Quantity versus quality: Optimal harvesting wind power for the smart grid

The need to reduce greenhouse gases from our current power systems accelerates the integration of renewable energy sources (for example, wind and solar power). A fundamental difficulty is that renewable energy is usually of high variability. Numerous advancements in technologies and methods for the smart grid are required to mitigate and absorb this variability. In this paper, we focus on one of them: how to plan wind farms with high capacity and low variability locally and distributedly. First, we study the characteristics of both wind resource and wind turbines and propose a novel wind power estimation method based on Gaussian regression. The experimental result shows that our method achieves a more accurate estimation compared to other ones and has a nearly zero error for most of the turbine types. Then, we analyze a tradeoff between wind power's quantity and quality for large-scale wind farms, and find that there is an optimal turbine type for each location as to either the quantity or the quality. We propose an approach to optimally combine different types of wind turbines to balance the tradeoff. Finally, we explore geographical diversity among different locations and develop an extended approach that jointly optimizes the combination of locations and turbine types. Besides applying to plan new wind farms, we also discuss how to adapt the two approaches to decide an upgrade plan for a wind farm and a network of wind farms, respectively. We conduct extensive experiments using two different wind resource data traces for both local and distributed cases. The result shows that the proposed approaches significantly outperform those approaches using a single turbine type and those separately optimizing locations and turbine types. We also provide interesting insights about the quantity-quality balancing.