Multiple WFs modeling for reliability estimation of generating system

Abstract

Multiple wind farms (WFs) model consists of wind speed model and single WF model. In the multiple WFs integrated power system, the wind speeds at different WFs are correlated to some degree if the distance between WFs is not very large. Hence, a novel MC-Nataf-CD method combining partitions Monte Carlo (MC) sampling, Nataf transformation and Cholesky Decomposition (CD) is proposed to simulate correlated wind speeds with desired cross-correlation coefficients. The wind speeds generated by the proposed wind speed model is closer to both the historical data and the Weibull fitting curve. For the single WF model, wake effects, the forced outage rates (FORs) of wind turbine generators (WTGs) and the reliability of the connected grids inside a WF need to be taken into consideration. Four different single WF models are proposed to reveal the impact of these relevant factors. A statistical method based on state-sampling MC simulation is implemented for the reliability estimation of Roy Billinton Test System (RBTS) with multiple WFs, using both historical and simulated wind speed data. The simulation results and the calculated reliability indices demonstrate that the proposed techniques are effective and accurate in the modeling of correlated multiple WFs.