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@ARTICLE{Tayyab:1008318,
      author       = {Tayyab, Muhammad and Hauer, Ines and Helm, Sebastian},
      title        = {{H}olistic approach for microgrid planning for e-mobility
                      infrastructure under consideration of long-term uncertainty},
      journal      = {Sustainable energy, grids and networks},
      volume       = {34},
      issn         = {2352-4677},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier},
      reportid     = {FZJ-2023-02280},
      pages        = {101073 -},
      year         = {2023},
      abstract     = {Integrating renewable energy sources in sectors such as
                      electricity, heat, and transportation has to be planned
                      economically, technologically, and emission-efficient to
                      address global environmental issues. Microgrids appear to be
                      the solution for large-scale renewable energy integration in
                      these sectors. The microgrid components must be optimally
                      planned and operated to prevent high costs, technical
                      issues, and emissions. Existing approaches for optimal
                      microgrid planning and operation in the literature do not
                      include a solution for e-mobility infrastructure
                      development. Consequently, the authors provide a compact new
                      methodology that considers the placement and the stochastic
                      evolution of e-mobility infrastructure. In this new
                      methodology, a retropolation approach to forecast the rise
                      in the number of electric vehicles, a monte-carlo simulation
                      for electric vehicle (EV) charging behaviors, a method for
                      the definition of electric vehicle charging station (EVCS)
                      numbers based on occupancy time, and public EVCS placement
                      based on monte-carlo simulation have been developed. A
                      deterministic optimization strategy for the planning and
                      operation of microgrids is created using the abovementioned
                      methodologies, which additionally consider technical power
                      system issues. As the future development of e-mobility
                      infrastructure has high associated uncertainties, a new
                      stochastic method referred to as the information gap
                      decision method (IGDM) is proposed. This method provides a
                      risk-averse strategy for microgrid planning and operation by
                      including long-term uncertainty related to e-mobility.
                      Finally, the deterministic and stochastic methodologies are
                      combined in a novel holistic approach for microgrid design
                      and operation in terms of cost, emission, and robustness.The
                      proposed method has been tested in a new settlement area in
                      Magdeburg, Germany, under three different EV development
                      scenarios (negative, trend, and positive). EVs are expected
                      to reach 31 percent of the total number of cars in the
                      investigated settlement area. Due to this, three public
                      electric vehicle charging stations will be required in the
                      2031 trend scenario. Thus, the investigated settlement area
                      requires a total cost of 127,029 €. In association with
                      possible uncertainties, the cost of the microgrid must be
                      raised by 80 percent to gain complete robustness against
                      long-term risks in the development of EVCS.},
      cin          = {IEK-9},
      ddc          = {333.7},
      cid          = {I:(DE-Juel1)IEK-9-20110218},
      pnm          = {1223 - Batteries in Application (POF4-122)},
      pid          = {G:(DE-HGF)POF4-1223},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001043366300001},
      doi          = {10.1016/j.segan.2023.101073},
      url          = {https://juser.fz-juelich.de/record/1008318},
}