Iranian Journal of Electrical and Electronic Engineering

Utilization of wind turbines as economic and green production units, poses new challenges to the power system planners, mainly due to the stochastic nature of the wind, adding a new source of uncertainty to the power system. Different types of distribution and correlation between this random variable and the system load makes conventional method inappropriate for modeling such a correlation. In this paper, the correlation between the wind speed and system load is modeled using Copula, a mathematical tool recently used in the field of the applied science. As the effect of the correlation coefficient is the main concern, the copula modeling technique allows simulating various scenarios with different correlations. The conducted simulations in this paper reveals that the wind speed correlation with the load has significant effect on the system reliability indices, such as expected energy not served (EENS) and loss of load probability (LOLP). Moreover, in this paper the effect of the correlation coefficient on the effective load carrying capability (ELCC) of the wind turbines is analyzed, too. To perform the aforementioned simulations and analyses, the modified RBTS with an additional wind farm is used.

Wind energy penetration in power system has been increased very fast and large amount of capitals invested for wind farms all around the world. Meanwhile, in power systems with wind turbine generators (WTGs), the value of Available transfer capability (ATC) is influenced by the probabilistic nature of the wind power. The Mont Carlo Simulation (MCS) is the most common method to model the uncertainty of WTG. However, the MCS method suffers from low convergence rate. To overcome this shortcoming, the proposed technique in this paper uses a new formulation for solving ATC problem analytically. This lowers the computational burden of the ATC computation and hence results in increased convergence rate of the MCS. Using this fast technique to evaluate the ATC, wind generation and load correlation is required to get into modeling. A numerical method is presented to consider load and wind correlation. The proposed method is tested on the modified IEEE 118 bus to analyze the impacts of the WTGs on the ATC. The obtained results show that wind generation capacity and its correlation with system load has significant impacts on the network transfer capability. In other words, ATC probability distribution is sensitive to the wind generation capacity.