Adequacy assessment of generating systems containing wind power considering wind speed correlation

TLDR: In this paper, genetic algorithm methods are applied to adjust autoregressive moving-average time series models in order to simulate correlated hourly wind speeds with specified wind speed cross-correlation coefficients of two wind sites.

Abstract: Wind power is an important renewable energy resource. Electrical power generation from wind energy behaves quite differently from that of conventional sources, and maintaining a reliable power supply is an important issue in power systems containing wind energy. In these systems, the wind speeds at different wind sites are correlated to some degree if the distances between the sites are not very large. Genetic algorithm methods are applied here to adjust autoregressive moving-average time series models in order to simulate correlated hourly wind speeds with specified wind speed cross-correlation coefficients of two wind sites. Multi-state wind energy conversion system models are used to incorporate the correlated wind farms in reliability studies of generating systems. A method to generate random numbers with specified correlation coefficients for application in a state-sampling Monte Carlo simulation technique is introduced. It is shown that the proposed method can be used in the adequacy assessment of a generating system incorporating partially dependent wind farms.