001021905 001__ 1021905
001021905 005__ 20240712112912.0
001021905 0247_ $$2doi$$a10.48550/ARXIV.2305.18338
001021905 0247_ $$2datacite_doi$$a10.34734/FZJ-2024-01053
001021905 037__ $$aFZJ-2024-01053
001021905 1001_ $$0P:(DE-HGF)0$$aMucci, Simone$$b0
001021905 245__ $$aCost-Optimal Power-to-Methanol: Flexible Operation or Intermediate Storage?
001021905 260__ $$barXiv$$c2023
001021905 3367_ $$0PUB:(DE-HGF)25$$2PUB:(DE-HGF)$$aPreprint$$bpreprint$$mpreprint$$s1706620450_7629
001021905 3367_ $$2ORCID$$aWORKING_PAPER
001021905 3367_ $$028$$2EndNote$$aElectronic Article
001021905 3367_ $$2DRIVER$$apreprint
001021905 3367_ $$2BibTeX$$aARTICLE
001021905 3367_ $$2DataCite$$aOutput Types/Working Paper
001021905 520__ $$aThe synthesis of methanol from captured carbon dioxide and green hydrogen could be a promising replacement for the current fossil-based production. The major energy input and cost driver for such a process is the electricity for hydrogen production. Time-variable electricity cost or availability thus motivates flexible operation. However, it is unclear if each unit of the process should be operated flexibly, and if storage of electricity or hydrogen reduces the methanol production cost. To answer these questions, we modeled a Power-to-Methanol plant with batteries and hydrogen storage. Using this model, we solved a combined design and scheduling optimization problem, which provides the optimal size of the units of the plant and their optimal (quasi-stationary) operation. The annualized cost of methanol was minimized for a grid-connected and a stand-alone case study. The optimization results confirm that storage, especially hydrogen storage, is particularly beneficial when the electricity price is high and highly fluctuating. Irrespective of the presence of storage, the whole Power-to-Methanol plant should be operated flexibly: even moderate flexibility of the methanol synthesis unit significantly reduces the production cost.
001021905 536__ $$0G:(DE-HGF)POF4-899$$a899 - ohne Topic (POF4-899)$$cPOF4-899$$fPOF IV$$x0
001021905 588__ $$aDataset connected to DataCite
001021905 650_7 $$2Other$$aOptimization and Control (math.OC)
001021905 650_7 $$2Other$$aFOS: Mathematics
001021905 7001_ $$0P:(DE-Juel1)172025$$aMitsos, Alexander$$b1$$ufzj
001021905 7001_ $$0P:(DE-HGF)0$$aBongartz, Dominik$$b2$$eCorresponding author
001021905 773__ $$a10.48550/ARXIV.2305.18338
001021905 8564_ $$uhttps://juser.fz-juelich.de/record/1021905/files/2305.18338-1.pdf$$yOpenAccess
001021905 8564_ $$uhttps://juser.fz-juelich.de/record/1021905/files/2305.18338-1.gif?subformat=icon$$xicon$$yOpenAccess
001021905 8564_ $$uhttps://juser.fz-juelich.de/record/1021905/files/2305.18338-1.jpg?subformat=icon-1440$$xicon-1440$$yOpenAccess
001021905 8564_ $$uhttps://juser.fz-juelich.de/record/1021905/files/2305.18338-1.jpg?subformat=icon-180$$xicon-180$$yOpenAccess
001021905 8564_ $$uhttps://juser.fz-juelich.de/record/1021905/files/2305.18338-1.jpg?subformat=icon-640$$xicon-640$$yOpenAccess
001021905 909CO $$ooai:juser.fz-juelich.de:1021905$$pdnbdelivery$$pdriver$$pVDB$$popen_access$$popenaire
001021905 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-HGF)0$$aRWTH Aachen$$b0$$kRWTH
001021905 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)172025$$aForschungszentrum Jülich$$b1$$kFZJ
001021905 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-Juel1)172025$$aRWTH Aachen$$b1$$kRWTH
001021905 9131_ $$0G:(DE-HGF)POF4-899$$1G:(DE-HGF)POF4-890$$2G:(DE-HGF)POF4-800$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$aDE-HGF$$bProgrammungebundene Forschung$$lohne Programm$$vohne Topic$$x0
001021905 9141_ $$y2023
001021905 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
001021905 920__ $$lyes
001021905 9201_ $$0I:(DE-Juel1)IEK-10-20170217$$kIEK-10$$lModellierung von Energiesystemen$$x0
001021905 9801_ $$aFullTexts
001021905 980__ $$apreprint
001021905 980__ $$aVDB
001021905 980__ $$aUNRESTRICTED
001021905 980__ $$aI:(DE-Juel1)IEK-10-20170217
001021905 981__ $$aI:(DE-Juel1)ICE-1-20170217