{"id":{"repo_id":"qucosa-diss","oai_identifier":"oai:qucosa:de:qucosa:72945"},"canonical_url":"https://search.dev.ndltd.org/etd/qucosa-diss/oai:qucosa:de:qucosa:72945","repository":{"repo_id":"qucosa-diss","name":"QUCOSA","base_url":"http://www.qucosa.de/oai/"},"display":{"title":"Analysis of hydrogen-based energy storage pathways","abstract":"Hydrogen is considered to become a main energy vector in sustainable energy systems to store large amounts of intermittent wind and solar power. In this work, exergy efficiency and cost analyses are conducted to compare pathways of hydrogen generation (PEM, alkaline or solid oxide electrolysis), storage (compression, liquefaction or methanation), transportation (trailer or pipeline) and utilization (PEMFC, SOFC or combined cycle gas turbine). All processes are simulated with respect to their full and part-load efficiencies and resulting costs. Furthermore, load profiles are estimated to simulate a whole year of operation at varying loads. The results show power-to-power exergy efficiencies varying between about 17.5 and 43 %. The main losses occur at utilization and generation. Methanation features both lower efficiency and higher costs than compressed hydrogen pathways. While gas turbines show very high efficiency at full load, their efficiency drops significantly during load-following operation , while fuel cells (especially solid oxide) can maintain their efficiency and exceed the combined cycle gas turbine full-load efficiency. Overall specific costs between 245 €/MWh and 646 €/MWh are resulting from the simulation. Lower costs are commonly reached in chains with higher overall efficiencies. Installation costs are identified as predominant because of the low amount of full-load hours. To decrease the energy storage overall costs of the process chains, the options to use revenue generated by by-products such as oxygen and heat as well as changing the system application scenario are investigated. While the effect of the oxygen sale is negligible, the revenue generated by heat can significantly decrease overall costs. An increase of full-load by accounting for an electrolysis base-load to provide hydrogen for vehicles also shows a significant decreases in costs per stored energy down to 151 €/MWh at 2337 h/a full-load hours. The optimization of the exergy efficiency is performed by analysing physical and heat exergy recovery options such as expansion machines in the gas grid, the use of additional thermodynamic cycles (both Joule and Clausius-Rankine), as well as providing heat for steam electrolysis from compression inter-cooling, methanation or stored heat from a solid oxide fuel cell. The analysis shows that at full-load, process chains using solid oxide electrolysis, compressed hydrogen and a combined cycle gas turbine or a solid oxide fuel cells with a heat exergy recovery cycle can reach exergy efficiencies of 47 % and 45.5 %, respectively. A reversible solid oxide cell systems with metal-hydride heat and hydrogen storage can also reach 46.5 % exergy efficiency. The energy storage costs for these processes can be as low as 35 to 40 €/MWh at full-load. At load-following operation the efficiency of the fuel cell systems is expected to increase.","abstract_html":"Hydrogen is considered to become a main energy vector in sustainable energy systems to store large amounts of intermittent wind and solar power. In this work, exergy efficiency and cost analyses are conducted to compare pathways of hydrogen generation (PEM, alkaline or solid oxide electrolysis), storage (compression, liquefaction or methanation), transportation (trailer or pipeline) and utilization (PEMFC, SOFC or combined cycle gas turbine). All processes are simulated with respect to their full and part-load efficiencies and resulting costs. Furthermore, load profiles are estimated to simulate a whole year of operation at varying loads. The results show power-to-power exergy efficiencies varying between about 17.5 and 43 %. The main losses occur at