Renewable Energy Targets and Unintended Storage Cycling: Implications for Energy Modeling

TL;DR

This paper investigates how renewable energy share constraints in energy models can unintentionally cause excessive storage cycling, leading to distorted dispatch, investment decisions, and market prices, and offers solutions to mitigate this issue.

Contribution

It introduces the concept of unintended storage cycling, analyzes its causes, and provides recommendations to prevent modeling artifacts in renewable energy integration.

Findings

Unintended storage cycling can distort optimal dispatch and investment decisions.

Different implementation approaches for renewable share constraints can induce storage cycling.

Recommendations are provided to avoid these modeling artifacts.

Abstract

To decarbonize the economy, many governments have set targets for the use of renewable energy sources. These are often formulated as relative shares of electricity demand or supply. Implementing respective constraints in energy models is a surprisingly delicate issue. They may cause a modeling artifact of excessive electricity storage use. We introduce this phenomenon as 'unintended storage cycling', which can be detected in case of simultaneous storage charging and discharging. In this paper, we provide an analytical representation of different approaches for implementing minimum renewable share constraints in models, and show how these may lead to unintended storage cycling. Using a parsimonious optimization model, we quantify related distortions of optimal dispatch and investment decisions as well as market prices, and identify important drivers of the phenomenon. Finally, we provide recommendations on how to avoid the distorting effects of unintended storage cycling in energy modeling.