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A Review of Power Distribution Test Feeders in the United States and the Need for Synthetic Representative Networks

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  • Fernando E. Postigo Marcos

    (Institute for Research in Technology (IIT), Comillas Pontifical University, Madrid 28015, Spain)

  • Carlos Mateo Domingo

    (Institute for Research in Technology (IIT), Comillas Pontifical University, Madrid 28015, Spain)

  • Tomás Gómez San Román

    (Institute for Research in Technology (IIT), Comillas Pontifical University, Madrid 28015, Spain)

  • Bryan Palmintier

    (National Renewable Energy Laboratory (NREL), Golden, CO 80401, USA)

  • Bri-Mathias Hodge

    (National Renewable Energy Laboratory (NREL), Golden, CO 80401, USA)

  • Venkat Krishnan

    (National Renewable Energy Laboratory (NREL), Golden, CO 80401, USA)

  • Fernando De Cuadra García

    (Institute for Research in Technology (IIT), Comillas Pontifical University, Madrid 28015, Spain)

  • Barry Mather

    (National Renewable Energy Laboratory (NREL), Golden, CO 80401, USA)

Abstract

Under the increasing penetration of distributed energy resources and new smart network technologies, distribution utilities face new challenges and opportunities to ensure reliable operations, manage service quality, and reduce operational and investment costs. Simultaneously, the research community is developing algorithms for advanced controls and distribution automation that can help to address some of these challenges. However, there is a shortage of realistic test systems that are publically available for development, testing, and evaluation of such new algorithms. Concerns around revealing critical infrastructure details and customer privacy have severely limited the number of actual networks published and that are available for testing. In recent decades, several distribution test feeders and US-featured representative networks have been published, but the scale, complexity, and control data vary widely. This paper presents a first-of-a-kind structured literature review of published distribution test networks with a special emphasis on classifying their main characteristics and identifying the types of studies for which they have been used. This both aids researchers in choosing suitable test networks for their needs and highlights the opportunities and directions for further test system development. In particular, we highlight the need for building large-scale synthetic networks to overcome the identified drawbacks of current distribution test feeders.

Suggested Citation

  • Fernando E. Postigo Marcos & Carlos Mateo Domingo & Tomás Gómez San Román & Bryan Palmintier & Bri-Mathias Hodge & Venkat Krishnan & Fernando De Cuadra García & Barry Mather, 2017. "A Review of Power Distribution Test Feeders in the United States and the Need for Synthetic Representative Networks," Energies, MDPI, vol. 10(11), pages 1-14, November.
  • Handle: RePEc:gam:jeners:v:10:y:2017:i:11:p:1896-:d:119454
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    1. Yuan, Zhao & Hesamzadeh, Mohammad Reza, 2017. "Hierarchical coordination of TSO-DSO economic dispatch considering large-scale integration of distributed energy resources," Applied Energy, Elsevier, vol. 195(C), pages 600-615.
    2. Venkatesan, Naveen & Solanki, Jignesh & Solanki, Sarika Khushalani, 2012. "Residential Demand Response model and impact on voltage profile and losses of an electric distribution network," Applied Energy, Elsevier, vol. 96(C), pages 84-91.
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    9. Leandro Lind & Rafael Cossent & José Pablo Chaves‐Ávila & Tomás Gómez San Román, 2019. "Transmission and distribution coordination in power systems with high shares of distributed energy resources providing balancing and congestion management services," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 8(6), November.
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    11. Martin Spitzer & Jonas Schlund & Elpiniki Apostolaki-Iosifidou & Marco Pruckner, 2019. "Optimized Integration of Electric Vehicles in Low Voltage Distribution Grids," Energies, MDPI, vol. 12(21), pages 1-19, October.
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    14. Hartvigsson, Elias & Taljegard, Maria & Odenberger, Mikael & Chen, Peiyuan, 2022. "A large-scale high-resolution geographic analysis of impacts of electric vehicle charging on low-voltage grids," Energy, Elsevier, vol. 261(PA).
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    17. Anna Traupmann & Thomas Kienberger, 2020. "Test Grids for the Integration of RES—A Contribution for the European Context," Energies, MDPI, vol. 13(20), pages 1-29, October.

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