Unlocking the Potential of Renewable Energy Through Curtailment Prediction

TL;DR

This paper emphasizes the importance of predicting renewable energy curtailment to enhance utilization and support grid decarbonization, highlighting the role of machine learning in addressing oversupply and transmission issues.

Contribution

It introduces a focus on machine learning approaches for predicting renewable energy curtailment to improve grid efficiency and renewable energy utilization.

Findings

Identifies key factors influencing curtailment

Proposes a framework for curtailment prediction

Highlights potential for machine learning to optimize renewable energy use

Abstract

A significant fraction (5-15%) of renewable energy generated goes into waste in the grids around the world today due to oversupply issues and transmission constraints. Being able to predict when and where renewable curtailment occurs would improve renewable utilization. The core of this work is to enable the machine learning community to help decarbonize electricity grids by unlocking the potential of renewable energy through curtailment prediction.