Day ahead dynamic available transfer capability evaluation incorporating probabilistic transmission capacity margins in presence of wind generators

摘要

Assessment of day ahead Available Transfer Capability (ATC) is a most vital task in deregulated power system. The main purpose of the ATC determination is to ensure the secure power transaction between the interfaces. In recent times, with the high penetration of renewable energy sources into conventional grid, it is essential to study its impact on ATC. Due to stochastic nature of the wind speed, the wind power output in the power generation mix can bring uncertainties in the ATC calculation. So, it is important to predict the wind speed correctly to obtain accurate day ahead ATC. Artificial Neural Network (ANN) is used to predict the future wind speed based on time series data. Using historical wind speed data, the ANN is developed. The impact of different wind generators such as constant speed wind generator operated based on Squirrel cage induction generator and variable wind generator operated based on Doubly fed induction generator on Dynamic ATC (DATC) is also analyzed. The dynamic voltage stability namely Hopf bifurcation point is considered as a limit for DATC calculation. The two-reserve margins namely Transmission Reliability Margin (TRM) and Capacity Benefit Margin (CBM) play a major role in accurate DATC estimation. The TRM and CBM are calculated based on probabilistic approach and its impact on DATC also analyzed. A Dragon fly algorithm (DFA) is applied to obtain the accurate dynamic voltage stability point for DATC evaluation. The proposed model and algorithm are tested and validated on New England 39 and South Indian 181 bus system.