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A comprehensive review on uncertainty modeling techniques in power system studies

Aien, M ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1016/j.rser.2015.12.070
  3. Publisher: Elsevier Ltd
  4. Abstract:
  5. 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
  6. Keywords:
  7. Joint possibilistic-probabilistic uncertainty modeling ; Possibilistic uncertainty modeling ; Probabilistic uncertainty modeling ; Uncertain power system studies ; Decision making ; Density functional theory ; Energy utilization ; Probability density function ; Renewable energy resources ; Engineering systems ; Possibilistic ; Possibilistic uncertainties ; Power system studies ; Probabilistic uncertainty model ; Uncertain variables ; Uncertainty modeling ; Uncertainty analysis
  8. Source: Renewable and Sustainable Energy Reviews ; Volume 57 , 2016 , Pages 1077-1089 ; 13640321 (ISSN)
  9. URL: http://www.sciencedirect.com/science/article/pii/S1364032115014537