IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i5p1709-d324888.html
   My bibliography  Save this article

Probabilistic Assessment of Distribution Network with High Penetration of Distributed Generators

Author

Listed:
  • Ziqiang Zhou

    (State Grid Zhejiang Electric Power Research Institute, Hangzhou 310014, Zhejiang Province, China)

  • Fei Tang

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 420072, Hubei Province, China)

  • Dichen Liu

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 420072, Hubei Province, China)

  • Chenxu Wang

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 420072, Hubei Province, China)

  • Xin Gao

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 420072, Hubei Province, China)

Abstract

Over the past decades, the deployment of distributed generations (DGs) in distribution systems has grown dramatically due to the concerns of environment and carbon emission. However, a large number of DGs have introduced more uncertainties and challenges into the operation of distribution networks. Due to the stochastic nature of renewable energy resources, probabilistic tools are needed to assist systems operators in analyzing operating states of systems. To address this issue, we develop a probabilistic framework for the assessment of systems. In the proposed framework, the uncertainties of DGs outputs are modeled using short term forecast values and errors. Moreover, an adaptive cluster-based cumulant method is developed for probabilistic load flow calculation. The performance of the proposed framework is evaluated in the IEEE 33-bus system and PG&E 69-bus system. The results indicate that the proposed framework could yield accurate results with a reasonable computational burden. The excellent performance of the proposed framework in estimating technological violations can help system operators underlying the potential risks of systems.

