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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
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    References listed on IDEAS

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    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.
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    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.

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