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Stochastic reactive power dispatch in hybrid power system with intermittent wind power generation

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  • Taghavi, Reza
  • Seifi, Ali Reza
  • Samet, Haidar

Abstract

Environmental concerns besides fuel costs are the predominant reasons for unprecedented escalating integration of wind turbine on power systems. Operation and planning of power systems are affected by this type of energy due to the intermittent nature of wind speed inputs with high uncertainty in the optimization output variables. Consequently, in order to model this high inherent uncertainty, a PRPO (probabilistic reactive power optimization) framework should be devised. Although MC (Monte-Carlo) techniques can solve the PRPO with high precision, PEMs (point estimate methods) can preserve the accuracy to attain reasonable results when diminishing the computational effort. Also, this paper introduces a methodology for optimally dispatching the reactive power in the transmission system, while minimizing the active power losses. The optimization problem is formulated as a LFP (linear fuzzy programing). The core of the problem lay on generation of 2m + 1 point estimates for solving PRPO, where n is the number of input stochastic variables. The proposed methodology is investigated using the IEEE-14 bus test system equipped with HVDC (high voltage direct current), UPFC (unified power flow controller) and DFIG (doubly fed induction generator) devices. The accuracy of the method is demonstrated in the case study.

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  • Taghavi, Reza & Seifi, Ali Reza & Samet, Haidar, 2015. "Stochastic reactive power dispatch in hybrid power system with intermittent wind power generation," Energy, Elsevier, vol. 89(C), pages 511-518.
    • Handle: RePEc:eee:energy:v:89:y:2015:i:c:p:511-518
    DOI: 10.1016/j.energy.2015.06.018
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    References listed on IDEAS

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    Cited by:

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    2. Ji, Ling & Huang, Guo-He & Huang, Lu-Cheng & Xie, Yu-Lei & Niu, Dong-Xiao, 2016. "Inexact stochastic risk-aversion optimal day-ahead dispatch model for electricity system management with wind power under uncertainty," Energy, Elsevier, vol. 109(C), pages 920-932.
    3. Zhang, Wenjie & Gandhi, Oktoviano & Quan, Hao & Rodríguez-Gallegos, Carlos D. & Srinivasan, Dipti, 2018. "A multi-agent based integrated volt-var optimization engine for fast vehicle-to-grid reactive power dispatch and electric vehicle coordination," Applied Energy, Elsevier, vol. 229(C), pages 96-110.
    4. Sharma, Akanksha & Jain, Sanjay K., 2021. "Day-ahead optimal reactive power ancillary service procurement under dynamic multi-objective framework in wind integrated deregulated power system," Energy, Elsevier, vol. 223(C).
    5. Kim, H.Y. & Kim, M.K., 2017. "Optimal generation rescheduling for meshed AC/HIS grids with multi-terminal voltage source converter high voltage direct current and battery energy storage system," Energy, Elsevier, vol. 119(C), pages 309-321.
    6. Samet, Haidar, 2016. "Evaluation of digital metering methods used in protection and reactive power compensation of micro-grids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 260-279.
    7. Samet, Haidar & Khorshidsavar, Morteza, 2018. "Analytic time series load flow," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3886-3899.

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