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A Probabilistic Method for Optimal Power Systems Planning with Wind Generators

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Transactions on Engineering Technologies

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Abstract

Radial Distribution Systems (RDS) connect a large number of renewable generators that are inherently uncertain. From being unidirectional power flow systems, RDS now enable bi-directional power flow. Depending upon availability of power from renewables, they receive or feed power to the connected transmission system. RDS optimal power flow (OPF), is an important tool in this new era for utilities, to minimize losses and operate efficiently. With large scale integration of wind generators to distribution systems, they must be appropriately represented using probabilistic models capturing their intermittent nature in these OPF algorithms. This paper proposes characterizing the solution of a Probabilistic Optimal Power Flow (P-OPF) for RDS using the Cumulant Method. This method makes it possible to linearly relate the probabilistic parameters of renewables at the optimal solution point to the state of the RDS. To assess the accuracy of the proposed P-OPF Cumulant Method, wind generators and system probabilistic data are incorporated in a 33-bus and 129-bus test system. The results are compared with those of Monte Carlo simulations (MCS). It is shown that the proposed method possesses high degree of accuracy, is significantly faster and more practical than an MCS approach.

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Acknowledgments

Financial Support: This work was supported in part by the NSERC Discovery and Wind Energy Strategic Network grants to Bala Venkatesh.

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Authors and Affiliations

  1. Centre for Urban Energy, Ryerson University, 350 Victoria Street, Toronto, M5B 2K3, Canada

    Maryam Dadkhah & Bala Venkatesh

Authors
  1. Maryam Dadkhah
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  2. Bala Venkatesh
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Corresponding author

Correspondence to Bala Venkatesh .

Editor information

Editors and Affiliations

  1. Department of Multimedia Engineering, College of Engineering, Mokpo National University, Mokpo, Jeonnam, Korea, Republic of (South Korea)

    Gi-Chul Yang

  2. Unit 1, 1/F IAENG Secretariat, International Association of Engine, Hong Kong, Hong Kong SAR

    Sio-Iong Ao

  3. Department of Applied Mathematics and Computing, Cranfield University, Cranfield, Bedfordshire, United Kingdom

    Len Gelman

Appendix

Appendix

Table 6 presents the mean values used for the load demands, and Table 7 presents the feeder data.

Table 6 33 bus RDS—mean value of the loads
Full size table
Table 7 33 bus RDS—line data
Full size table

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Dadkhah, M., Venkatesh, B. (2014). A Probabilistic Method for Optimal Power Systems Planning with Wind Generators. In: Yang, GC., Ao, SI., Gelman, L. (eds) Transactions on Engineering Technologies. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-8832-8_27

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  • DOI: https://doi.org/10.1007/978-94-017-8832-8_27

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  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-017-8831-1

  • Online ISBN: 978-94-017-8832-8

  • eBook Packages: EngineeringEngineering (R0)

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