Suggested Citation

  • Ziqiang Zhou & Fei Tang & Dichen Liu & Chenxu Wang & Xin Gao, 2020. "Probabilistic Assessment of Distribution Network with High Penetration of Distributed Generators," Sustainability, MDPI, vol. 12(5), pages 1-20, February.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:5:p:1709-:d:324888
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/5/1709/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/5/1709/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Haque, M. Mejbaul & Wolfs, Peter, 2016. "A review of high PV penetrations in LV distribution networks: Present status, impacts and mitigation measures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 1195-1208.
    2. Jurasz, Jakub & Beluco, Alexandre & Canales, Fausto A., 2018. "The impact of complementarity on power supply reliability of small scale hybrid energy systems," Energy, Elsevier, vol. 161(C), pages 737-743.
    3. Wang, Zhiwen & Shen, Chen & Liu, Feng, 2018. "A conditional model of wind power forecast errors and its application in scenario generation," Applied Energy, Elsevier, vol. 212(C), pages 771-785.
    4. Prusty, B Rajanarayan & Jena, Debashisha, 2017. "A critical review on probabilistic load flow studies in uncertainty constrained power systems with photovoltaic generation and a new approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 1286-1302.
    5. Arman Oshnoei & Rahmat Khezri & Mehrdad Tarafdar Hagh & Kuaanan Techato & SM Muyeen & Omid Sadeghian, 2018. "Direct Probabilistic Load Flow in Radial Distribution Systems Including Wind Farms: An Approach Based on Data Clustering," Energies, MDPI, vol. 11(2), pages 1-19, February.
    6. Zhang, Shenxi & Cheng, Haozhong & Li, Ke & Tai, Nengling & Wang, Dan & Li, Furong, 2018. "Multi-objective distributed generation planning in distribution network considering correlations among uncertainties," Applied Energy, Elsevier, vol. 226(C), pages 743-755.
    7. Kabir, M.N. & Mishra, Y. & Bansal, R.C., 2016. "Probabilistic load flow for distribution systems with uncertain PV generation," Applied Energy, Elsevier, vol. 163(C), pages 343-351.
    8. Xie, Shiwei & Hu, Zhijian & Zhou, Daming & Li, Yan & Kong, Shunfei & Lin, Weiwei & Zheng, Yunfei, 2018. "Multi-objective active distribution networks expansion planning by scenario-based stochastic programming considering uncertain and random weight of network," Applied Energy, Elsevier, vol. 219(C), pages 207-225.
    9. Xiaoyang Deng & Jinghan He & Pei Zhang, 2017. "A Novel Probabilistic Optimal Power Flow Method to Handle Large Fluctuations of Stochastic Variables," Energies, MDPI, vol. 10(10), pages 1-21, October.
    10. Hernández, J.C. & Ruiz-Rodriguez, F.J. & Jurado, F., 2017. "Modelling and assessment of the combined technical impact of electric vehicles and photovoltaic generation in radial distribution systems," Energy, Elsevier, vol. 141(C), pages 316-332.
    11. Francisco J. Ruiz-Rodríguez & Jesús C. Hernández & Francisco Jurado, 2017. "Probabilistic Load-Flow Analysis of Biomass-Fuelled Gas Engines with Electrical Vehicles in Distribution Systems," Energies, MDPI, vol. 10(10), pages 1-23, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dichen Liu & Chenxu Wang & Fei Tang & Yixi Zhou, 2020. "Probabilistic Assessment of Hybrid Wind-PV Hosting Capacity in Distribution Systems," Sustainability, MDPI, vol. 12(6), pages 1-19, March.
    2. Li, Yinxiao & Wang, Yi & Chen, Qixin, 2020. "Study on the impacts of meteorological factors on distributed photovoltaic accommodation considering dynamic line parameters," Applied Energy, Elsevier, vol. 259(C).
    3. Ussama Assad & Muhammad Arshad Shehzad Hassan & Umar Farooq & Asif Kabir & Muhammad Zeeshan Khan & S. Sabahat H. Bukhari & Zain ul Abidin Jaffri & Judit Oláh & József Popp, 2022. "Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods," Energies, MDPI, vol. 15(6), pages 1-36, March.
    4. Samet, Haidar & Khorshidsavar, Morteza, 2018. "Analytic time series load flow," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3886-3899.
    5. Paulo M. De Oliveira-De Jesus & Mario A. Rios & Gustavo A. Ramos, 2018. "Energy Loss Allocation in Smart Distribution Systems with Electric Vehicle Integration," Energies, MDPI, vol. 11(8), pages 1-19, July.
    6. Yi, Tao & Cheng, Xiaobin & Chen, Yaxuan & Liu, Jinpeng, 2020. "Joint optimization of charging station and energy storage economic capacity based on the effect of alternative energy storage of electric vehicle," Energy, Elsevier, vol. 208(C).
    7. Hossein Shayeghi & Elnaz Shahryari & Mohammad Moradzadeh & Pierluigi Siano, 2019. "A Survey on Microgrid Energy Management Considering Flexible Energy Sources," Energies, MDPI, vol. 12(11), pages 1-26, June.
    8. Christian Thiel & Andreea Julea & Beatriz Acosta Iborra & Nerea De Miguel Echevarria & Emanuela Peduzzi & Enrico Pisoni & Jonatan J. Gómez Vilchez & Jette Krause, 2019. "Assessing the Impacts of Electric Vehicle Recharging Infrastructure Deployment Efforts in the European Union," Energies, MDPI, vol. 12(12), pages 1-23, June.
    9. Quan Li & Xin Wang & Shuaiang Rong, 2018. "Probabilistic Load Flow Method Based on Modified Latin Hypercube-Important Sampling," Energies, MDPI, vol. 11(11), pages 1-14, November.
    10. Smarajit Ghosh & Vinod Karar, 2018. "Assimilation of Optimal Sized Hybrid Photovoltaic-Biomass System by Dragonfly Algorithm with Grid," Energies, MDPI, vol. 11(7), pages 1-19, July.
    11. Ali M. Hakami & Kazi N. Hasan & Mohammed Alzubaidi & Manoj Datta, 2022. "A Review of Uncertainty Modelling Techniques for Probabilistic Stability Analysis of Renewable-Rich Power Systems," Energies, MDPI, vol. 16(1), pages 1-26, December.
    12. Kharrazi, A. & Sreeram, V. & Mishra, Y., 2020. "Assessment techniques of the impact of grid-tied rooftop photovoltaic generation on the power quality of low voltage distribution network - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 120(C).
    13. Kegang Zhao & Jinghao Bei & Yanwei Liu & Zhihao Liang, 2019. "Development of Global Optimization Algorithm for Series-Parallel PHEV Energy Management Strategy Based on Radau Pseudospectral Knotting Method," Energies, MDPI, vol. 12(17), pages 1-23, August.
    14. Benjamin Böckl & Matthias Greiml & Lukas Leitner & Patrick Pichler & Lukas Kriechbaum & Thomas Kienberger, 2019. "HyFlow—A Hybrid Load Flow-Modelling Framework to Evaluate the Effects of Energy Storage and Sector Coupling on the Electrical Load Flows," Energies, MDPI, vol. 12(5), pages 1-25, March.
    15. Ehsan, Ali & Yang, Qiang, 2019. "State-of-the-art techniques for modelling of uncertainties in active distribution network planning: A review," Applied Energy, Elsevier, vol. 239(C), pages 1509-1523.
    16. Mohammad Rayati & Pasquale De Falco & Daniela Proto & Mokhtar Bozorg & Mauro Carpita, 2021. "Generation Data of Synthetic High Frequency Solar Irradiance for Data-Driven Decision-Making in Electrical Distribution Grids," Energies, MDPI, vol. 14(16), pages 1-21, August.
    17. Jin-Xin Ou-Yang & Xiao-Xuan Long & Xue Du & Yan-Bo Diao & Meng-Yang Li, 2019. "Voltage Control Method for Active Distribution Networks Based on Regional Power Coordination," Energies, MDPI, vol. 12(22), pages 1-23, November.
    18. Yongchun Yang & Xiaodan Wang & Jingjing Luo & Jie Duan & Yajing Gao & Hong Li & Xiangning Xiao, 2017. "Multi-Objective Coordinated Planning of Distributed Generation and AC/DC Hybrid Distribution Networks Based on a Multi-Scenario Technique Considering Timing Characteristics," Energies, MDPI, vol. 10(12), pages 1-29, December.
    19. Zhang, Shenxi & Cheng, Haozhong & Wang, Dan & Zhang, Libo & Li, Furong & Yao, Liangzhong, 2018. "Distributed generation planning in active distribution network considering demand side management and network reconfiguration," Applied Energy, Elsevier, vol. 228(C), pages 1921-1936.
    20. Hamza Mubarak & Nurulafiqah Nadzirah Mansor & Hazlie Mokhlis & Mahazani Mohamad & Hasmaini Mohamad & Munir Azam Muhammad & Mohammad Al Samman & Suhail Afzal, 2021. "Optimum Distribution System Expansion Planning Incorporating DG Based on N-1 Criterion for Sustainable System," Sustainability, MDPI, vol. 13(12), pages 1-24, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:5:p:1709-:d:324888. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.