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Designing electricity distribution networks: The impact of demand coincidence

Author

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  • Gust, Gunther
  • Schlüter, Alexander
  • Feuerriegel, Stefan
  • Úbeda, Ignacio
  • Lee, Jonathan T.
  • Neumann, Dirk

Abstract

With the global effort to reduce carbon emissions, clean technologies such as electric vehicles and heat pumps are increasingly introduced into electricity distribution networks. These technologies considerably increase electricity flows and can lead to more coincident electricity demand. In this paper, we analyze how such increases in demand coincidence impact future distribution network investments. For this purpose, we develop a novel model for designing electricity distribution networks, called the distribution network reconfiguration problem with line-specific demand coincidence (DNRP-LSDC). Our analysis is two-fold: (1) We apply our model to a large sample of real-world networks from a Swiss distribution network operator. We find that a high demand coincidence due to, for example, a large-scale uptake of electric vehicles, requires a substantial amount of new network line construction and increases average network cost by 84% in comparison to the status quo. (2) We use a set of synthetic networks to isolate the effect of specific network characteristics. Here, we show that high coincidence has a more detrimental effect on large networks and on networks with low geographic consumer densities, as present in, e.g., rural areas. We also show that expansion measures are robust to variations in the cost parameters. Our results demonstrate the necessity of designing policies and operational protocols that reduce demand coincidence. Moreover, our findings show that operators of distribution networks must consider the demand coincidence of new electricity uses and adapt investment budgets accordingly. Here, our solution algorithms for the DNRP-LSDC problem can support operators of distribution networks in strategic and operational network design tasks.

Suggested Citation

  • Gust, Gunther & Schlüter, Alexander & Feuerriegel, Stefan & Úbeda, Ignacio & Lee, Jonathan T. & Neumann, Dirk, 2024. "Designing electricity distribution networks: The impact of demand coincidence," European Journal of Operational Research, Elsevier, vol. 315(1), pages 271-288.
  • Handle: RePEc:eee:ejores:v:315:y:2024:i:1:p:271-288
    DOI: 10.1016/j.ejor.2023.11.029
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    Cited by:

    1. Ameling, Justus & Gust, Gunther, 2024. "Automated feeder routing for underground electricity distribution networks based on aerial images," European Journal of Operational Research, Elsevier, vol. 318(2), pages 629-641.
    2. Rüde, Lenard & Wussow, Moritz & Heleno, Miguel & Gust, Gunther & Neumann, Dirk, 2024. "Estimating electrical distribution network length and capital investment needs from real-world topologies and land cover data," Energy Policy, Elsevier, vol. 195(C).

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