Accurately modeling long-term storage with minimum representative hours in large-scale renewable energy systems

TL;DR

This paper introduces a novel storage representation method based on representative hours that combines high accuracy with computational efficiency, significantly improving large-scale renewable energy system modeling.

Contribution

The paper presents a new RH-based storage modeling method that reduces computational time by over 95% while maintaining high accuracy, suitable for large-scale energy system models.

Findings

Reduces solving time by over 95% compared to existing methods.

Effective at aggregations of 100-500 hours per year.

Applicable to large-scale, sector-coupled energy transition models.

Abstract

Energy system optimization often relies on time series aggregation to ensure computational tractability. Aggregation generally loses the chronology of time steps, which renders the storage level representation challenging. Typically, this challenge is addressed by using representative days (RD) to utilize intra-day chronology, even though representative hours (RH) can describe the input time series more accurately at fewer representative time steps than RD. However, until now, the use of RH storage representation methods has been limited by either high computational complexity, poor accuracy in clustering and storage representation, or restricted applicability. Here, we present a novel storage representation method based on RH that combines the high accuracy of RH time series aggregation with the high computational efficiency of methods based on RD. Through benchmarking the four most established storage representation methods on a model of a net-zero European energy system, we find that the proposed method can reduce the solving time by over 95% for the same objective value compared to the most established RD and RH methods. The proposed method exhibits particular strengths at strong aggregations of around 100 to 500 representative hours per year, making the method especially applicable to large-scale and sector-coupled transition pathway models. The developed method for accurately modeling both short-term and long-term storage, along with the presented findings, is of practical relevance to energy system modelers who seek computational tractability in large-scale applications while avoiding the misallocation of storage and conversion capacities.