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@INPROCEEDINGS{Wijesinghe:1048193,
      author       = {Wijesinghe, Lovindu and Weinand, Jann and Hartono, Titan
                      and Linssen, Jochen and Stolten, Detlef},
      title        = {{I}dentifying {F}lexibility in {A}utonomous {M}unicipal
                      {E}nergy {S}ystems {U}sing {M}odeling to {G}enerate
                      {A}lternatives},
      reportid     = {FZJ-2025-04555},
      year         = {2025},
      abstract     = {Identifying Flexibility in Autonomous Municipal Energy
                      Systems Using Modeling to Generate AlternativesLovindu
                      Wijesinghe, Forschungszentrum Jülich GmbH, Institute of
                      Climate and Energy Systems (ICE), Juelich Systems Analysis
                      (ICE-2), 52425, Jülich, Germany.Phone: +4915110615548,
                      email: l.wijesinghe@fz-juelich.deJann Michael Weinand,
                      Forschungszentrum Jülich GmbH, Institute of Climate and
                      Energy Systems (ICE), Juelich Systems Analysis (ICE-2),
                      52425, Jülich, Germany. email: j.weinand@fz-juelich.deNoor
                      Titan Putri Hartono, Forschungszentrum Jülich GmbH,
                      Institute of Climate and Energy Systems (ICE), Juelich
                      Systems Analysis (ICE-2), 52425, Jülich, Germany. email:
                      t.hartono@fz-juelich.deDetlef Stolten, Forschungszentrum
                      Jülich GmbH, Institute of Climate and Energy Systems (ICE),
                      Juelich Systems Analysis (ICE-2), 52425, Jülich,
                      GermanyJochen Linßen, Forschungszentrum Jülich GmbH,
                      Institute of Climate and Energy Systems (ICE), Juelich
                      Systems Analysis (ICE-2), 52425, Jülich, Germany. email:
                      j.linssen@fz-juelich.deOverviewAs the global energy
                      landscape shifts toward decentralization and sustainability,
                      municipalities are increasingly exploring self-sufficient,
                      autonomous energy systems to improve resilience, reduce
                      environmental impacts, and achieve carbon neutrality targets
                      [1]. In Germany, where climate neutrality by 2045 is
                      mandated [2], understanding how flexible municipal energy
                      systems can be is vital for effective planning. However,
                      energy system designs are subject to significant uncertainty
                      in demand, policy, and technology pathways [3]. This study
                      aims to identify the range of technically and economically
                      feasible system configurations that municipalities can adopt
                      under such uncertainties. To do so, we employ modeling to
                      generate alternatives (MGA) approach, enabling the
                      systematic exploration of near-optimal energy system designs
                      for selected municipalities previously identified as fully
                      autonomous [4].MethodsETHOS.FINE is a Python-based,
                      open-source tool for techno-economic optimization of
                      regional energy systems [5]. The study builds upon the
                      ETHOS.FINE energy system modeling framework and applies the
                      random vector MGA method (e.g., Ref [6]) to five German
                      municipalities: Neuschoo, Ilmenau, Müden (Mosel), Urmitz,
                      and Kahl am Main. Each municipality’s energy system
                      includes renewable sources (wind, rooftop PV, open-field PV,
                      biomass, etc.), storage technologies, and conversion
                      components for electricity, heat, hydrogen, and industrial
                      process heat. Demand is modeled using fixed hourly profiles
                      for the year 2045, scaled to municipal levels. MGA is used
                      to identify near-optimal configurations that remain within a
                      $10\%$ cost margin from the optimal solution. The harmonic
                      mean of squared Euclidean distances (HMSED) [7] is used to
                      select maximally distinct alternatives, with iterations
                      stopped when the relative diversity of solutions falls below
                      a $1\%$ threshold.ResultsThe number and diversity of
                      near-optimal alternatives varied significantly among
                      municipalities. Neuschoo and Ilmenau (low-cost autonomy
                      cases) showed the highest flexibility, with 42 and 36
                      distinct MGA solutions, respectively. In these cases,
                      renewable technology capacities—especially open-field PV
                      and wind could vary widely without exceeding the $10\%$ cost
                      margin, indicating a robust and substitutable system
                      structure. In contrast, high-cost municipalities like Urmitz
                      and Kahl am Main exhibited only 13 and 6 viable MGA
                      alternatives, respectively, with minimal variation in
                      technology capacities due to spatial constraints and limited
                      renewable potential. Flexibility in low-temperature heat and
                      industrial process heat supply was also observed in some
