Composite Reliability Evaluation With Renewable Sources Based on Quasi-Sequential Monte Carlo and Cross Entropy Methods

被引：0

作者：

da Silva, Armando M. Leite ^{[1
]}

Gonzalez-Fernandez, Reinaldo A. ^{[2
]}

Flavio, Silvan A. ^{[1
]}

Manso, Luiz A. F. ^{[3
]}

机构：

[1] Fed Univ Itajuba UNIFEI, Inst Elect Sytems & Energy, Itajuba, MG, Brazil

[2] ITAIPU Binac Hernandarias, Superintendency Operat, Hernandarias, Paraguay

[3] Fed Univ Sao Joao Rei UFSJ, Dept Elect Engn, Sao Joao Del Rei, MG, Brazil

来源：

2014 INTERNATIONAL CONFERENCE ON PROBABILISTIC METHODS APPLIED TO POWER SYSTEMS (PMAPS) | 2014年

关键词：

Composite reliability; cross-entropy method; Monte Carlo simulation; renewable sources; wind power generation; SIMULATION; GENERATION; SYSTEM;

D O I：

暂无

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

This paper presents a new approach to assess reliability indices in composite generation and transmission systems, considering high penetration of renewable energy. The main idea is to combine a Cross-Entropy (CE)-based optimization approach and quasi-sequential Monte Carlo simulation (MCS) to obtain an auxiliary sampling distribution, which can minimize the variance of the reliability index estimators. This auxiliary sampling distribution will properly modify the original unavailabilities of both generation and transmission equipment, so that important failure events are sampled more often. As a result, the MCS algorithm can converge faster and with fewer samples, leading to significant speed-ups, especially when dealing with very reliable system network configurations. The results obtained for the IEEE Reliability Test System - 1996 will be presented and discussed.

引用

页数：6

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