Probabilistic Study of Grid-connected Wind Electric Conversion Systems

Abstract

Purpose of the study is to model the power output of Wind Electric Conversion System (WECS) as a random variable given that wind speeds incident on them is random. The model is extended to model probability functions for combined power outputs of multiple WECS located in a wind regime. The impact of variable region in the power characteristic on the probability functions for power output of individual and multiple WECS is investigated. This model is employed in performance assessment of wind farms within probabilistic framework to obtain its load supplying capability. Smart grid functionalities and Demand Side Management (DSM) are identified to have complementary behavior beneficial for optimal operation of electric grid. This is demonstrated using the obtained model for wind farms and a possible modification of load demand distribution function. The power output of WECS is a mixed random variable. Impact of exponent `n' on the probability density function (pdf) for power output of multiple WECS is "minor" for a low number of WECS. For a large number of WECS, there occurs a major redistribution of probabilities of power outputs leading to distinct pdf plots for different exponents. Increasing wind penetration leads to flatter power duration curves. Smart grid functionalities and DSM techniques if complemented in a suitable manner will assist in greater assimilation of wind energy into the grid.