Probabilistic Forecasting of Regional Net-load with Conditional Extremes and Gridded NWP

TL;DR

This paper introduces a probabilistic net-load forecasting framework that models the tails of predictive distributions using Generalised Pareto Distribution, improving risk management and reserve optimization in renewable-heavy power systems.

Contribution

It adapts load forecasting methods for net-load and models distribution tails with covariate-dependent GPD, enhancing tail calibration and risk assessment.

Findings

Conditional tail modeling reduces reserve requirements.

Forecasts are better calibrated and sharper.

High-risk periods are effectively identified.

Abstract

The increasing penetration of embedded renewables makes forecasting net-load, consumption less embedded generation, a significant and growing challenge. Here a framework for producing probabilistic forecasts of net-load is proposed with particular attention given to the tails of predictive distributions, which are required for managing risk associated with low-probability events. Only small volumes of data are available in the tails, by definition, so estimation of predictive models and forecast evaluation requires special attention. We propose a solution based on a best-in-class load forecasting methodology adapted for net-load, and model the tails of predictive distributions with the Generalised Pareto Distribution, allowing its parameters to vary smoothly as functions of covariates. The resulting forecasts are shown to be calibrated and sharper than those produced with unconditional tail distributions. In a use-case inspired evaluation exercise based on reserve setting, the conditional tails are shown to reduce the overall volume of reserve required to manage a given risk. Furthermore, they identify periods of high risk not captured by other methods. The proposed method therefore enables user to both reduce costs and avoid excess risk.