Operational Optimization of Seasonal Ice-Storage Systems with Time-Series Aggregation

Maximilian Hillen (), Patrik Schönfeldt, Philip Groesdonk and Bernhard Hoffschmidt
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Maximilian Hillen: German Aerospace Center (DLR), Institute of Solar Research, 52428 Juelich, Germany
Patrik Schönfeldt: German Aerospace Center (DLR), Institute of Networked Energy Systems, 26129 Oldenburg, Germany
Philip Groesdonk: German Aerospace Center (DLR), Institute of Solar Research, 52428 Juelich, Germany
Bernhard Hoffschmidt: German Aerospace Center (DLR), Institute of Solar Research, 52428 Juelich, Germany

Energies, 2025, vol. 18, issue 22, 1-30

Abstract: The transition to sustainable energy systems increasingly relies on advanced optimization methods to address the challenges of designing and operating them efficiently. Seasonal storage systems play a pivotal role in aligning renewable energy generation with fluctuating energy demand, with ice storage emerging as a promising solution for seasonal energy storage. This paper presents a novel optimization framework for the operation of seasonal ice-storage systems, leveraging Mixed-Integer Linear Programming (MILP) with time-series aggregation (TSA) techniques. The proposed model accurately captures the physical behavior of ice storage, incorporating both latent and sensible heat storage phases, discrete temperature levels, and charging/discharging efficiency curves. A key feature of this framework is its ability to address computational challenges in large-scale optimization, while maintaining high detail. Using a business park in Germany as a case study, the results demonstrate a significant reduction in computational time of up to 80% for 110 typical periods, with only a 2.5% deviation in the objective value and 9% in the Seasonal Energy Efficiency Ratio (SEER), although this efficiency gain depends on the number of typical periods used. This work addresses key gaps in seasonal ice-storage optimization models and provides a robust tool for designing and optimizing sustainable energy systems.

Keywords: seasonal storage; ice storage; MILP; time-series aggregation; energy system optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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