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

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 high computational complexity, poor accuracy in clustering and storage representation, and restricted applicability. Here, we present a novel storage representation method based on RH that combines the high accuracy of time-series aggregation via RH with the high computational efficiency of RD-based methods. 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 low- to mid-hundreds of representative hours per year, making it especially applicable to large-scale, 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.