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Application of a Regional Data Set of the Housing Sector for Hydrogen Storage-Supported Energy System Planning

Steffen Schedler1,*, Michael Bareev-Rudy1, Stefanie Meilinger2, Tanja Clees1,3

1 Department of Engineering and Communication (IWK), Institute for Technology, Resource and Energy-efficient Engineering (TREE), Hochschule Bonn-Rhein-Sieg, University of Applied Science, Sankt Augustin, 53757, Germany
2 International Centre for Sustainable Development (IZNE), Hochschule Bonn-Rhein-Sieg, University of Applied Science, Sankt Augustin, 53757, Germany
3 Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Schloss Birlinghoven, Sankt Augustin, 53754, Germany

* Corresponding Author: Steffen Schedler. Email: email-brs

(This article belongs to the Special Issue: Selected Papers from the SDEWES 2024 Conference on Sustainable Development of Energy, Water and Environment Systems)

Energy Engineering 2025, 122(5), 1755-1770. https://doi.org/10.32604/ee.2025.061962

Abstract

Germany aims to achieve a national climate-neutral energy system by 2045. The residential sector still accounts for 29% of end energy consumption, with 74% attributed to the direct use of fossil fuels for heating and hot water. In order to reduce fossil energy use in the household sector, great efforts are being made to design new energy concepts that expand the use of renewable energies to supply electricity and heat. One possibility is to convert parts of the natural gas grid to a hydrogen-based gas grid to deliver and store energy for urban quarters of buildings, especially with older building stock where electrification of heat via heat pumps is difficult due to technical, acoustical, and economic reasons. A comprehensive dataset was generated by a bottom-up analysis with open governmental and statistical data to determine regional building types regarding energy demand, solar potential, and existing grid infrastructure. The buildings’ connections to the electricity, gas, and district heating networks are considered. From this, a representative sample dataset was chosen as input for a newly developed energy system model based on energy flow simulation. The model simulates the interaction of hydrogen generation (HG) (from excess solar energy by electrolysis), storage in a metal-hydride storage (MHS) tank, and hydrogen use in a connected fuel cell (FC), forming a local PVPtGtHP (Photovoltaic Power-to-Gas-to-Heat-and-Power) network. Next to the seasonal hydrogen storage path (HSP), a battery will complete the system to form a hybrid energy storage system (HESS). Paired with seasonal time series for PV power, electricity and heat demand, and a model for connection to grid infrastructure, the simulation of different hydrogen applications and MHS placements aims to analyze operating times and energy share of the systems’ equipment and existing infrastructure. The method to obtain the data set together with the simulation model presented can be used by energy planners for cities, communities, and building developers to analyze the potentials of a quarter or region and plan a transition towards a more energy-efficient and sustainable energy system.

Keywords

Hydrogen storage; hybrid energy storage system; simulation; housing sector; energy share

Cite This Article

APA Style
Schedler, S., Bareev-Rudy, M., Meilinger, S., Clees, T. (2025). Application of a Regional Data Set of the Housing Sector for Hydrogen Storage-Supported Energy System Planning. Energy Engineering, 122(5), 1755–1770. https://doi.org/10.32604/ee.2025.061962
Vancouver Style
Schedler S, Bareev-Rudy M, Meilinger S, Clees T. Application of a Regional Data Set of the Housing Sector for Hydrogen Storage-Supported Energy System Planning. Energ Eng. 2025;122(5):1755–1770. https://doi.org/10.32604/ee.2025.061962
IEEE Style
S. Schedler, M. Bareev-Rudy, S. Meilinger, and T. Clees, “Application of a Regional Data Set of the Housing Sector for Hydrogen Storage-Supported Energy System Planning,” Energ. Eng., vol. 122, no. 5, pp. 1755–1770, 2025. https://doi.org/10.32604/ee.2025.061962



cc Copyright © 2025 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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