Generation Reliability Assessment in Power Market Using Fuzzy Logic and Monte Carlo Simulation

Haroonabadi, Hossein; Haghifam, Mahmoud‐Reza

doi:10.1109/isap.2007.4441637

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…At present, most research work are under the assumption that probability information is complete, and belong to the quasi-precise risk assessment. The other research work consider the uncertainty caused by incompleteness of probability information: Equipment reliability parameters' effect on power system reliability is discussed in [3][4][5]; Fuzzy theory is used to evaluate the reliability of power system in [6][7][8]; Interval number is used to represent reliability parameters' uncertainty and interval analysis theory is used to study the reliability of power distribution network in [9]; Credibility theory is proposed to deal with randomness and fuzziness, and evaluate the risk of power system in [10].…”

Section: Introductionmentioning

confidence: 99%

Imprecise Risk Assessment of Power System

Peng

Huang

2014

AMM

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The incomplete probabilistic information can lead to imprecision. The traditional power system risk assessment can only deal with random information, but it cannot deal with the imprecise information. Interval Probability (IP) can reflect both randomness and imprecision. The risk assessment of power system based on IP is an effective method to deal with imprecision and can provide more useful information to the decision-makers. In the paper, the completeness degree of probabilistic information was depicted by IP. The nonlinear optimization models for imprecise risk assessment of generating system and composite generation-transmission system were established. And the genetic algorithm was used to obtain the upper and lower bound of reliability indices. Case study on revised IEEE-RTS79 and IEEE-RBTS system showed the rationality and equity of presented method.

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