IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i11p2981-d179784.html
   My bibliography  Save this article

Distributionally Robust Distributed Generation Hosting Capacity Assessment in Distribution Systems

Author

Listed:
  • Mohammad Seydali Seyf Abad

    (School of Electrical and Information Engineering, The University of Sydney, Sydney NSW 2006, Australia)

  • Jin Ma

    (School of Electrical and Information Engineering, The University of Sydney, Sydney NSW 2006, Australia)

  • Ahmad Shabir Ahmadyar

    (School of Electrical and Information Engineering, The University of Sydney, Sydney NSW 2006, Australia)

  • Hesamoddin Marzooghi

    (School of Engineering and Technology, Central Queensland University (CQ University), Brisbane, QLD 4000, Australia)

Abstract

Uncertainties associated with the loads and the output power of distributed generations create challenges in quantifying the integration limits of distributed generations in distribution networks, i.e., hosting capacity. To address this, we propose a distributionally robust optimization-based method to determine the hosting capacity considering the voltage rise, thermal capacity of the feeders and short circuit level constraints. In the proposed method, the uncertain variables are modeled as stochastic variables following ambiguous distributions defined based on the historical data. The distributionally robust optimization model guarantees that the probability of the constraint violation does not exceed a given risk level, which can control robustness of the solution. To solve the distributionally robust optimization model of the hosting capacity, we reformulated it as a joint chance constrained problem, which is solved using the sample average approximation technique. To demonstrate the efficacy of the proposed method, a modified IEEE 33-bus distribution system is used as the test-bed. Simulation results demonstrate how the sample size of historical data affects the hosting capacity. Furthermore, using the proposed method, the impact of electric vehicles aggregated demand and charging stations are investigated on the hosting capacity of different distributed generation technologies.

Suggested Citation

  • Mohammad Seydali Seyf Abad & Jin Ma & Ahmad Shabir Ahmadyar & Hesamoddin Marzooghi, 2018. "Distributionally Robust Distributed Generation Hosting Capacity Assessment in Distribution Systems," Energies, MDPI, vol. 11(11), pages 1-19, November.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:2981-:d:179784
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/11/2981/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/11/11/2981/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Draxl, Caroline & Clifton, Andrew & Hodge, Bri-Mathias & McCaa, Jim, 2015. "The Wind Integration National Dataset (WIND) Toolkit," Applied Energy, Elsevier, vol. 151(C), pages 355-366.
    2. Ammar Arshad & Martin Lindner & Matti Lehtonen, 2017. "An Analysis of Photo-Voltaic Hosting Capacity in Finnish Low Voltage Distribution Networks," Energies, MDPI, vol. 10(11), pages 1-16, October.
    3. B. K. Pagnoncelli & S. Ahmed & A. Shapiro, 2009. "Sample Average Approximation Method for Chance Constrained Programming: Theory and Applications," Journal of Optimization Theory and Applications, Springer, vol. 142(2), pages 399-416, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mohammad Seydali Seyf Abad & Jennifer A. Hayward & Saad Sayeef & Peter Osman & Jin Ma, 2021. "Tidal Energy Hosting Capacity in Australia’s Future Energy Mix," Energies, MDPI, vol. 14(5), pages 1-20, March.

