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Optimal wind power generation investment, considering voltage stability of power systems

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  • Nikkhah, Saman
  • Rabiee, Abbas

Abstract

Studies show that improper sizing and placement of wind farms (WFs) lead to undesired investment and operation costs as well as the risk of voltage instability. Thus, optimal placement of WFs and enough loading margin (LM) are important factors which ensure the voltage stability of system as well as optimal investment and expenditure for WFs development. In this paper, modal analysis is used to determine the optimal place of WFs from the voltage stability viewpoint. Moreover, a new voltage stability constrained wind energy planning (VSC-WEP) model is proposed to determine the optimal yearly wind power penetration while satisfying voltage stability constraints. A 10-years horizon is considered and the net profit from the energy procurement via the WFs' installed optimally, is maximized. Furthermore, the added capacity of WFs and the net profit are analyzed by sensitivity analyzes to investigate the impact of various technical and financial factors on the obtained results. The proposed VSC-WEP model is implemented on the IEEE New-England 39-bus test system, and solved by General Algebraic Modeling System (GAMS) optimization package. The simulation results demonstrate the capability of the proposed model for optimal determination of WFs capacity while preserving a proper LM of system.

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  • Nikkhah, Saman & Rabiee, Abbas, 2018. "Optimal wind power generation investment, considering voltage stability of power systems," Renewable Energy, Elsevier, vol. 115(C), pages 308-325.
    • Handle: RePEc:eee:renene:v:115:y:2018:i:c:p:308-325
    DOI: 10.1016/j.renene.2017.08.056
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    1. Roy, N.K. & Pota, H.R. & Hossain, M.J., 2013. "Reactive power management of distribution networks with wind generation for improving voltage stability," Renewable Energy, Elsevier, vol. 58(C), pages 85-94.
    2. Mohseni-Bonab, Seyed Masoud & Rabiee, Abbas & Mohammadi-Ivatloo, Behnam, 2016. "Voltage stability constrained multi-objective optimal reactive power dispatch under load and wind power uncertainties: A stochastic approach," Renewable Energy, Elsevier, vol. 85(C), pages 598-609.
    3. Dicorato, M. & Forte, G. & Pisani, M. & Trovato, M., 2011. "Guidelines for assessment of investment cost for offshore wind generation," Renewable Energy, Elsevier, vol. 36(8), pages 2043-2051.
    4. Esmaili, Masoud & Firozjaee, Esmail Chaktan & Shayanfar, Heidar Ali, 2014. "Optimal placement of distributed generations considering voltage stability and power losses with observing voltage-related constraints," Applied Energy, Elsevier, vol. 113(C), pages 1252-1260.
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    Cited by:

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