Probabilistic load flow computation using Copula and Latin hypercube sampling

• Published in: IET generation, transmission & distribution transmission & distribution, 2014-09, Vol.8 (9), p.1539-1549
• Main Authors: Cai, Defu, Shi, Dongyuan, Chen, Jinfu
• Format: Article
• Language: English
• Subjects: Applied sciences; Copula theory; correlated input random variables; Correlation; discrete data; Electric power plants; Electrical engineering. Electrical power engineering; Electrical power engineering; Exact sciences and technology; IEEE 118‐bus test systems; IEEE 14‐bus test systems; improved Latin hypercube sampling; Latin hypercube sampling; load flow; New Jersey; Non classical power plants; Operation. Load control. Reliability; PLF method; Power networks and lines; probabilistic load flow computation; Probabilistic methods; Probability distribution; Probability theory; Random variables; sampling methods; statistical distributions; stochastic dependence; stochastic processes; Stochasticity; wind farms; Wind power; Wind power plants
• ISSN: 1751-8687; 1751-8695; 1751-8695
• DOI: 10.1049/iet-gtd.2013.0649

A probabilistic load flow (PLF) method using Copula and improved Latin hypercube sampling is proposed. The stochastic dependence between input random variables is considered. Copula theory is adopted to establish the probability distribution of correlated input random variables. Based on discrete data, an improved Latin hypercube sampling is proposed. The accuracy of probability distribution of correlated input random variables established by Copula theory is evaluated by adopting the power output of wind farms located at New Jersey. The performance of the proposed PLF method is investigated using IEEE 14-bus and IEEE 118-bus test systems.