Probabilistic assessment of available transfer capability considering spatial correlation in wind power integrated system

• Published in: IET generation, transmission & distribution Vol. 7; no. 12; pp. 1527 - 1535
• Main Authors: Gang, Luo, Jinfu, Chen, Defu, Cai, Dongyuan, Shi, Xianzhong, Duan
• Format: Journal Article
• Language: English
• Published: Stevenage The Institution of Engineering and Technology 01.12.2013; Institution of Engineering and Technology; The Institution of Engineering & Technology
• Subjects: Algorithms; Applied sciences; ATC evaluation; ATC volatility; available transfer capability probabilistic assessment; Cholesky decomposition; Clustering; Computational efficiency; Computer simulation; Correlation; Direct energy conversion and energy accumulation; Electric power plants; Electrical engineering. Electrical power engineering; Electrical power engineering; Energy; Exact sciences and technology; IEEE reliability test system; IEEE standards; input random variables; Latin hypercube sampling; LHS; Mathematical analysis; Miscellaneous; Monte Carlo methods; Monte Carlo simulation; Natural energy; Non classical power plants; optimal power flow; pattern clustering; power generation reliability; Power networks and lines; probability; Sampling; sampling efficiency; sampling methods; scenario clustering techniques; spatial correlation; spatially correlated wind power enhancement; vector quantification clustering algorithm; Wind energy; wind farms; Wind power; wind power integrated system; Wind power plants; power generation reliability; sampling methods; vector quantification clustering algorithm; ATC evaluation; input random variables; ATC volatility; probability; available transfer capability probabilistic assessment; sampling efficiency; optimal power flow; spatially correlated wind power enhancement; spatial correlation; wind farms; Cholesky decomposition; IEEE reliability test system; Monte Carlo methods; wind power integrated system; Latin hypercube sampling; pattern clustering; LHS; scenario clustering techniques; IEEE standards; wind power plants; Monte Carlo simulation; Monte Carlo method; Automatic classification; Windfarm; Case study; Probabilistic assessment; Electrical network analysis; Sensitivity; Wind energy; Optimal power flow; Electrical network; Cholesky method; High efficiency; Hypercube; Available transfer capability; Numerical simulation; Integrated system; Fast algorithm; Sampling; Reliability; Random variable
• ISSN: 1751-8687; 1751-8695; 1751-8695
• DOI: 10.1049/iet-gtd.2013.0081

With the increasing integration of wind farms, modification of current tools for evaluating and managing power systems such as available transfer capability (ATC) becomes an important issue. This study presents a computationally accurate and efficient method in evaluating ATC with large amount of uncertainty based on Latin hypercube sampling (LHS) and scenario clustering techniques. LHS is used in Monte Carlo simulation to select a system state with high sampling efficiency and good precision. Cholesky decomposition is combined into the sampling process to deal with the dependencies among input random variables. The sampled scenarios are clustered by vector quantification clustering algorithm, which contributes to the fast calculation of ATC evaluation for numerous scenarios. Finally, a sensitivity method based on optimal power flow is proposed for the clustered scenarios. The case studies, with the IEEE reliability test system, illustrate the advantages of the proposed method that largely reduces the computation burden under the premise of ensuring its accuracy. The results also verify the obvious enhancement of spatially correlated wind power on the volatility of ATC.