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@ARTICLE{Patil:917544,
      author       = {Patil, Shruthi and Kotzur, Leander and Stolten, Detlef},
      title        = {{A}dvanced {S}patial and {T}echnological {A}ggregation
                      {S}cheme for {E}nergy {S}ystem {M}odels},
      journal      = {Energies},
      volume       = {15},
      number       = {24},
      issn         = {1996-1073},
      address      = {Basel},
      publisher    = {MDPI},
      reportid     = {FZJ-2023-00747},
      pages        = {9517 -},
      year         = {2022},
      abstract     = {Energy system models that consider variable renewable
                      energy sources (VRESs) are computationally complex. The
                      greater spatial scope and level of detail entailed in the
                      models exacerbates complexity. As a complexity-reduction
                      approach, this paper considers the simultaneous spatial and
                      technological aggregation of energy system models. To that
                      end, a novel two-step aggregation scheme is introduced.
                      First, model regions are spatially aggregated to obtain a
                      reduced region set. The aggregation is based on model
                      parameters such as VRES time series, capacities, etc. In
                      addition, spatial contiguity of regions is considered. Next,
                      technological aggregation is performed on each VRES, in each
                      region, based on their time series. The aggregations’
                      impact on accuracy and complexity of a cost-optimal,
                      European energy system model is analyzed. The model is
                      aggregated to obtain different combinations of numbers of
                      regions and VRES types. Results are benchmarked against an
                      initial resolution of 96 regions, with 68 VRES types in
                      each. System cost deviates significantly when lower numbers
                      of regions and/or VRES types are considered. As spatial and
                      technological resolutions increase, the cost fluctuates
                      initially and stabilizes eventually, approaching the
                      benchmark. Optimal combination is determined based on an
                      acceptable cost deviation of $<5\%$ and the point of
                      stabilization. A total of 33 regions with 38 VRES types in
                      each is deemed optimal. Here, the cost is underestimated by
                      $4.42\%,$ but the run time is reduced by $92.95\%.$},
      cin          = {IEK-3},
      ddc          = {620},
      cid          = {I:(DE-Juel1)IEK-3-20101013},
      pnm          = {1111 - Effective System Transformation Pathways (POF4-111)
                      / 1112 - Societally Feasible Transformation Pathways
                      (POF4-111)},
      pid          = {G:(DE-HGF)POF4-1111 / G:(DE-HGF)POF4-1112},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000902470600001},
      doi          = {10.3390/en15249517},
      url          = {https://juser.fz-juelich.de/record/917544},
}