utilization and generation. Methanation features both lower efficiency and higher costs than compressed hydrogen pathways. While gas turbines show very high efficiency at full load, their efficiency drops significantly during load-following operation , while fuel cells (especially solid oxide) can maintain their efficiency and exceed the combined cycle gas turbine full-load efficiency. Overall specific costs between 245 €/MWh and 646 €/MWh are resulting from the simulation. Lower costs are commonly reached in chains with higher overall efficiencies. Installation costs are identified as predominant because of the low amount of full-load hours. To decrease the energy storage overall costs of the process chains, the options to use revenue generated by by-products such as oxygen and heat as well as changing the system application scenario are investigated. While the effect of the oxygen sale is negligible, the revenue generated by heat can significantly decrease overall costs. An increase of full-load by accounting for an electrolysis base-load to provide hydrogen for vehicles also shows a significant decreases in costs per stored energy down to 151 €/MWh at 2337 h/a full-load hours. The optimization of the exergy efficiency is performed by analysing physical and heat exergy recovery options such as expansion machines in the gas grid, the use of additional thermodynamic cycles (both Joule and Clausius-Rankine), as well as providing heat for steam electrolysis from compression inter-cooling, methanation or stored heat from a solid oxide fuel cell. The analysis shows that at full-load, process chains using solid oxide electrolysis, compressed hydrogen and a combined cycle gas turbine or a solid oxide fuel cells with a heat exergy recovery cycle can reach exergy efficiencies of 47 % and 45.5 %, respectively. A reversible solid oxide cell systems with metal-hydride heat and hydrogen storage can also reach 46.5 % exergy efficiency. The energy storage costs for these processes can be as low as 35 to 40 €/MWh at full-load. At load-following operation the efficiency of the fuel cell systems is expected to increase.","abstract_has_math":false,"creators":["Ludwig, Mario"],"institution":"Technische Universität Dresden","degree_name":null,"degree_level":"thesis.doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Hesse, Ullrich","Hurtado, Antonio","Möst, Dominik"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-10-16","date_published":"2020-10-16","updated_at":"2026-07-24T03:56:55Z","subjects":["hydrogen","cost","exergy","process chain","Wasserstoff","Kosten","Exergie","Prozesskette"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hesse, Ullrich","Hurtado, Antonio","Möst, Dominik"]},{"key":"dc:creator","label":"Author","values":["Ludwig, Mario"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:publisher","label":"Institution","values":["Technische Universität Dresden"]},{"key":"dc:type","label":"Dc Type","values":["doctoralThesis"]},{"key":"thesis:degree_level","label":"Degree Level","values":["thesis.doctoral"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Technische Universität Dresden"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["hydrogen","cost","exergy","process chain","Wasserstoff","Kosten","Exergie","Prozesskette"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Hydrogen is considered to become a main energy vector in sustainable energy systems to store large amounts of intermittent wind and solar power. In this work, exergy efficiency and cost analyses are conducted to compare pathways of hydrogen generation (PEM, alkaline or solid oxide electrolysis), storage (compression, liquefaction or methanation), transportation (trailer or pipeline) and utilization (PEMFC, SOFC or combined cycle gas turbine). All processes are simulated with respect to their full and part-load efficiencies and resulting costs. Furthermore, load profiles are estimated to simulate a whole year of operation at varying loads. The results show power-to-power