001005149 001__ 1005149
001005149 005__ 20240709082147.0
001005149 0247_ $$2doi$$a10.11581/dtu.00000267
001005149 037__ $$aFZJ-2023-01337
001005149 041__ $$aEnglish
001005149 1001_ $$0P:(DE-Juel1)180106$$aHoltwerth, Alexander$$b0$$eCorresponding author$$ufzj
001005149 1112_ $$aThe 35th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems$$cCopenhagen$$d2022-07-01 - 2022-07-03$$gECOS2022$$wDenmark
001005149 245__ $$aInvestigation on the Influence of Real-World Weather and Demand Forecasts on the Model Predictive Control of a Hydrogen-Battery Energy Storage System
001005149 250__ $$a35th
001005149 260__ $$c2022
001005149 300__ $$a140-152
001005149 3367_ $$2ORCID$$aCONFERENCE_PAPER
001005149 3367_ $$033$$2EndNote$$aConference Paper
001005149 3367_ $$2BibTeX$$aINPROCEEDINGS
001005149 3367_ $$2DRIVER$$aconferenceObject
001005149 3367_ $$2DataCite$$aOutput Types/Conference Paper
001005149 3367_ $$0PUB:(DE-HGF)8$$2PUB:(DE-HGF)$$aContribution to a conference proceedings$$bcontrib$$mcontrib$$s1677647649_32145
001005149 520__ $$aModel predictive control is used in many applications due to its capability to incorporate complex system dynamics and long forecast horizons. Complex hybrid energy storage systems using hydrogen and battery storage systems have vastly different storage dynamics that necessitate a detailed system model in combination with a long forecast horizon. The integration of renewable energy sources increases the importance of energy storage over long time periods due to their inherent dependency on highly volatile environmental energy sources. Model predictive control determines the ideal scheduling of the different storage systems using mathematical optimization and therefore utilizes volatile renewable energy sources more efficiently while decreasing the strain on the energy grid. However, the actual energy production and demand are not available under real-world conditions. Therefore, appropriate forecasts are needed to ensure a high quality of the optimization results. This work investigates the influence of open-source weather forecasts and demand forecasting on the quality of model predictive control of a hybrid hydrogen-battery energy storage system with a photovoltaics (PV) parkas renewable energy source. We use weather forecasts and real-world measurements from the Deutsche Wetter Dienst (dwd) to predict and calculate the energy production of a PV-park. Moreover, we utilize data of real-world office buildings to deduce a realistic electricity demand in combination with autoregressive models for the demand forecast. Using the forecast inside a simulative sensitivity analysis, we show that the solar power forecast has the most significant impact on the operation of the hybrid energy system. The impact of short-time fluctuations in the PV-power drastically decreases the performance of the model predictive control algorithm. Furthermore, we show the influence of the control horizon length for model predictive control since the prediction uncertainty increases over time. Finally, we demonstrate the mitigation of effects from short-term fluctuation in solar power by adapting a rule-based controller to adjust the results of the scheduling optimization with regard to the operating points of the storage components.
001005149 536__ $$0G:(DE-HGF)POF4-1123$$a1123 - Smart Areas and Research Platforms (POF4-112)$$cPOF4-112$$fPOF IV$$x0
001005149 7001_ $$0P:(DE-Juel1)174136$$aFritz, Jakob$$b1$$ufzj
001005149 7001_ $$0P:(DE-Juel1)8457$$aXhonneux, André$$b2$$ufzj
001005149 7001_ $$0P:(DE-Juel1)172026$$aMüller, Dirk$$b3$$ufzj
001005149 773__ $$a10.11581/dtu.00000267
001005149 909CO $$ooai:juser.fz-juelich.de:1005149$$pVDB
001005149 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)180106$$aForschungszentrum Jülich$$b0$$kFZJ
001005149 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)174136$$aForschungszentrum Jülich$$b1$$kFZJ
001005149 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)8457$$aForschungszentrum Jülich$$b2$$kFZJ
001005149 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)172026$$aForschungszentrum Jülich$$b3$$kFZJ
001005149 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$$x0
001005149 9141_ $$y2022
001005149 920__ $$lyes
001005149 9201_ $$0I:(DE-Juel1)IEK-10-20170217$$kIEK-10$$lModellierung von Energiesystemen$$x0
001005149 980__ $$acontrib
001005149 980__ $$aVDB
001005149 980__ $$aI:(DE-Juel1)IEK-10-20170217
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001005149 981__ $$aI:(DE-Juel1)ICE-1-20170217