                      cases, particularly through varied use of heat pumps and
                      electric furnaces. However, high-cost municipalities showed
                      rigid dependence on expensive technologies like large-scale
                      batteries and heat pumps, which constrained system
                      adaptability.Neuschoo and Ilmenau show minimal variability
                      in specific system costs across 42 and 36 MGA solutions,
                      respectively, indicating stable and robust autonomy
                      pathways. Müden (medium-cost autonomous municipality),
                      despite higher overall costs, also exhibits low-cost
                      variance, suggesting consistent outcomes despite land
                      constraints. In contrast, Urmitz displays high variability
                      among its 13 MGA solutions, highlighting sensitivity to
                      local constraints and reduced planning reliability. Kahl am
                      Main’s narrow, costly solution space (only six
                      alternatives) reflects the structural difficulty of
                      achieving affordable autonomy under severe spatial and
                      technological limitations.ConclusionsThis study demonstrates
                      that MGA can effectively quantify the flexibility and
                      robustness of autonomous municipal energy systems under
                      uncertainty. Municipalities with higher renewable resource
                      availability and larger land areas can support multiple
                      viable configurations with minimal cost increases, offering
                      greater decision-making flexibility. The stability of MGA
                      solutions in Neuschoo, Ilmenau, and Müden indicates that
                      some municipalities can plan for autonomy with confidence
                      despite varying configurations. However, in municipalities
                      like Urmitz and Kahl am Main, the limited or volatile
                      solution space reveals critical constraints that challenge
                      the reliability and affordability of energy autonomy. These
                      findings emphasize the need for flexible strategies tailored
                      to local conditions. MGA thus not only uncovers alternatives
                      but also reveals the depth of structural limitations in
                      planning decentralized energy systems.References[1] M.
                      Engelken, B. Römer, M. Drescher, and I. Welpe,
                      “Transforming the energy system: Why municipalities strive
                      for energy self-sufficiency,” Energy Policy, vol. 98, pp.
                      365–377, Nov. 2016, doi: 10.1016/j.enpol.2016.07.049.[2]
                      Bundes-Klimaschutzgesetz vom 12. Dezember 2019 (BGBl. I S.
                      2513), das zuletzt durch Artikel 1 des Gesetzes vom 15. Juli
                      2024 (BGBl. 2024 I Nr. 235) geändert worden ist. 2019.
                      [Online]. Available:
                      https://www.gesetze-im-internet.de/ksg/BJNR251310019.html[3]
                      U. J. Frey, S. Sasanpour, T. Breuer, J. Buschmann, and K.-K.
                      Cao, “Tackling the multitude of uncertainties in energy
                      systems analysis by model coupling and high-performance
                      computing,” Front. Environ. Econ., vol. 3, p. 1398358,
                      Oct. 2024, doi: 10.3389/frevc.2024.1398358.[4] S. Risch et
                      al., “Scaling energy system optimizations: Techno-economic
                      assessment of energy autonomy in 11 000 German
                      municipalities,” Energy Conversion and Management, vol.
                      309, p. 118422, Jun. 2024, doi:
                      10.1016/j.enconman.2024.118422.[5] L. Welder, D. S. Ryberg,
                      L. Kotzur, T. Grube, M. Robinius, and D. Stolten,
                      “Spatio-temporal optimization of a future energy system
                      for power-to-hydrogen applications in Germany,” Energy,
                      vol. 158, pp. 1130–1149, Sep. 2018, doi:
                      10.1016/j.energy.2018.05.059.[6] N. Patankar, X.
                      Sarkela-Basset, G. Schivley, E. Leslie, and J. Jenkins,
                      “Land use trade-offs in decarbonization of electricity
                      generation in the American West,” Energy and Climate
                      Change, vol. 4, p. 100107, Dec. 2023, doi:
                      10.1016/j.egycc.2023.100107.[7] P. B. Berntsen and E.
                      Trutnevyte, “Ensuring diversity of national energy
                      scenarios: Bottom-up energy system model with Modeling to
                      Generate Alternatives,” Energy, vol. 126, pp. 886–898,
                      May 2017, doi: 10.1016/j.energy.2017.03.043.},
      month         = {Nov},
      date          = {2025-11-20},
      organization  = {9th AIEE Energy Symposium - Current
                       and Future Challanges to Energy
                       Security, Rome (Italy), 20 Nov 2025 -
                       22 Nov 2025},
      cin          = {ICE-2},
      cid          = {I:(DE-Juel1)ICE-2-20101013},
      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)1},
      url          = {https://juser.fz-juelich.de/record/1048193},
}