exergy efficiencies varying between about 17.5 and 43 %. The main losses occur at utilization and generation. Methanation features both lower efficiency and higher costs than compressed hydrogen pathways. While gas turbines show very high efficiency at full load, their efficiency drops significantly during load-following operation , while fuel cells (especially solid oxide) can maintain their efficiency and exceed the combined cycle gas turbine full-load efficiency. Overall specific costs between 245 €/MWh and 646 €/MWh are resulting from the simulation. Lower costs are commonly reached in chains with higher overall efficiencies. Installation costs are identified as predominant because of the low amount of full-load hours. To decrease the energy storage overall costs of the process chains, the options to use revenue generated by by-products such as oxygen and heat as well as changing the system application scenario are investigated. While the effect of the oxygen sale is negligible, the revenue generated by heat can significantly decrease overall costs. An increase of full-load by accounting for an electrolysis base-load to provide hydrogen for vehicles also shows a significant decreases in costs per stored energy down to 151 €/MWh at 2337 h/a full-load hours. The optimization of the exergy efficiency is performed by analysing physical and heat exergy recovery options such as expansion machines in the gas grid, the use of additional thermodynamic cycles (both Joule and Clausius-Rankine), as well as providing heat for steam electrolysis from compression inter-cooling, methanation or stored heat from a solid oxide fuel cell. The analysis shows that at full-load, process chains using solid oxide electrolysis, compressed hydrogen and a combined cycle gas turbine or a solid oxide fuel cells with a heat exergy recovery cycle can reach exergy efficiencies of 47 % and 45.5 %, respectively. A reversible solid oxide cell systems with metal-hydride heat and hydrogen storage can also reach 46.5 % exergy efficiency. The energy storage costs for these processes can be as low as 35 to 40 €/MWh at full-load. At load-following operation the efficiency of the fuel cell systems is expected to increase.","Wasserstoff wird als einer der wichtigsten Energieträger zur Speicherung von fluktuierender Wind- und Solarenergie in einem nachhaltigen Energiesystem betrachtet. In dieser Arbeit werden Exergieeffizienz und Kostenanalysen durchgeführt, um verschiedene Pfade von Wasserstoffherstellung (PEM, alkalische oder Festoxidelektrolyse), -speicherung (Verdichtung, Verflüssigung oder Methanisierung), -transport (Trailer oder Pipeline) und -rückverstromung (PEM-, Festoxidbrennstoffzellen oder Gas- und Dampfkraftwerke (GuD)) zu vergleichen. Alle Prozessketten werden für Voll- und Teillast simuliert und ihrWirkungsgrad sowie die Kosten berechnet. Weiterhin werden Lastprofile abgeschätzt, um ein gesamtes Betriebsjahr unter schwankender Last zu simulieren. Die Ergebnisse zeigen exergetische Strom-zu-Strom-Wirkungsgrade von etwa 17.5 % bis 43 %. Die größten Verluste treten bei der Rückverstromung und bei der Herstellung von Wasserstoff auf. Methanisierung zeigt sowohl niedrigere Wirkungsgrade als auch höhere Kosten als Pfade mit reinem Wasserstoff. Während GuD-Kraftwerke sehr hohe Wirkungsgrade bei Volllast aufweisen, zeigen Brennstoffzellen im Lastfolgebetrieb über ein Gesamtjahr höhere Wirkungsgrade. Spezifische Gesamtkosten zwischen 245 e/MWh und 646 e/MWh werden durch die Simulation berechnet. Niedrigere Prozesskettengesamtkosten sind gemeinhin mit einem hohem Wirkungsgrad verbunden. Installationskosten sind auf Grund der niedrigen Volllaststundenzahl der hauptsächliche Treiber der Gesamtkosten. Um die Energiespeicherkosten der Prozessketten zu verringern, werden die Kostenreduktion durch den Verkauf von Nebenprodukten wie Sauerstoff und Wärme, sowie die Erweiterung der Anwendung untersucht. Während der Effekt des Erlöses durch den Verkauf von Sauerstoff gering ist, kann der von Wärme