Case Study of a Hydrogen-Based District Heating in a Rural Area: Modeling and Evaluation of Prediction and Optimization Methodologies

Daniel Lust (), Marcus Brennenstuhl, Robert Otto, Tobias Erhart, Dietrich Schneider and Dirk Pietruschka
Additional contact information
Daniel Lust: Hochschule für Technik Stuttgart
Marcus Brennenstuhl: Hochschule für Technik Stuttgart
Robert Otto: Hochschule für Technik Stuttgart
Tobias Erhart: Hochschule für Technik Stuttgart
Dietrich Schneider: Hochschule für Technik Stuttgart
Dirk Pietruschka: Institute for Applied Research, University of Applied Sciences Stuttgart

Chapter 10 in iCity. Transformative Research for the Livable, Intelligent, and Sustainable City, 2022, pp 145-181 from Springer

Abstract: Abstract Buildings are accountable for about one third of the greenhouse gas emissions in Germany. An important step toward the reduction of greenhouse gases is to decarbonize the power productions and heating systems. However, in an energy system with a high share of renewable energy sources, large shares of energy have to be stored in summer for the winter season. Chemical energy storages, in this case hydrogen, can provide these qualities and offer diverse opportunities for coupling different sectors. In this work, a simulation model is introduced which combines a PEM electrolyzer, a hydrogen compression, a high-pressure storage, and a PEM fuel cell for power and heat production. Applied on a building cluster in a rural area with existing PV modules, this system is optimized for operation as a district heating system based on measured and forecasted data. Evolutionary algorithms were used to determine the optimized system parameters. The investigated system achieves an overall heat demand coverage of 63%. However, the local hydrogen production is not sufficient to meet the fuel cell demand. Several refills of the storage tanks with delivered hydrogen would be necessary within the year studied.

Keywords: Water electrolysis; Hydrogen storage; Fuel cell; Hydrogen system model; District heating; Evolutionary algorithms; Machine learning; Random forest; Decision tree; Gradient boosting (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-92096-8_10

Ordering information: This item can be ordered from
http://www.springer.com/9783030920968

DOI: 10.1007/978-3-030-92096-8_10

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().