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Dynamic hosting capacity analysis for distributed photovoltaic resources—Framework and case study

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  • Jain, Akshay Kumar
  • Horowitz, Kelsey
  • Ding, Fei
  • Sedzro, Kwami Senam
  • Palmintier, Bryan
  • Mather, Barry
  • Jain, Himanshu

Abstract

Distributed photovoltaic systems can cause adverse distribution system impacts, including voltage violations at customer locations and thermal overload of lines, transformers, and other equipment resulting from high current. The installed capacity at which violations first occur and above which would require system upgrades is called the hosting capacity. Current static methods for determining hosting capacity tend to either consider infrequent worst-case snapshots in time and/or capture coarse time and spatial resolution. Because the duration of violations cannot be captured with these traditional methods, the metric thresholds used in these studies conservatively use the strictest constraints given in operating standards, even though both worse voltage performance and higher overloads may be temporarily acceptable. However, assessing the full details requires accurately capturing time-dependence, voltage-regulating equipment operations, and performance of advanced controls-based mitigation techniques. In this paper, we propose a dynamic distributed photovoltaic hosting capacity methodology to address these issues by conducting power flow analysis for a full year. A key contribution is the formulation of time aware metrics to take these annual results and identify the hosting capacity. Through a case study, we show that this approach can more fully capture grid impacts of distributed photovoltaic than traditional methods and the dynamic hosting capacity was 60%–200% higher than the static hosting capacity in this case study.

Suggested Citation

  • Jain, Akshay Kumar & Horowitz, Kelsey & Ding, Fei & Sedzro, Kwami Senam & Palmintier, Bryan & Mather, Barry & Jain, Himanshu, 2020. "Dynamic hosting capacity analysis for distributed photovoltaic resources—Framework and case study," Applied Energy, Elsevier, vol. 280(C).
  • Handle: RePEc:eee:appene:v:280:y:2020:i:c:s0306261920311351
    DOI: 10.1016/j.apenergy.2020.115633
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    References listed on IDEAS

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    1. Tiago Elias Castelo de Oliveira & Math Bollen & Paulo Fernando Ribeiro & Pedro M. S. de Carvalho & Antônio C. Zambroni & Benedito D. Bonatto, 2019. "The Concept of Dynamic Hosting Capacity for Distributed Energy Resources: Analytics and Practical Considerations," Energies, MDPI, vol. 12(13), pages 1-18, July.
    2. Ismael, Sherif M. & Abdel Aleem, Shady H.E. & Abdelaziz, Almoataz Y. & Zobaa, Ahmed F., 2019. "State-of-the-art of hosting capacity in modern power systems with distributed generation," Renewable Energy, Elsevier, vol. 130(C), pages 1002-1020.
    3. Ji, Haoran & Wang, Chengshan & Li, Peng & Zhao, Jinli & Song, Guanyu & Ding, Fei & Wu, Jianzhong, 2018. "A centralized-based method to determine the local voltage control strategies of distributed generator operation in active distribution networks," Applied Energy, Elsevier, vol. 228(C), pages 2024-2036.
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    Cited by:

    1. Rajabi, A. & Elphick, S. & David, J. & Pors, A. & Robinson, D., 2022. "Innovative approaches for assessing and enhancing the hosting capacity of PV-rich distribution networks: An Australian perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    2. Jin-Sol Song & Ji-Soo Kim & Barry Mather & Chul-Hwan Kim, 2021. "Hosting Capacity Improvement Method Using MV–MV Solid-State-Transformer," Energies, MDPI, vol. 14(3), pages 1-12, January.
    3. Jude Suchithra & Amin Rajabi & Duane A. Robinson, 2024. "Enhancing PV Hosting Capacity of Electricity Distribution Networks Using Deep Reinforcement Learning-Based Coordinated Voltage Control," Energies, MDPI, vol. 17(20), pages 1-27, October.
    4. Hwang, Hyunkyeong & Yoon, Ahyun & Yoon, Yongtae & Moon, Seungil, 2023. "Demand response of HVAC systems for hosting capacity improvement in distribution networks: A comprehensive review and case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 187(C).
    5. Md Tariqul Islam & M. J. Hossain, 2023. "Artificial Intelligence for Hosting Capacity Analysis: A Systematic Literature Review," Energies, MDPI, vol. 16(4), pages 1-33, February.
    6. Ang Dong & Seon-Keun Lee, 2024. "Research on a Distributed Photovoltaic Two-Level Planning Method Based on the SCMPSO Algorithm," Energies, MDPI, vol. 17(13), pages 1-20, July.
    7. Jude Suchithra & Duane Robinson & Amin Rajabi, 2023. "Hosting Capacity Assessment Strategies and Reinforcement Learning Methods for Coordinated Voltage Control in Electricity Distribution Networks: A Review," Energies, MDPI, vol. 16(5), pages 1-28, March.
    8. Ramón E. De-Jesús-Grullón & Rafael Omar Batista Jorge & Abraham Espinal Serrata & Justin Eladio Bueno Díaz & Juan José Pichardo Estévez & Nestor Francisco Guerrero-Rodríguez, 2024. "Modeling and Simulation of Distribution Networks with High Renewable Penetration in Open-Source Software: QGIS and OpenDSS," Energies, MDPI, vol. 17(12), pages 1-19, June.
    9. Zabihinia Gerdroodbari, Yasin & Khorasany, Mohsen & Razzaghi, Reza, 2022. "Dynamic PQ Operating Envelopes for prosumers in distribution networks," Applied Energy, Elsevier, vol. 325(C).

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