Probabilistic load flow for distribution systems with uncertain PV generation

•Latin Hypercube Sampling with Cholesky Decomposition (LHS-CD) is used to maintain voltage profile in distribution network.•LHS-CD is efficient for complex computations.•LHS technique is verified for Australian distribution network.•LHS-CD provides accurate results with reasonably low computation ti...

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Bibliographic Details
Published inApplied energy Vol. 163; pp. 343 - 351
Main Authors Kabir, M.N., Mishra, Y., Bansal, R.C.
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.02.2016
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Summary:•Latin Hypercube Sampling with Cholesky Decomposition (LHS-CD) is used to maintain voltage profile in distribution network.•LHS-CD is efficient for complex computations.•LHS technique is verified for Australian distribution network.•LHS-CD provides accurate results with reasonably low computation time compare to Monte Carlo technique. Large integration of solar Photo Voltaic (PV) in distribution network has resulted in over-voltage problems. Several control techniques are developed to address over-voltage problem using Deterministic Load Flow (DLF). However, intermittent characteristics of PV generation require Probabilistic Load Flow (PLF) to introduce variability in analysis that is ignored in DLF. The traditional PLF techniques are not suitable for distribution systems and suffer from several drawbacks such as computational burden (Monte Carlo, Conventional convolution), sensitive accuracy with the complexity of system (point estimation method), requirement of necessary linearization (multi-linear simulation) and convergence problem (Gram–Charlier expansion, Cornish Fisher expansion). In this research, Latin Hypercube Sampling with Cholesky Decomposition (LHS-CD) is used to quantify the over-voltage issues with and without the voltage control algorithm in the distribution network with active generation. LHS technique is verified with a test network and real system from an Australian distribution network service provider. Accuracy and computational burden of simulated results are also compared with Monte Carlo simulations.
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ISSN:0306-2619
1872-9118
DOI:10.1016/j.apenergy.2015.11.003