Building Seasonal Highways for Residential Energy Hubs: Sizing, planning and operating thermal energy storage
Abstract
The operation of residential energy hubs with multiple energy carriers (electricity, heat, mobility) poses a significant challenge due to the energy storage differences in time-constants, round-trip efficiencies and self-discharge rates. Usually, thermal storage exhibits flexibility in yearly planning optimizations or long-term scenarios. However, as optimization horizons shrink (1-48hs) so does their supplied value due to the lower round-trip efficiencies. To avoid this early depletion during operation this paper proposes a data-driven highway to steer the short-term daily control towards long-term optimality. The proposed methodology also presents how to optimally size the thermal storage and avoid yearly simulations and how all of this is related to nonlinearities in the daily operation. The presented framework links seasonal and daily optimizations through dynamic terminal sets and value functions. The seasonally-aware nonlinear economic model predictive controller achieves the most balanced performance, with the second best mean grid cost of all MPCs at -\texteuro 209. It also achieves better battery degradation control than its linear counterparts (between 26-34%) and the best thermal comfort of the nonlinear benchmarks. Nevertheless, the data-driven seasonal highway restrains the ability to control battery degradation and slightly increases computational time.
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BibTeX
@misc{slaifstein2026building,
title = {{Building Seasonal Highways for Residential Energy Hubs: Sizing, planning and operating thermal energy storage}},
author = {Dario Slaifstein and Mohammad Khosravi and Gautham Ram Chandra Mouli and Laura Ramirez-Elizondo and Pavol Bauer},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.01489},
eprint = {2610.01489},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.01489},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Building Seasonal Highways for Residential Energy Hubs: Sizing, planning and operating thermal energy storage
AU - Dario Slaifstein
AU - Mohammad Khosravi
AU - Gautham Ram Chandra Mouli
AU - Laura Ramirez-Elizondo
AU - Pavol Bauer
PY - 2026
DA - 2026/10/01
PB - arXiv
DO - 10.48550/arxiv.2610.01489
UR - https://arxiv.org/abs/2610.01489
AB - The operation of residential energy hubs with multiple energy carriers (electricity, heat, mobility) poses a significant challenge due to the energy storage differences in time-constants, round-trip efficiencies and self-discharge rates. Usually, thermal storage exhibits flexibility in yearly planning optimizations or long-term scenarios. However, as optimization horizons shrink (1-48hs) so does their supplied value due to the lower round-trip efficiencies. To avoid this early depletion during operation this paper proposes a data-driven highway to steer the short-term daily control towards long-term optimality. The proposed methodology also presents how to optimally size the thermal storage and avoid yearly simulations and how all of this is related to nonlinearities in the daily operation. The presented framework links seasonal and daily optimizations through dynamic terminal sets and value functions. The seasonally-aware nonlinear economic model predictive controller achieves the most balanced performance, with the second best mean grid cost of all MPCs at -\texteuro 209. It also achieves better battery degradation control than its linear counterparts (between 26-34%) and the best thermal comfort of the nonlinear benchmarks. Nevertheless, the data-driven seasonal highway restrains the ability to control battery degradation and slightly increases computational time.
ER -