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Identification of potential off-grid municipalities with 100% renewable energy supply

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  • Weinand, Jann
  • Ried, Sabrina
  • Kleinebrahm, Max
  • McKenna, Russell
  • Fichtner, Wolf

Abstract

An increasing number of municipalities are striving for energy autonomy. This study determines in which municipalities and at what additional cost energy autonomy is feasible for a case study of Germany. An existing municipal energy system optimization model is extended to include the personal transport, industrial and commercial sectors. A machine learning approach identifies a regression model among 19 methods, which is best suited for the transfer of individual optimization results to all municipalities. The resulting levelized cost of energy (LCOE) from the optimization of 15 case studies are transferred using a stepwise linear regression model. The regression model shows a mean absolute percentage error of 12.5%. The study demonstrates that energy autonomy is technically feasible in 6,314 (56%) municipalities. Thereby, the LCOEs increase in the autonomous case on average by 0.41 €/kWh compared to the minimum cost scenario. Apart from energy demand, base-load-capable bioenergy and deep geothermal energy appear to have the greatest influence on the LCOEs. This study represents a starting point for defining possible scenarios in studies of future national energy system or transmission grid expansion planning, which for the first time consider completely energy autonomous municipalities.

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  • Weinand, Jann & Ried, Sabrina & Kleinebrahm, Max & McKenna, Russell & Fichtner, Wolf, 2020. "Identification of potential off-grid municipalities with 100% renewable energy supply," Working Paper Series in Production and Energy 40, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
    • Handle: RePEc:zbw:kitiip:40
    DOI: 10.5445/IR/1000118013
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    References listed on IDEAS

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    1. McKenna, R. & Hollnaicher, S. & Fichtner, W., 2014. "Cost-potential curves for onshore wind energy: A high-resolution analysis for Germany," Applied Energy, Elsevier, vol. 115(C), pages 103-115.
    2. Weinand, Jann Michael & McKenna, Russell & Kleinebrahm, Max & Mainzer, Kai, 2019. "Assessing the contribution of simultaneous heat and power generation from geothermal plants in off-grid municipalities," Applied Energy, Elsevier, vol. 255(C).
    3. Engelken, Maximilian & Römer, Benedikt & Drescher, Marcus & Welpe, Isabell, 2016. "Transforming the energy system: Why municipalities strive for energy self-sufficiency," Energy Policy, Elsevier, vol. 98(C), pages 365-377.
    4. Braeuer, Fritz & Rominger, Julian & McKenna, Russell & Fichtner, Wolf, 2019. "Battery storage systems: An economic model-based analysis of parallel revenue streams and general implications for industry," Applied Energy, Elsevier, vol. 239(C), pages 1424-1440.
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    Cited by:

    1. Xu, Da & Yuan, Zhe-Li & Bai, Ziyi & Wu, Zhibin & Chen, Shuangyin & Zhou, Ming, 2022. "Optimal operation of geothermal-solar-wind renewables for community multi-energy supplies," Energy, Elsevier, vol. 249(C).
    2. McGookin, Connor & Ó Gallachóir, Brian & Byrne, Edmond, 2021. "An innovative approach for estimating energy demand and supply to inform local energy transitions," Energy, Elsevier, vol. 229(C).
    3. Kleinebrahm, Max & Weinand, Jann Michael & Naber, Elias & McKenna, Russell & Ardone, Armin, 2023. "Analysing municipal energy system transformations in line with national greenhouse gas reduction strategies," Applied Energy, Elsevier, vol. 332(C).

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    Keywords

    Energy autonomy; renewable energy; geothermal power generation; electric vehicles; vehicle-to-grid; mixed integer linear programming; regression analysis;
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