Quantifying and mitigating wind power variability

Understanding variability and unpredictability of wind power is essential for improving power system reliability and energy dispatch in transmission and distribution systems. The research presented herein intends to address a major challenge in managing and utilizing wind energy with mitigated fluctuation and intermittency. Caused by the varying wind speed, power variability can be explained as power imbalances. These imbalances create power surplus or deficiency in respect to the desired demand. To ameliorate the aforementioned issue, the fluctuating wind energy needs to be properly quantified, controlled, and re-distributed to the grid. The first major study in this dissertations is to develop accurate wind turbine models and model reductions to generate wind power time-series in a laboratory time-efficient manner. Reliable wind turbine models can also perform power control events and acquire dynamic responses more realistic to a real-world condition. Therefore, a Type 4 direct-drive wind turbine with power electronic converters has been modeled and designed with detailed aerodynamic and electric parameters based on a given generator. Later, using averaging and approximation techniques for power electronic circuits, the order of the original model is lowered to boost the computational efficiency for simulating long-term wind speed data. To quantify the wind power time-series, efforts are made to enhance adaptability and robustness of the original conditional range metric (CRM) algorithm that has been proposed by literatures for quantitatively assessing the power variability within a certain time frame. The improved CRM performs better under scarce and noisy time-series data with a reduced computational complexity. Rather than using a discrete probability model, the improved method implements a continuous gamma distribution with parameters estimated by the maximum likelihood estimators. With the leverage from the aforementioned work, a wind farm level behavior can be revealed by analyzing the data through long-term simulations using individual wind turbine models. Mitigating the power variability by reserved generation sources is attempted and the generation scenarios are generalized using an unsupervised machine learning algorithm regarding power correlations of those individual wind turbines. A systematic blueprint for reducing intra-hour power variations via coordinating a fast- and a slow- response energy storage systems (ESS) has been proposed. Methods for sizing, coordination control, ESS regulation, and power dispatch schemes are illustrated in detail. Applying the real-world data, these methods have been demonstrated desirable for reducing short-term wind power variability to an expected level.