die Gesamtkosten signifikant verringern. Eine Erhöhung der Volllaststudenzahl durch das Einbeziehen einer Elektrolyse-Grundlast für die Bereitstellung von Wasserstoff für die mobile Anwendung zeigt auch eine deutliche Verringerung der Gesamtkosten auf bis zu 151 €/MWh bei 2337 h/a Volllaststunden. Die Optimierung des Wirkungsgrades wird durch die Analyse von physischer sowie Wärmeexergierückgewinnung durchgeführt. Dafür wird die Nutzung von Expansionsmaschinen im Gasnetz, der Einsatz von zusätzlichen Joule- und Clausius-Rankine-Prozessen, wie auch die Bereitstellung von Wärme für die Dampfelektrolyse aus der Methanisierung, der Kühlung zwischen Verdichtungsstufen und der Speicherung von Wärme analysiert. Die Berechnung zeigt, dass bei Volllast Prozessketten, die Wasserstoff mit Hilfe von Festoxidelektrolyse herstellen und diesen dann in einem GuD-Kraftwerk oder einer Festoxidbrennstoffzelle mit Clausius-Rankine- Prozess rückverstromen, exergetischeWirkungsgrade von 47 % bzw. 45.5 % erreicht werden können. Eine reversible Festoxidbrennstoffzelle, die Wärme und Wasserstoff in einem Metallhydrid speichert, kann exergetische Wirkungsgrade von 46.5 % erreichen. Die Energiespeicherkosten für diese Systeme können bei Volllast 35 bis 40 €/MWh betragen. Es kann angenommen werden, dass über ein Betriebsjahr der Wirkungsgrad steigen wird."]},{"key":"dc:title","label":"Title","values":["Analysis of hydrogen-based energy storage pathways"]}]}],"canonical_facts":{"dc:contributor":["Hesse, Ullrich","Hurtado, Antonio","Möst, Dominik"],"dc:creator":["Ludwig, Mario"],"dc:description.abstract":["Hydrogen is considered to become a main energy vector in sustainable energy systems to store large amounts of intermittent wind and solar power. In this work, exergy efficiency and cost analyses are conducted to compare pathways of hydrogen generation (PEM, alkaline or solid oxide electrolysis), storage (compression, liquefaction or methanation), transportation (trailer or pipeline) and utilization (PEMFC, SOFC or combined cycle gas turbine). All processes are simulated with respect to their full and part-load efficiencies and resulting costs. Furthermore, load profiles are estimated to simulate a whole year of operation at varying loads. The results show power-to-power exergy efficiencies varying between about 17.5 and 43 %. The main losses occur at utilization and generation. Methanation features both lower efficiency and higher costs than compressed hydrogen pathways. While gas turbines show very high efficiency at full load, their efficiency drops significantly during load-following operation , while fuel cells (especially solid oxide) can maintain their efficiency and exceed the combined cycle gas turbine full-load efficiency. Overall specific costs between 245 €/MWh and 646 €/MWh are resulting from the simulation. Lower costs are commonly reached in chains with higher overall efficiencies. Installation costs are identified as predominant because of the low amount of full-load hours. To decrease the energy storage overall costs of the process chains, the options to use revenue generated by by-products such as oxygen and heat as well as changing the system application scenario are investigated. While the effect of the oxygen sale is negligible, the revenue generated by heat can significantly decrease overall costs. An increase of full-load by accounting for an electrolysis base-load to provide hydrogen for vehicles also shows a significant decreases in costs per stored energy down to 151 €/MWh at 2337 h/a full-load hours. The optimization of the exergy efficiency is performed by analysing physical and heat exergy recovery options such as expansion machines in the gas grid, the use of additional thermodynamic cycles (both Joule and Clausius-Rankine), as well as providing heat for steam electrolysis from compression inter-cooling, methanation or stored heat from a solid oxide fuel cell. The analysis shows that at full-load, process chains using solid oxide electrolysis, compressed