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Direct Probabilistic Load Flow in Radial Distribution Systems Including Wind Farms: An Approach Based on Data Clustering

Author

Listed:
  • Arman Oshnoei

    (Faculty of Electrical and Computer Engineering, Shahid Beheshti University, Tehran 1983969411, Iran)

  • Rahmat Khezri

    (Department of Electrical and Computer Engineering, University of Kurdistan, Sanandaj, Kurdistan 6617715177, Iran)

  • Mehrdad Tarafdar Hagh

    (Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 5166616471, Iran)

  • Kuaanan Techato

    (Faculty of Environmental Management, Prince of Songkla University, Songkhla 90110, Thailand)

  • SM Muyeen

    (Department of Electrical and Computer Engineering, Curtin University, Perth, WA 6845, Australia)

  • Omid Sadeghian

    (Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 5166616471, Iran)

Abstract

The ongoing study aims to establish a direct probabilistic load flow (PLF) for the analysis of wind integrated radial distribution systems. Because of the stochastic output power of wind farms, it is very important to find a method which can reduce the calculation burden significantly, without having compromising the accuracy of results. In the proposed approach, a K-means based data clustering algorithm is employed, in which all data points are bunched into desired clusters. In this regard, probable agents are selected to run the PLF algorithm. The clustered data are used to employ the Monte Carlo simulation (MCS) method. In this paper, the analysis is performed in terms of simulation run-time. Also, this research follows a two-fold aim. In the first stage, the superiority of data clustering-based MCS over the unsorted data MCS is demonstrated properly. Moreover, the impact of data clustering-based MCS and unsorted data-based MCS is investigated using an indirect probabilistic forward/backward sweep (PFBS) method. Thus, in the second stage, the simulation run-time comparison is carried out rigorously between the proposed direct PLF and the indirect PFBS method to examine the computational burden effects. Simulation results are exhibited on the IEEE 33-bus and 69-bus radial distribution systems.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:2:p:310-:d:129755
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    References listed on IDEAS

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    1. Vasiliki Vita, 2017. "Development of a Decision-Making Algorithm for the Optimum Size and Placement of Distributed Generation Units in Distribution Networks," Energies, MDPI, vol. 10(9), pages 1-13, September.
    2. Ahmed Al Ameri & Aouchenni Ounissa & Cristian Nichita & Aouzellag Djamal, 2017. "Power Loss Analysis for Wind Power Grid Integration Based on Weibull Distribution," Energies, MDPI, vol. 10(4), pages 1-16, April.
    3. 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.
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    Cited by:

    1. 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.
    2. Van Ky Huynh & Van Duong Ngo & Dinh Duong Le & Nhi Thi Ai Nguyen, 2018. "Probabilistic Power Flow Methodology for Large-Scale Power Systems Incorporating Renewable Energy Sources," Energies, MDPI, vol. 11(10), pages 1-12, October.
    3. Zain Anwer Memon & Riccardo Trinchero & Yanzhao Xie & Flavio G. Canavero & Igor S. Stievano, 2020. "An Iterative Scheme for the Power-Flow Analysis of Distribution Networks based on Decoupled Circuit Equivalents in the Phasor Domain," Energies, MDPI, vol. 13(2), pages 1-16, January.
    4. Nikolaos Koutsoukis & Pavlos Georgilakis, 2019. "A Chance-Constrained Multistage Planning Method for Active Distribution Networks," Energies, MDPI, vol. 12(21), pages 1-19, October.
    5. 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.
    6. Ayşe Aybike Şeker & Tuba Gözel & Mehmet Hakan Hocaoğlu, 2021. "BIBC Matrix Modification for Network Topology Changes: Reconfiguration Problem Implementation," Energies, MDPI, vol. 14(10), pages 1-16, May.

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