Distributed Renewable Energy Operating Impacts and Valuation Study

Solar Characterization
however, initial adopters vary by technology and customer segment. Further, the equilibrium
market share for a given technology will change over time and is a function of assumptions
regarding technology costs, electricity costs and usage, tax credits, and incentives. Thus, the final
shape of the diffusion curve will vary depending on the behavior of all these key variables over
time.
Figure 2-18: Example Technology Diffusion Curves
100%
90%
80%
Adoption Percentage
70%
60%
50%
40%
30%
20%
10%
0%
2008 2010 2012 2014 2016 2018 2020 2022 2024 2026
Time
_____
Note: This figure is for illustrative purposes only and does not represent the results of this Study
(see text for discussion).
The fraction of customers ultimately willing to adopt a technology was estimated based on the
calculated simple payback time of the technology. The formulation for the ultimate market
share 34 is calculated as:
( – sensitivity to payback * payback time)
fraction willing to adopt = e
where:
sensitivity to payback is estimated to be 0.3, and
payback time is calculated in years based on installed cost, annual savings, and electricity
cost.
For comparison, Figure 2-19 illustrates the formulation described above as it compares with the
baseline curve assumed in a recent Arizona Department of Commerce study. 35 The baseline
value used in that study is the average of two estimations provided by Navigant Consulting and
34 That is, the market share that would occur given sufficient time for the technology to diffuse per the parameters
affecting the S-shaped adoption.
35 See Arizona Department of Commerce’s “Arizona Solar Electric Roadmap Study,” January 2007.
[http://www.azcommerce.com/doclib/energy/az_solar_electric_roadmap_study_full_report.pdf]
Distributed Renewable Energy Operating Impacts & Valuation Study R. W. Beck, Inc. 2-39