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@INPROCEEDINGS{Holtwerth:1005149,
      author       = {Holtwerth, Alexander and Fritz, Jakob and Xhonneux, André
                      and Müller, Dirk},
      title        = {{I}nvestigation on the {I}nfluence of {R}eal-{W}orld
                      {W}eather and {D}emand {F}orecasts on the {M}odel
                      {P}redictive {C}ontrol of a {H}ydrogen-{B}attery {E}nergy
                      {S}torage {S}ystem; 35th},
      reportid     = {FZJ-2023-01337},
      pages        = {140-152},
      year         = {2022},
      abstract     = {Model 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.},
      month         = {Jul},
      date          = {2022-07-01},
      organization  = {The 35th International Conference on
                       Efficiency, Cost, Optimization,
                       Simulation and Environmental Impact of
                       Energy Systems, Copenhagen (Denmark), 1
                       Jul 2022 - 3 Jul 2022},
      cin          = {IEK-10},
      cid          = {I:(DE-Juel1)IEK-10-20170217},
      pnm          = {1123 - Smart Areas and Research Platforms (POF4-112)},
      pid          = {G:(DE-HGF)POF4-1123},
      typ          = {PUB:(DE-HGF)8},
      doi          = {10.11581/dtu.00000267},
      url          = {https://juser.fz-juelich.de/record/1005149},
}