Over the past 30 years, the cost of wind energy has significantly decreased, due to both capital cost reductions
and performance improvements. However, from roughly 2004 to 2009, continued performance increases were not
enough to offset the sizable increase in capital costs of this time period, resulting in an overall increase in the cost of
wind energy. Nevertheless, as capital costs have moderated from their 2009–2010 levels, the cost of wind energy has
fallen and is now at an all-time low within fixed wind resource classes. Looking forward, a variety of factors suggest that the LCOE of wind energy will continue to fall on a long-term global basis and within fixed wind resource classes. Most recent estimates project that the LCOE of onshore wind could fall by 20%–30% over the next two decades.
However, other factors may put upward pressure on wind energy costs, such as continued movement towards lower
wind speed sites and local factors such as transmission needs. With these factors in mind, it is of utmost
importance to consider the interdependence of capital costs and performance and to evaluate the future cost of wind
energy on an LCOE basis. Such evaluations must consider trends in the quality of the wind resource in which projects
are located, as well as development, transmission, integration, and other cost elements that may also change
(and increase) with time and deployment levels. Further improving our understanding of possible future
cost trends will require additional data gathering and improved modeling capability. Robust data collection is
needed across the array of variables that must be factored into estimating LCOE and in each of the wind energy
markets around the globe. Also needed are data on the many contextual factors that impact the overall cost of
wind energy and that may also vary with time, such as interconnection costs, permitting costs, and the average
wind speed of installed projects. Such data would allow historical LCOE trends to be more closely analyzed, with
insights gleaned both through advanced learning curve analysis as well as bottom-up assessments of historical cost
drivers. More advanced component, turbine, and project level design and cost tools would allow for more
sophisticated cost modeling and provide greater insights into possible future costs based on changes in material use
and design architectures. Together these efforts would enhance our ability to understand future costs, prioritize
R&D efforts, and understand the role and impact of deployment incentives in the future.