    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. Pedro, Hugo T.C. & Lim, Edwin & Coimbra, Carlos F.M., 2018. "A database infrastructure to implement real-time solar and wind power generation intra-hour forecasts," Renewable Energy, Elsevier, vol. 123(C), pages 513-525.
    2. Sward, J.A. & Ault, T.R. & Zhang, K.M., 2023. "Spatial biases revealed by LiDAR in a multiphysics WRF ensemble designed for offshore wind," Energy, Elsevier, vol. 262(PA).
    3. Weifeng Xu & Bing Yu & Qing Song & Liguo Weng & Man Luo & Fan Zhang, 2022. "Economic and Low-Carbon-Oriented Distribution Network Planning Considering the Uncertainties of Photovoltaic Generation and Load Demand to Achieve Their Reliability," Energies, MDPI, vol. 15(24), pages 1-15, December.
    4. Munir Ali Elfarra & Mustafa Kaya, 2018. "Comparison of Optimum Spline-Based Probability Density Functions to Parametric Distributions for the Wind Speed Data in Terms of Annual Energy Production," Energies, MDPI, vol. 11(11), pages 1-15, November.
    5. Craig, Michael & Guerra, Omar J. & Brancucci, Carlo & Pambour, Kwabena Addo & Hodge, Bri-Mathias, 2020. "Valuing intra-day coordination of electric power and natural gas system operations," Energy Policy, Elsevier, vol. 141(C).
    6. Zimmerman, Ryan & Panda, Anurag & Bulović, Vladimir, 2020. "Techno-economic assessment and deployment strategies for vertically-mounted photovoltaic panels," Applied Energy, Elsevier, vol. 276(C).
    7. McManamay, Ryan A. & DeRolph, Christopher R. & Surendran-Nair, Sujithkumar & Allen-Dumas, Melissa, 2019. "Spatially explicit land-energy-water future scenarios for cities: Guiding infrastructure transitions for urban sustainability," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 880-900.
    8. Julien Walzberg & Annika Eberle, 2023. "Modeling Systems’ Disruption and Social Acceptance—A Proof-of-Concept Leveraging Reinforcement Learning," Sustainability, MDPI, vol. 15(13), pages 1-13, June.
    9. Erfan Mohagheghi & Mansour Alramlawi & Aouss Gabash & Pu Li, 2018. "A Survey of Real-Time Optimal Power Flow," Energies, MDPI, vol. 11(11), pages 1-20, November.
    10. Zhuqi Miao & Balabhaskar Balasundaram & Eduardo L. Pasiliao, 2014. "An exact algorithm for the maximum probabilistic clique problem," Journal of Combinatorial Optimization, Springer, vol. 28(1), pages 105-120, July.
    11. Lamas, Patricio & Goycoolea, Marcos & Pagnoncelli, Bernardo & Newman, Alexandra, 2024. "A target-time-windows technique for project scheduling under uncertainty," European Journal of Operational Research, Elsevier, vol. 314(2), pages 792-806.
    12. Pierre Pinson & Liyang Han & Jalal Kazempour, 2022. "Regression markets and application to energy forecasting," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(3), pages 533-573, October.
    13. Tanner, Sophia & Burnett, Wesley & Maguire, Karen & Winikoff, Justin, 2024. "Blown Away: The Influence of Wind Farms on Agricultural Land Values," 2024 Annual Meeting, July 28-30, New Orleans, LA 343970, Agricultural and Applied Economics Association.
    14. Emelogu, Adindu & Chowdhury, Sudipta & Marufuzzaman, Mohammad & Bian, Linkan & Eksioglu, Burak, 2016. "An enhanced sample average approximation method for stochastic optimization," International Journal of Production Economics, Elsevier, vol. 182(C), pages 230-252.
    15. Shawhan, Daniel & Funke, Christoph & Witkin, Steven, 2020. "Benefits of Energy Technology Innovation Part 1: Power Sector Modeling Results," RFF Working Paper Series 20-19, Resources for the Future.
    16. G. Pantuso & L. M. Hvattum, 2021. "Maximizing performance with an eye on the finances: a chance-constrained model for football transfer market decisions," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 583-611, July.
    17. Xu, Yuanyuan & Yang, Genke & Luo, Jiliang & He, Jianan & Sun, Haixin, 2022. "A multi-location short-term wind speed prediction model based on spatiotemporal joint learning," Renewable Energy, Elsevier, vol. 183(C), pages 148-159.
    18. Denholm, Paul & Nunemaker, Jacob & Gagnon, Pieter & Cole, Wesley, 2020. "The potential for battery energy storage to provide peaking capacity in the United States," Renewable Energy, Elsevier, vol. 151(C), pages 1269-1277.
    19. Yiyang Sun & Xiangwen Wang & Junjie Yang, 2022. "Modified Particle Swarm Optimization with Attention-Based LSTM for Wind Power Prediction," Energies, MDPI, vol. 15(12), pages 1-17, June.
    20. Howard, B. & Waite, M. & Modi, V., 2017. "Current and near-term GHG emissions factors from electricity production for New York State and New York City," Applied Energy, Elsevier, vol. 187(C), pages 255-271.

    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:jeners:v:11:y:2018:i:11:p:2981-:d:179784. 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.