001005151 001__ 1005151
001005151 005__ 20240709082149.0
001005151 0247_ $$2doi$$a10.1109/OSMSES54027.2022.9769104
001005151 0247_ $$2WOS$$aWOS:000852742000007
001005151 037__ $$aFZJ-2023-01339
001005151 041__ $$aEnglish
001005151 1001_ $$0P:(DE-Juel1)180106$$aHoltwerth, Alexander$$b0$$eCorresponding author$$ufzj
001005151 1112_ $$a1st International Workshop on Open Source Modelling and Simulation of Energy Systems$$cAachen$$d2022-04-04 - 2022-04-05$$gOSMSES2022$$wGermany
001005151 245__ $$aData-Driven Generation of Mixed-Integer Linear Programming Formulations for Model Predictive Control of Hybrid Energy Storage Systems using detailed nonlinear Simulation Models
001005151 250__ $$a1st
001005151 260__ $$c2022
001005151 300__ $$a160-69
001005151 3367_ $$2ORCID$$aCONFERENCE_PAPER
001005151 3367_ $$033$$2EndNote$$aConference Paper
001005151 3367_ $$2BibTeX$$aINPROCEEDINGS
001005151 3367_ $$2DRIVER$$aconferenceObject
001005151 3367_ $$2DataCite$$aOutput Types/Conference Paper
001005151 3367_ $$0PUB:(DE-HGF)8$$2PUB:(DE-HGF)$$aContribution to a conference proceedings$$bcontrib$$mcontrib$$s1677647732_17431
001005151 520__ $$aThe scheduling of hybrid energy systems with battery storage systems (BSS) and hydrogen storage systems (HSS) for the storage of renewable energies is a non-trivial task due to the nonlinear nature of electrolyzers and fuel cells and the volatile electricity generation by renewable energies. Mathematical optimization of the scheduling increases the system efficiency and decreases the share of grid electricity required to cover the electrical demand. Hence, tailor-made models are required for each hydrogen component due to the uniqueness of each hydrogen system. Therefore, the time-consuming work of model generation and validation needs to be done for every system in order to ensure adequate accuracy of the mathematical models used for model predictive control. This work derives and utilizes a simulation model of a hybrid energy system as a substitute for a real-world system. We propose a framework that uses functional mock-up units of detailed simulation models to derive tailormade mixed-integer linear programming (MILP) formulations of the steady-state operational behavior. We combine the derived formulations for the operational behavior of each component into an optimization model of the whole hybrid energy system. The optimization model is then used for model predictive control of the simulation model. The results show that we can generate accuratemodels of the component behavior without detailed knowledge of the simulation model. The resulting optimization model of the whole energy system accurately reflects the simulation model and is, therefore, suitable for model predictive control.
001005151 536__ $$0G:(DE-HGF)POF4-1123$$a1123 - Smart Areas and Research Platforms (POF4-112)$$cPOF4-112$$fPOF IV$$x0
001005151 7001_ $$0P:(DE-Juel1)8457$$aXhonneux, André$$b1$$ufzj
001005151 7001_ $$0P:(DE-Juel1)172026$$aMüller, Dirk$$b2$$ufzj
001005151 773__ $$a10.1109/OSMSES54027.2022.9769104
001005151 909CO $$ooai:juser.fz-juelich.de:1005151$$pVDB
001005151 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)180106$$aForschungszentrum Jülich$$b0$$kFZJ
001005151 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)8457$$aForschungszentrum Jülich$$b1$$kFZJ
001005151 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)172026$$aForschungszentrum Jülich$$b2$$kFZJ
001005151 9131_ $$0G:(DE-HGF)POF4-112$$1G:(DE-HGF)POF4-110$$2G:(DE-HGF)POF4-100$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-1123$$aDE-HGF$$bForschungsbereich Energie$$lEnergiesystemdesign (ESD)$$vDigitalisierung und Systemtechnik$$x0
001005151 9141_ $$y2022
001005151 920__ $$lyes
001005151 9201_ $$0I:(DE-Juel1)IEK-10-20170217$$kIEK-10$$lModellierung von Energiesystemen$$x0
001005151 980__ $$acontrib
001005151 980__ $$aVDB
001005151 980__ $$aI:(DE-Juel1)IEK-10-20170217
001005151 980__ $$aUNRESTRICTED
001005151 981__ $$aI:(DE-Juel1)ICE-1-20170217