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@ARTICLE{Li:1037333,
      author       = {Li, Chuan and Carta, Daniele and Benigni, Andrea},
      title        = {{EV} {C}harging {S}tation {P}lacement {C}onsidering {V}2{G}
                      and {H}uman {F}actors in {M}ulti-{E}nergy {S}ystems},
      journal      = {IEEE transactions on smart grid},
      volume       = {16},
      number       = {1},
      issn         = {1949-3053},
      address      = {New York, NY},
      publisher    = {IEEE},
      reportid     = {FZJ-2025-00651},
      pages        = {529-540},
      year         = {2025},
      abstract     = {This paper proposes a new planning framework to determine
                      the optimal location, capacity, and types of EV charging
                      stations (EVCSs) in multi-energy systems (MESs). We propose
                      a two-stage stochastic programming approach -with
                      scenario-based algorithms- that explicitly considers
                      vehicle-to-grid (V2G) peculiarities (four-quadrant operation
                      and stochastic human factors influence: V2G willingness,
                      walking distance, and charging patterns). Considering those
                      factors together with MES uncertainties -RES generation,
                      load demands, and electricity price- enables a comprehensive
                      study of V2G and MES impact on EVCS planning. The proposed
                      approach is applied to both a purely electric distribution
                      network (EDN) and an MES to analyze the interplay of EVCSs
                      in different energy domains, in consideration of different
                      V2G contracts. The obtained results underline that the sole
                      consideration of the EDN can lead to non-optimal results,
                      while the more comprehensive analysis leads to optimal
                      planning of all energy resources and cost savings. Finally,
                      we analyse how each considered factor individually impacts
                      EVCSs planning.},
      cin          = {ICE-1},
      ddc          = {620},
      cid          = {I:(DE-Juel1)ICE-1-20170217},
      pnm          = {1122 - Design, Operation and Digitalization of the Future
                      Energy Grids (POF4-112) / 1123 - Smart Areas and Research
                      Platforms (POF4-112) / LLEC::VxG - Integration von
                      "Vehicle-to-grid" (BMBF-03SF0628)},
      pid          = {G:(DE-HGF)POF4-1122 / G:(DE-HGF)POF4-1123 /
                      G:(DE-Juel1)BMBF-03SF0628},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001392825000034},
      doi          = {10.1109/TSG.2024.3424530},
      url          = {https://juser.fz-juelich.de/record/1037333},
}