Open AccessProbabilistic load flow with correlated wind power sources using a frequency and duration method
Author(s) -
Zhu JinZhou,
Zhang Yan
Publication year - 2019
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2018.6530
Subject(s) - probabilistic logic , markov chain , latin hypercube sampling , wind power , electric power system , transformation (genetics) , computer science , duration (music) , random variable , power (physics) , statistics , mathematics , engineering , monte carlo method , artificial intelligence , machine learning , art , physics , biochemistry , electrical engineering , chemistry , literature , quantum mechanics , gene
Probabilistic load flow (PLF) is an important tool in power system planning and operation. One limitation of conventional PLF is that only the probability information of random variables is obtained as a reference for related analyses. Frequency and duration information often plays an important role in power system assessment. In this study, a frequency and duration method for PLF with correlated wind power sources and loads is proposed based on Latin hypercube sampling (LHS), Nataf transformation, and Markov chains. In the proposed method, the spatial and temporal correlations among wind power sources and loads are addressed using Nataf transformation and Markov chains, which are incorporated into the LHS framework to solve the PLF problem with frequency and duration quantities. With the proposed method, not only the probability information but also the frequency and duration information of the random output variables in a PLF problem can be efficiently and accurately obtained. The performance of the proposed PLF method is verified using comparative tests on three modified test systems.