Optimizing Wind Power Generation while Minimizing Wildlife Impacts in an Urban Area

Abstract

The location of a wind turbine is critical to its power output, which is strongly affected by the local wind field. Turbine operators typically seek locations with the best wind at the lowest level above ground since turbine height affects installation costs. In many urban applications, such as small-scale turbines owned by local communities or organizations, turbine placement is challenging because of limited available space and because the turbine often must be added without removing existing infrastructure, including buildings and trees. The need to minimize turbine hazard to wildlife compounds the challenge. We used an exclusion zone approach for turbine-placement optimization that incorporates spatially detailed maps of wind distribution and wildlife densities with power output predictions for the Ohio State University campus. We processed public GIS records and airborne lidar point-cloud data to develop a 3D map of all campus buildings and trees. High resolution large-eddy simulations and long-term wind climatology were combined to provide land-surface-affected 3D wind fields and the corresponding wind-power generation potential. This power prediction map was then combined with bird survey data. Our assessment predicts that exclusion of areas where bird numbers are highest will have modest effects on the availability of locations for power generation. The exclusion zone approach allows the incorporation of wildlife hazard in wind turbine siting and power output considerations in complex urban environments even when the quantitative interaction between wildlife behavior and turbine activity is unknown.

Environmental drivers of methane fluxes from an urban temperate wetland park

“…Nonetheless, we assume that these effects are small at twice the height of the canopy. The detailed spatial distribution of the different microsites, which included open water in the experimental wetlands, macrophyte vegetation, upland forest, grass, other open water (river or vernal pool), trees, paved areas, and buildings was generated using quantum geographic information system (GIS) based on data from aerial imagery (conducted by the Ohio Department of Natural Resources), remote sensing images (Google Earth), an airborne lidar map (generated by the Ohio Statewide Imagery Program), and publicly available GIS data [ Bohrer et al ., ] combined with seasonal ground‐based GPS surveys. A 2‐D footprint‐likelihood matrix within each half hour was spatially integrated across all points within each microsite type to determine the probable percentages of this half‐hourly reading that originated from each microsite type.…”

Evaluating the Consequences of Physical Barriers on Fish During Long-distance Upstream Migrations Through Rivers

“…The community must also recognize that river biota include far more than just fish (Pringle 2003). Finally, we encourage other researchers to implement similar environmental 'bright spot' thinking related to other energy sectors (e.g., minimizing wind turbine impacts on birds, Bohrer et al 2013; minimizing the loss of productive habitat from solar, Stoms et al 2013) and support greater integration of clean energy sources into the energy portfolio of regions and countries.…”