Gast ::
| Hauptseite > Online First > Identifying Flexibility in Autonomous Municipal Energy Systems Using Modeling to Generate Alternatives |
| Hauptseite > Online First > Identifying Flexibility in Autonomous Municipal Energy Systems Using Modeling to Generate Alternatives |
| Abstract | FZJ-2025-04555 |
; ; ; ;
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.
|
The record appears in these collections: |