitivities of physics-defined grid devices. We demonstrate the efficacy of our method by showcasing improvements in sensitivity calculations and leveraging them to design a robust power dispatch that improves grid reliability under stochastic weather events. Our approach enables the computation of system sensitivities to exogenous factors which supports broader analyses that improve grid reliability in the presence of load variability and extreme weather conditions.