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Synthetic Models of Distribution Networks Based on Open Data and Georeferenced Information

Author

Listed:
  • Giuditta Pisano

    (Department of Electrical and Electronic Engineering, University of Cagliari, 09100 Cagliari, Italy)

  • Nayeem Chowdhury

    (Department of Electrical and Electronic Engineering, University of Cagliari, 09100 Cagliari, Italy
    Enel Produzione Società per Azioni (S.p.A), 56122 Pisa, Italy)

  • Massimiliano Coppo

    (Department of Industrial Engineering, University of Padova, 35131 Padova, Italy)

  • Nicola Natale

    (Department of Electrical and Electronic Engineering, University of Cagliari, 09100 Cagliari, Italy)

  • Giacomo Petretto

    (Enel Produzione Società per Azioni (S.p.A), 56122 Pisa, Italy)

  • Gian Giuseppe Soma

    (Department of Electrical and Electronic Engineering, University of Cagliari, 09100 Cagliari, Italy)

  • Roberto Turri

    (Department of Industrial Engineering, University of Padova, 35131 Padova, Italy)

  • Fabrizio Pilo

    (Department of Electrical and Electronic Engineering, University of Cagliari, 09100 Cagliari, Italy)

Abstract

Many planning and operation studies that aim at fully assessing and optimizing the performance of the distribution grids, in response to the current trends, cannot ignore grid limitations. Modelling the distribution system, by including the electrical characteristics of the network (e.g., topology) and end user behaviors, has become complex, but essential, for all conventional and emerging actors/players of power systems (i.e., system and market operators, regulators, new market parties as service providers, aggregators, researchers, etc.). This paper deals with a methodology that, starting from publicly available open data on the energy consumption of a region or wider area, is capable to obtain reasonable load and generation profiles for the network supplied by each primary substation in the region/area. Furthermore, by combining these profiles with territorial and socio-economic information, the proposed methodology is able to model the network in terms of lines, conductors, loads and generators. The results of this procedure are the synthetic networks of the real distribution networks, that do not correspond exactly to the actual networks, but can characterize them in a realistic way. Such models can be used for all the kind of optimization studies that need to check the grid limitations. Results derived from Italian test cases are presented and discussed.

Suggested Citation

  • Giuditta Pisano & Nayeem Chowdhury & Massimiliano Coppo & Nicola Natale & Giacomo Petretto & Gian Giuseppe Soma & Roberto Turri & Fabrizio Pilo, 2019. "Synthetic Models of Distribution Networks Based on Open Data and Georeferenced Information," Energies, MDPI, vol. 12(23), pages 1-24, November.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:23:p:4500-:d:291078
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    References listed on IDEAS

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    1. Anna Rita Di Fazio & Mario Russo & Michele De Santis, 2019. "Zoning Evaluation for Voltage Optimization in Distribution Networks with Distributed Energy Resources," Energies, MDPI, vol. 12(3), pages 1-28, January.
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    Cited by:

    1. Jasiūnas, Justinas & Heikkinen, Tatu & Lund, Peter D. & Láng-Ritter, Ilona, 2023. "Resilience of electric grid to extreme wind: Considering local details at national scale," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    2. Zhai, Chengwei & Chen, Thomas Ying-jeh & White, Anna Grace & Guikema, Seth David, 2021. "Power outage prediction for natural hazards using synthetic power distribution systems," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    3. Srđan Skok & Ahmed Mutapčić & Renata Rubesa & Mario Bazina, 2020. "Transmission Power System Modeling by Using Aggregated Distributed Generation Model Based on a TSO—DSO Data Exchange Scheme," Energies, MDPI, vol. 13(15), pages 1-15, August.
    4. Silva, Walquiria N. & Bandória, Luís H.T. & Dias, Bruno H. & de Almeida, Madson C. & de Oliveira, Leonardo W., 2023. "Generating realistic load profiles in smart grids: An approach based on nonlinear independent component estimation (NICE) and convolutional layers," Applied Energy, Elsevier, vol. 351(C).

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