reviewed both internally and externally prior to publication for purposes of external review and the study benefited from the advice and comments from a dozen individuals representing wind industry consulting firms, state agencies, wind turbine manufacturers, and other federal laboratories.

Between 2003 and the end of 2015, over 75,000 wind turbines, totaling 934 MW in cumulative capacity, were deployed in distributed applications across all 50 states, the District of Columbia, Puerto Rico, and the U.S. Virgin Islands. In 2015, 28 states added 28 MW of new distributed wind capacity, representing 1,713 turbine units and $102 million in investment. While the number of units installed increased slightly, capacity additions and investments decreased compared to 2014, when 63.6 MW of new distributed wind capacity from nearly 1,700 turbines was added, representing $170 million in investment across 24 states. In 2015, 4.3 MW of small wind (turbines up through 100 kW) was deployed in the United States, representing 1,695 units and over $21 million in investment. This is slightly higher than in 2014 (3.7 MW of small wind, approximately 1,600 units, and $20 million in investment), but down from 2013 (5.6 MW, approximately 2,700 units, and $36 million investment). U.S. small wind manufacturers accounted for nearly 100% of 2015 domestic small wind sales. A total of 23.7 MW of capacity was installed in 2015 using turbines greater than 100 kW in distributed applications. Three of the five manufacturers and suppliers of these turbines, representing 9.4 MW and ten turbine units, were not based in the United States. A total of 14.3 MW and eight turbine units were from the two U.S.-based manufacturers or suppliers. Ohio, Nebraska, and Connecticut led the United States in new distributed wind power capacity additions in 2015 as a result of larger project installations in those states. California, New York, and Minnesota led the nation for small wind capacity deployment in 2015.

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The Capacity Expansion Regional Feasibility (CERF) model is an open-source geospatial model, written in Python and C++, that is designed to determine the on-the-ground feasibility of achieving a projected energy technology expansion plan. Integrated human-Earth systems models and grid expansion models typically do not have sufficient spatial, temporal, or process-level resolution to account for technologyspecific siting considerations-for example, the value or costs of connecting a new power plant to the electric grid at a particular location or whether there is sufficient cooling water to support the installation of thermal power plants in a certain region. CERF was developed to specifically examine where power plant locations can be feasibly sited when considering high spatial resolution siting suitability data as well as the net locational costs (i.e., considering both net operating value and interconnection costs), at a spatial resolution of 1 km 2 . The outputs from CERF can provide insight into factors that influence energy system resilience under a variety of future scenarios can be used to refine model-based projections and be useful for capacity expansion planning exercises. CERF is open-source and publicly available via GitHub. (1) Overview Model DescriptionThe Capacity Expansion Regional Feasibility (CERF) model is written in Python and C++ and was built to determine the on-the-ground feasibility of achieving energy system expansion plans generated by integrated human-Earth systems models or regional capacity expansion models. Energy expansion projections from such models are typically limited to fairly large regions (e.g., balancing authorities or control regions) and do not account for on-the-ground barriers such as protected lands, potentially hazardous areas, highly populated areas, water availability, proximity to Class 1 airsheds, and other siting constraints that may impede the ability to achieve a planned expansion. CERF was developed to evaluate the feasibility of siting a mix of energy technologies over the contiguous United States of America (CONUS) at a resolution of 1 km 2 while considering current and future socioeconomic conditions, energy demands, land use, environmental regulations, and water availability. The information gleaned from determining the feasibility of technology expansion plans can be used to provide insight into factors that may create or exacerbate vulnerabilities related to energy system resilience.CERF is unique in that it determines feasible siting locations using a combination of on-the-ground suitability constraints (e.g., protected lands) with simulated economic competition between energy technologies using an algorithm that minimizes net locational cost (NLC) to choose specific siting locations within suitable areas.1 The NLCs are calculated for each technology and are influenced by the distance to existing transmission infrastructure, technology-specific marginal operating costs, and technology-and location-specific marginal energy values. In effect, the algori...