Wind power costs expected to decrease due to technological progress. (July 2017)

• Record Type: Journal Article
• Authors: Williams, Eric; Hittinger, Eric; Carvalho, Rexon; Williams, Ryan
• Is Part Of: Energy policy. Volume 106(2017)
• Page Start: 427
• Page End: 435
• Publication Date: 2017-07
• Subjects: Wind energy -- Technological progress -- Cost trends -- Forecasting -- Energy policy; Energy policy -- Periodicals; Politique énergétique -- Périodiques; Electronic journals; 333.79
• DOI: 10.1016/j.enpol.2017.03.032
• Languages: English
• ISSNs: 0301-4215

Abstract

Abstract: The potential for future cost reductions in wind power affects adoption and support policies. Prior analyses of cost reductions give inconsistent results. The learning rate, or fractional cost reduction per doubling of production, ranges from −3% to +33% depending on the study. This lack of consensus has, we believe, contributed to high variability in forecasts of future costs of wind power. We find that learning rate can be very sensitive to the starting and ending years of datasets and the geographical scope of the study. Based on a single factor experience curve that accounts for capacity factor gains, wind quality decline, and exogenous shifts in capital costs, we develop an improved model with reduced temporal variability. Using a global adoption model, the wind-learning rate is between 7.7% and 11%, with a preferred estimate of 9.8%. Using global scenarios for future wind deployment, this learning rate range implies that the cost of wind power will decline from 5.5 cents/kWh in 2015 to 4.1–4.5 cents/kWh in 2030, lower than a number of other forecasts. If attained, wind power may be the cheapest form of new electricity generation by 2030, suggesting that support and investment in wind should be maintained or expanded. Highlights: Expectations for cost reductions in wind power is important for policy. Wind learning rates are sensitive to data time period and regional choice. We develop improved wind cost model with much reduced variability. New model gives global wind learning rates between 7.7%-11%.