Scalable and Interactive Electricity Grid Expansion Planning

TL;DR

This paper introduces a scalable, interactive algorithm for electricity grid expansion planning that efficiently handles large models and multiple scenarios, enabling rapid updates and uncertainty analysis.

Contribution

The study presents an implicit gradient descent algorithm that significantly improves scalability and interactivity in large-scale grid expansion planning.

Findings

Successfully solved a case with over 100 million variables

Warm starts can speed up subsequent runs by up to 100x

Enables quick uncertainty analysis of storage costs

Abstract

Large scale grid expansion planning studies are essential to rapidly and efficiently decarbonizing the electricity sector. These studies help policy makers and grid participants understand which renewable generation, storage, and transmission assets should be built and where they will be most cost effective or have the highest emissions impact. However, these studies are often either too computationally expensive to run repeatedly or too coarsely modeled to give actionable decision information. In this study, we present an implicit gradient descent algorithm to solve expansion planning studies at scale, i.e., problems with many scenarios and large network models. Our algorithm is also interactive: given a base plan, planners can modify assumptions and data then quickly receive an updated plan. This allows the planner to study expansion outcomes for a wide variety of technology cost, weather, and electrification assumptions. We demonstrate the scalability of our tool, solving a case with over a hundred million variables. Then, we show that using warm starts can speed up subsequent runs by as much as 100x. We highlight how this can be used to quickly conduct storage cost uncertainty analysis.