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Non-sequential Monte Carlo simulation tool in order to minimize gaseous pollutants emissions in presence of fluctuating wind power

Author

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  • Vallée, François
  • Versèle, Christophe
  • Lobry, Jacques
  • Moiny, Francis

Abstract

In this paper, an original non-sequential Monte Carlo simulation tool is developed. This tool permits to compute the optimal dispatch of classical (coal, oil, etc.) thermal generation in order to minimize polluting gases (NOx, CO2, etc.) emissions in presence of wind power and under constraints. These constraints include, e.g., the maximal generation cost or the ability of the electrical system to cover the load, … In comparison with existing analytical tools that are based on restrictive hypotheses when it comes to wind power modelling (generally represented by a single entirely correlated global wind park), unexpected outages of conventional parks or fluctuating representation of the load, the use of Monte Carlo simulation allows to remove all those limitations. Indeed, thanks to the developed tool, the optimal dispatch of classical thermal generation can be reached under several load conditions. Well-known reliability indices can also be computed and, moreover, following the wind speed sampling that is used, entirely correlated, independent or more accurate correlation level between wind parks can be considered. Finally, it is thought that the proposed solution can be a useful tool for electrical system operators in order to dispatch the polluting thermal units under cost, reliability, emissions, fluctuating wind power and unexpected outages constraints.

Suggested Citation

  • Vallée, François & Versèle, Christophe & Lobry, Jacques & Moiny, Francis, 2013. "Non-sequential Monte Carlo simulation tool in order to minimize gaseous pollutants emissions in presence of fluctuating wind power," Renewable Energy, Elsevier, vol. 50(C), pages 317-324.
    • Handle: RePEc:eee:renene:v:50:y:2013:i:c:p:317-324
    DOI: 10.1016/j.renene.2012.06.046
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    Citations

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    Cited by:

    1. Bakhshideh Zad, Bashir & Toubeau, Jean-François & Bruninx, Kenneth & Vatandoust, Behzad & De Grève, Zacharie & Vallée, François, 2022. "Supervised learning-assisted modeling of flow-based domains in European resource adequacy assessments," Applied Energy, Elsevier, vol. 325(C).
    2. Khodakarami, Alireza & Farahani, Hassan Feshki & Aghaei, Jamshid, 2017. "Stochastic characterization of electricity energy markets including plug-in electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 112-122.
    3. Kharrazi, A. & Sreeram, V. & Mishra, Y., 2020. "Assessment techniques of the impact of grid-tied rooftop photovoltaic generation on the power quality of low voltage distribution network - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 120(C).
    4. Aien, Morteza & Hajebrahimi, Ali & Fotuhi-Firuzabad, Mahmud, 2016. "A comprehensive review on uncertainty modeling techniques in power system studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1077-1089.
    5. 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.
    6. Abdullah, M.A. & Agalgaonkar, A.P. & Muttaqi, K.M., 2014. "Climate change mitigation with integration of renewable energy resources in the electricity grid of New South Wales, Australia," Renewable Energy, Elsevier, vol. 66(C), pages 305-313.
    7. Ehsan, Ali & Yang, Qiang, 2019. "State-of-the-art techniques for modelling of uncertainties in active distribution network planning: A review," Applied Energy, Elsevier, vol. 239(C), pages 1509-1523.

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