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@ARTICLE{Cardoso:848003,
      author       = {Cardoso, Goncarlo and Brouhard, Thomas and DeForest,
                      Nicholas and Wang, Dai and Heleno, Miguel and Kotzur,
                      Leander},
      title        = {{B}attery {A}ging in {M}ulti-{E}nergy {M}icrogrid {D}esign
                      {U}sing {M}ixed {I}nteger {L}inear {P}rogramming},
      journal      = {Applied energy},
      volume       = {231},
      issn         = {0306-2619},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2018-03311},
      pages        = {1059 - 1069},
      year         = {2018},
      abstract     = {This paper introduces a linear battery aging and
                      degradation model to a multi-energy microgrid sizing model
                      using mixed integer linear programming. The battery aging
                      model and its integration into a larger microgrid sizing
                      formulation are described. A case study is provided to
                      explore the impact of considering battery aging on key
                      results: optimal photovoltaic and storage capacities,
                      optimal distributed energy resources operations strategies,
                      and annual cost and generation metrics.The case study
                      results suggest that considering battery degradation in
                      optimal microgrid sizing problems significantly impacts the
                      perceived value of storage. Depending on capacity loss and
                      lifetime targets, considering battery degradation is shown
                      to decrease optimal storage capacities between 6 and $92\%$
                      versus scenarios that do not consider battery health. When
                      imposing constant distributed energy resource capacities,
                      inclusion of degradation can decrease optimal annual battery
                      cycling by as much as a factor five and reduce total annual
                      electricity cost savings from otherwise identical
                      photovoltaic and storage systems by $5–12\%.$ These
                      results emphasize that as batteries grow in maturity and
                      ubiquity for distributed energy applications, considering
                      battery health and capacity loss is an essential component
                      of any analytical tool or model to guide system planning and
                      decision-making.},
      cin          = {IEK-3},
      ddc          = {620},
      cid          = {I:(DE-Juel1)IEK-3-20101013},
      pnm          = {134 - Electrolysis and Hydrogen (POF3-134) / PhD no Grant -
                      Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405)},
      pid          = {G:(DE-HGF)POF3-134 / G:(DE-Juel1)PHD-NO-GRANT-20170405},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000452345400081},
      doi          = {10.1016/j.apenergy.2018.09.185},
      url          = {https://juser.fz-juelich.de/record/848003},
}