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@ARTICLE{Reinert:1021919,
      author       = {Reinert, Christiane and Nilges, Benedikt and Baumgärtner,
                      Nils and Bardow, André},
      title        = {{T}his is {S}p{A}rta: {R}igorous {O}ptimization of
                      {R}egionally {R}esolved {E}nergy {S}ystems by {S}patial
                      {A}ggregation and {D}ecomposition},
      publisher    = {arXiv},
      reportid     = {FZJ-2024-01067},
      year         = {2023},
      abstract     = {Energy systems with high shares of renewable energy are
                      characterized by local variability and grid limitations. The
                      synthesis of such energy systems, therefore, requires models
                      with high spatial resolution. However, high spatial
                      resolution increases the computational effort. Here, we
                      present the SpArta method for rigorous optimization of
                      regionally resolved energy systems by Spatial Aggregation
                      and decomposition. SpArta significantly reduces
                      computational effort while maintaining the full spatial
                      resolution of sector-coupled energy systems. SpArta first
                      reduces problem size by spatially aggregating the energy
                      system using clustering. The aggregated problem is then
                      relaxed and restricted to obtain a lower and an upper bound.
                      The spatial resolution is iteratively increased until the
                      difference between upper and lower bound satisfies a
                      predefined optimality gap. Finally, each cluster of the
                      aggregated problem is redesigned at full resolution. For
                      this purpose, SpArta decomposes the original synthesis
                      problem into subproblems for each cluster. Combining the
                      redesigned cluster solutions yields an optimal feasible
                      solution of the full-scale problem within a predefined
                      optimality gap. SpArta thus optimizes large-scale energy
                      systems rigorously with significant reductions in
                      computational effort. We apply SpArta to a case study of the
                      sector-coupled German energy system, reducing the
                      computational time by a factor of 7.5, compared to the
                      optimization of the same problem at full spatial resolution.
                      As SpArta shows a linear increase in computational time with
                      problem size, SpArta enables computing larger problems
                      allowing to resolve energy system designs with improved
                      accuracy.},
      keywords     = {Optimization and Control (math.OC) (Other) / Systems and
                      Control (eess.SY) (Other) / FOS: Mathematics (Other) / FOS:
                      Electrical engineering, electronic engineering, information
                      engineering (Other)},
      cin          = {IEK-10},
      cid          = {I:(DE-Juel1)IEK-10-20170217},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.48550/ARXIV.2302.05222},
      url          = {https://juser.fz-juelich.de/record/1021919},
}