improved experience curve indicates large future cost reductions for wind power ryan williams, eric...
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Improved Experience Curve Indicates Large Future Cost Reductions for Wind Power
Ryan Williams,
Eric Hittinger, and Eric Williams
Rochester Institute of Technology
USAEE, October 26, 2015
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Forecasting cost of evolving energy technologies
• Expectations of cost reductions important for policy, e.g. temporary vs. ongoing subsidy
• Cost forecasts embedded in many energy system models, e.g. National Energy Modeling System (NEMS).
• What is “best practice” cost forecast?• Approaches: Retrospective (e.g. experience
curve), Expert elicitation, Bottom-up engineering/cost model
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Experience Curve First ref.: Wright (1936)
C(P) = C0 (P/P0)-α
C = cost of production per energy unit
P= cumulative production
Learning rate (LR) : fraction cost down every doubling of production
LR= 1-2-α
For PV modules, LR=20%
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Commentary on experience curve
Pros:•Simple – one free parameter: learning rate•Surprisingly robust empirically given simplicity, e.g. Nagy et al (2013) showed that R-squared exceeds 90% for a majority of 62 investigated technologies.
Cons:•Phenomenological – Why does it work?•How causal? If yes, “buy-down” is the key to cost reductions. Great if true, but…
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Expectations for cost reductions in wind power
Generally “pessimistic” outlook in policy relevant models:•U.S. Annual Energy Outlook assumes wind costs fall at a rate of 1% annually, same as coal generation•DOE 2015 Wind Vision report uses equivalent learning rate = 5.7%
What do experience curves say about wind?
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Prior experience curve results for wind power
• 120 results from different studies, various countries and time intervals (Rubin et al 2015).
• As yet, no explanation of variability.
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Hypotheses
1. Experience curve is wrong model to describe historical costs of wind power. Need new model.
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Hypotheses
1. Experience curve is wrong model to describe historical costs of wind power. Need new model.
2. If variability could be understood, model could be modified to address.
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Goal: explain variability and develop a better model
The plan:
1.Explore different versions of experience curve for wind power costs in the U.S.
2.Try to understand sources of variability in learning rates.
3.Find model that reduces variability
4.Note: U.S. wind growth roughly tracks global, so OK on international aspects of experience curve.
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The factors to account for
1. Energy Cost ($/kWh) vs. Power Capacity Cost ($/kW) : Generation = Power Capacity x Capacity Factor
2. Wind Quality Adjusted Energy : a) Annual wind variability
b) Lower site quality over time.
3. Exogenous fluctuations in capital cost: capital costs some years 2002-2010, partly due to weak US$ and high material costs
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Regression results for wind: 1989-2013
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Understanding variability
• Better R-squared does not fix the problem of variability in results
• If variability can be understood, model form that reduces it is preferred.
• Hypothesis: different starting and ending years of data-set is major driver of variability
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Variability by dataset start year
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Variability by dataset end year
Model modifications much less sensitive to start and end year.
Full model:
LR = 10-21%, baseline 16%
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Forecasting wind costs
Full model forecasts wind cheaper than coal or gas.
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Conclusions
• Retrospective forecasting suggests very positive outlook for wind (~3 cents/kWh in 2030)
• Best practice ~ critical view of engineering, expert and experience curve perspectives.
• Implications of cheap wind important enough to justify effort.
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Thank you for your attention!
Letchworth State Park, near Rochester, New York
This research was supported by the National Science Foundation