Practical Resource Assessment for Distributed Wind Energy Planning and Siting

Distributed energy resources provide energy solutions to communities, individuals and regions at or near the location of energy use. In the realm of wind energy, distributed wind (DW) involves turbines with a hub height of 10-60 m, appropriate for deployment in a broad range of application areas, including residences, farms, industrial sites and campuses. Wind resource data within the United States indicate a potential to profitably install more than 2,000 GW of DW capacity able to generate 3,244 TWh of energy in a typical year [1]. This amount is equivalent to more than 80% of the United States' energy production from all sources [2] and could be leveraged to power millions of homes and businesses and replace countless fossil fuel-based generating plants. Despite this enormous opportunity, adoption of such technology has been somewhat slowed by a lack of confidence in the underlying technology and resources. Low confidence in the technology is propelled by the limited availability of accessible energy production predictions and poor understanding of the accuracy of siting methods. While utility-scale wind projects can afford in-depth wind resource site assessment with high-fidelity models and on-site meteorological data collection, DW relies on less computationally expensive models and survey methods to make estimations of the available resources. The financial limits of DW siting operations result in a higher dependence on the results of such models and estimations. However, because of the presence of obstacles and vegetation closer to the hub height of DW turbines, modelling the resource introduces more challenges that must be overcome and may introduce uncertainty into the accuracy of estimates. To confront this important problem, this chapter provides a comprehensive discussion of approaches to modelling the wind resource for practical DW installations. As depicted in Figure 2.1 the siting process involves (1) resource data from atmospheric models and bias correction for those models, (2) terrain-based downscaling and obstacle impact assessment and (3) calculation of suitable measures of energy generation to estimate real-world productivity, including uncertainty and loss. The following sections discuss each of these steps in turn, providing context for the inherent challenges and a summary of recent research that informs industry best practices.