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An accurate probability distribution model of wind speed is critical to the assessment of reliability contribution of wind energy to power systems. Most of current models are built using the parametric density estimation (PDE) methods, which usually assume that the wind speed are subordinate to a certain known distribution (e.g. Weibull distribution and Normal distribution) and estimate the parameters of models with the historical data. This paper presents a kernel density estimation (KDE) method which is a nonparametric way to estimate the probability density function (PDF) of wind speed. The method is a kind of data-driven approach without making any assumption on the form of the underlying wind speed distribution, and capable of uncovering the statistical information hidden in the historical data. The proposed method is compared with three parametric models using wind data from six sites.The results indicate that the KDE outperforms the PDE in terms of accuracy and flexibility in describing the longterm wind speed distributions for all sites. A sensitivity analysis with respect to kernel functions is presented and Gauss kernel function is proved to be the best one. Case studies on a standard IEEE reliability test system (IEEE-RTS) have verified the applicability and effectiveness of the proposed model in evaluating the reliability performance of wind farms.

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The integration of large-scale stochastic renewable energy, the aging of transmission facilities, and the growth of load demand all contribute to the increasing congestion levels of transmission systems. Such factors pose considerable stress on the economical and secure operation of power systems and the accommodation of large-scale renewable energies. However, under the smart grid circumstance, some cutting-edge transmission technologies can bring potential cost-effective solutions to leverage the potential capacity of existing transmission infrastructures. Such technologies can help the utilities to deal with the rapid change of operating conditions of the power system in a more flexible manner. For example, the network topology optimization (NTO) technology can change the transmission topology based on the operating conditions, which increases the flexibility of the transmission system. Dynamic thermal rating (DTR) can evaluate the maximum transmission capacity of transmission lines dynamically according to the weather condition parameters around the conductor. These two cost-effective technologies are promising in improving the congestion mitigation performance and can contribute to the efficient utilization of transmission network-so they will bring potential economic and reliability benefits. This paper incorporates NTO and DTR in the network-constrained unit commitment (NCUC) framework to study their synergistic effect on the power system day-ahead schedule. Case studies are performed on a modified RTS-79 system. The numerical results verify that the coordination of NTO and DTR may help decrease the generation cost and wind power curtailment. INDEX TERMS Day-ahead scheduling, network-constrained unit commitment, network topology optimization, dynamic thermal rating, wind power curtailment. NOMENCLATURE A. INDICES g No-load cost for generator g.