hydrogen and a combined cycle gas turbine or a solid oxide fuel cells with a heat exergy recovery cycle can reach exergy efficiencies of 47 % and 45.5 %, respectively. A reversible solid oxide cell systems with metal-hydride heat and hydrogen storage can also reach 46.5 % exergy efficiency. The energy storage costs for these processes can be as low as 35 to 40 €/MWh at full-load. At load-following operation the efficiency of the fuel cell systems is expected to increase.","Wasserstoff wird als einer der wichtigsten Energieträger zur Speicherung von fluktuierender Wind- und Solarenergie in einem nachhaltigen Energiesystem betrachtet. In dieser Arbeit werden Exergieeffizienz und Kostenanalysen durchgeführt, um verschiedene Pfade von Wasserstoffherstellung (PEM, alkalische oder Festoxidelektrolyse), -speicherung (Verdichtung, Verflüssigung oder Methanisierung), -transport (Trailer oder Pipeline) und -rückverstromung (PEM-, Festoxidbrennstoffzellen oder Gas- und Dampfkraftwerke (GuD)) zu vergleichen. Alle Prozessketten werden für Voll- und Teillast simuliert und ihrWirkungsgrad sowie die Kosten berechnet. Weiterhin werden Lastprofile abgeschätzt, um ein gesamtes Betriebsjahr unter schwankender Last zu simulieren. Die Ergebnisse zeigen exergetische Strom-zu-Strom-Wirkungsgrade von etwa 17.5 % bis 43 %. Die größten Verluste treten bei der Rückverstromung und bei der Herstellung von Wasserstoff auf. Methanisierung zeigt sowohl niedrigere Wirkungsgrade als auch höhere Kosten als Pfade mit reinem Wasserstoff. Während GuD-Kraftwerke sehr hohe Wirkungsgrade bei Volllast aufweisen, zeigen Brennstoffzellen im Lastfolgebetrieb über ein Gesamtjahr höhere Wirkungsgrade. Spezifische Gesamtkosten zwischen 245 e/MWh und 646 e/MWh werden durch die Simulation berechnet. Niedrigere Prozesskettengesamtkosten sind gemeinhin mit einem hohem Wirkungsgrad verbunden. Installationskosten sind auf Grund der niedrigen Volllaststundenzahl der hauptsächliche Treiber der Gesamtkosten. Um die Energiespeicherkosten der Prozessketten zu verringern, werden die Kostenreduktion durch den Verkauf von Nebenprodukten wie Sauerstoff und Wärme, sowie die Erweiterung der Anwendung untersucht. Während der Effekt des Erlöses durch den Verkauf von Sauerstoff gering ist, kann der von Wärme die Gesamtkosten signifikant verringern. Eine Erhöhung der Volllaststudenzahl durch das Einbeziehen einer Elektrolyse-Grundlast für die Bereitstellung von Wasserstoff für die mobile Anwendung zeigt auch eine deutliche Verringerung der Gesamtkosten auf bis zu 151 €/MWh bei 2337 h/a Volllaststunden. Die Optimierung des Wirkungsgrades wird durch die Analyse von physischer sowie Wärmeexergierückgewinnung durchgeführt. Dafür wird die Nutzung von Expansionsmaschinen im Gasnetz, der Einsatz von zusätzlichen Joule- und Clausius-Rankine-Prozessen, wie auch die Bereitstellung von Wärme für die Dampfelektrolyse aus der Methanisierung, der Kühlung zwischen Verdichtungsstufen und der Speicherung von Wärme analysiert. Die Berechnung zeigt, dass bei Volllast Prozessketten, die Wasserstoff mit Hilfe von Festoxidelektrolyse herstellen und diesen dann in einem GuD-Kraftwerk oder einer Festoxidbrennstoffzelle mit Clausius-Rankine- Prozess rückverstromen, exergetischeWirkungsgrade von 47 % bzw. 45.5 % erreicht werden können. Eine reversible Festoxidbrennstoffzelle, die Wärme und Wasserstoff in einem Metallhydrid speichert, kann exergetische Wirkungsgrade von 46.5 % erreichen. Die Energiespeicherkosten für diese Systeme können bei Volllast 35 bis 40 €/MWh betragen. Es kann angenommen werden, dass über ein Betriebsjahr der Wirkungsgrad steigen wird."],"dc:publisher":["Technische Universität Dresden"],"dc:subject":["hydrogen","cost","exergy","process chain","Wasserstoff","Kosten","Exergie","Prozesskette"],"dc:title":["Analysis of hydrogen-based energy storage pathways"],"dc:type":["doctoralThesis"],"thesis:degree_level":["thesis.doctoral"],"thesis:institution_name":["Technische Universität Dresden"]},"updated_at":"2026-07-24T03:56:55Z"}