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A comprehensive review on uncertainty modeling techniques in power system studies

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  • Aien, Morteza
  • Hajebrahimi, Ali
  • Fotuhi-Firuzabad, Mahmud

Abstract

As a direct consequence of power systems restructuring on one hand and unprecedented renewable energy utilization on the other, the uncertainties of power systems are getting more and more attention. This fact intensifies the difficulty of decision making in the power system context; therefore, the uncertainty analysis of the system performance seems necessary. Generally, uncertainties in any engineering system study can be represented probabilistically or possibilistically. When sufficient historical data of the system variables is not available, a probability density function (PDF) might not be defined, they must be represented in another manner i.e. using possibilistic theory. When some of the system uncertain variables are probabilistic and some are possibilistic, neither the conventional pure probabilistic nor pure possibilistic methods can be implemented. Hence, a combined solution is needed. This paper gives a complete review on uncertainty modeling approaches for power system studies making sense about the strengths and weakness of these methods. This work may be used in order to select the most appropriate method for each application.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:rensus:v:57:y:2016:i:c:p:1077-1089
    DOI: 10.1016/j.rser.2015.12.070
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    1. 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.
    2. Ferrario, E. & Zio, E., 2014. "Assessing nuclear power plant safety and recovery from earthquakes using a system-of-systems approach," Reliability Engineering and System Safety, Elsevier, vol. 125(C), pages 103-116.
    3. Soroudi, Alireza & Ehsan, Mehdi, 2011. "A possibilistic-probabilistic tool for evaluating the impact of stochastic renewable and controllable power generation on energy losses in distribution networks--A case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(1), pages 794-800, January.
    4. George B. Dantzig, 1955. "Linear Programming under Uncertainty," Management Science, INFORMS, vol. 1(3-4), pages 197-206, 04-07.
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