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.

中文翻译：

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在存在风力发电机的情况下，将概率传输容量容限纳入考虑范围的前一天动态可用传输容量评估

评估提前一天的可用传输能力（ATC）是解除管制的电力系统中最重要的任务。确定ATC的主要目的是确保接口之间的安全电源交易。近年来，随着可再生能源对常规电网的高度渗透，研究其对ATC的影响至关重要。由于风速的随机性，发电组合中的风能输出会给ATC计算带来不确定性。因此，正确预测风速以获得准确的ATC提前很重要。人工神经网络（ANN）用于根据时间序列数据预测未来的风速。利用历史风速数据，开发了人工神经网络。还分析了不同的风力发电机，例如基于鼠笼式感应发电机运行的恒速风力发电机和基于双馈感应发电机运行的可变风力发电机，对动态ATC（DATC）的影响。动态电压稳定性，即Hopf分叉点，被视为DATC计算的极限。两种储备裕度，即传输可靠性裕度（TRM）和容量收益裕度（CBM）在准确的DATC估算中起着重要作用。根据概率方法计算TRM和CBM，并分析其对DATC的影响。应用蜻蜓算法（DFA）以获得用于DATC评估的准确动态电压稳定点。所提出的模型和算法在新英格兰39和南印度181公交系统上进行了测试和验证。