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Probabilistic load flow for distribution systems with uncertain PV generation

Author

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  • Kabir, M.N.
  • Mishra, Y.
  • Bansal, R.C.

Abstract

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.

Suggested Citation

  • Kabir, M.N. & Mishra, Y. & Bansal, R.C., 2016. "Probabilistic load flow for distribution systems with uncertain PV generation," Applied Energy, Elsevier, vol. 163(C), pages 343-351.
    • Handle: RePEc:eee:appene:v:163:y:2016:i:c:p:343-351
    DOI: 10.1016/j.apenergy.2015.11.003
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    References listed on IDEAS

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    1. Munkhammar, Joakim & Widén, Joakim & Rydén, Jesper, 2015. "On a probability distribution model combining household power consumption, electric vehicle home-charging and photovoltaic power production," Applied Energy, Elsevier, vol. 142(C), pages 135-143.
    2. Tina, Giuseppe Marco & Gagliano, Salvina & Graditi, Giorgio & Merola, Angelo, 2012. "Experimental validation of a probabilistic model for estimating the double axis PV tracking energy production," Applied Energy, Elsevier, vol. 97(C), pages 990-998.
    3. Alessandrini, S. & Delle Monache, L. & Sperati, S. & Cervone, G., 2015. "An analog ensemble for short-term probabilistic solar power forecast," Applied Energy, Elsevier, vol. 157(C), pages 95-110.
    4. Kabir, M.N. & Mishra, Y. & Ledwich, G. & Xu, Z. & Bansal, R.C., 2014. "Improving voltage profile of residential distribution systems using rooftop PVs and Battery Energy Storage systems," Applied Energy, Elsevier, vol. 134(C), pages 290-300.
    5. Savić, Aleksandar & Đurišić, Željko, 2014. "Optimal sizing and location of SVC devices for improvement of voltage profile in distribution network with dispersed photovoltaic and wind power plants," Applied Energy, Elsevier, vol. 134(C), pages 114-124.
    6. Carpinelli, Guido & Caramia, Pierluigi & Varilone, Pietro, 2015. "Multi-linear Monte Carlo simulation method for probabilistic load flow of distribution systems with wind and photovoltaic generation systems," Renewable Energy, Elsevier, vol. 76(C), pages 283-295.
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