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An over-limit risk assessment of PV integrated power system using probabilistic load flow based on multi-time instant uncertainty modeling

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  • Prusty, B. Rajanarayan
  • Jena, Debashisha

Abstract

In this paper, the risk assessment of a PV integrated power system is accomplished by computing the over-limit probabilities and the severities of events such as under-voltage, over-voltage, over-load, and thermal over-load. These aspects are computed by performing temperature-augmented probabilistic load flow (TPLF) using Monte Carlo simulation. For TPLF, the historical data for PV generation, ambient temperature, and load power, each collected at twelve specific time instants of a day for the past five years are pre-processed by using three linear regression models for accurate uncertainty modeling. For PV generation data, the developed model is capable of filtering out the annual predictable periodic variation (owing to positioning of the Sun) and decreasing production trend due to ageing effect whereas, for ambient temperature and load power, the corresponding models accurately remove the annual cyclic variations in the data and their growth. The simulations pertaining to the aforesaid risk assessment are performed on a PV integrated New England 39-bus test system. The system over-limit risk indices are calculated for different PV penetrations and input correlations. In addition, the changes in the values of TPLF model parameters on the statistics of the result variables are analyzed. The risk indices so obtained help in executing necessary steps to reduce system risks for reliable operation.

Suggested Citation

  • Prusty, B. Rajanarayan & Jena, Debashisha, 2018. "An over-limit risk assessment of PV integrated power system using probabilistic load flow based on multi-time instant uncertainty modeling," Renewable Energy, Elsevier, vol. 116(PA), pages 367-383.
    • Handle: RePEc:eee:renene:v:116:y:2018:i:pa:p:367-383
    DOI: 10.1016/j.renene.2017.09.077
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    References listed on IDEAS

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    1. Prusty, B Rajanarayan & Jena, Debashisha, 2017. "A critical review on probabilistic load flow studies in uncertainty constrained power systems with photovoltaic generation and a new approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 1286-1302.
    2. Gupta, Neeraj, 2016. "Probabilistic load flow with detailed wind generator models considering correlated wind generation and correlated loads," Renewable Energy, Elsevier, vol. 94(C), pages 96-105.
    3. Abdullah, M.A. & Agalgaonkar, A.P. & Muttaqi, K.M., 2013. "Probabilistic load flow incorporating correlation between time-varying electricity demand and renewable power generation," Renewable Energy, Elsevier, vol. 55(C), pages 532-543.
    4. 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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    Cited by:

    1. Gao, Jianwei & Guo, Fengjia & Li, Xiangzhen & Huang, Xin & Men, Huijuan, 2021. "Risk assessment of offshore photovoltaic projects under probabilistic linguistic environment," Renewable Energy, Elsevier, vol. 163(C), pages 172-187.

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