An analysis of defussion and competition trend of PV and ST technologies and impact of incentives on their development in US

Document Type : Research Paper


1 Associate Prof. School of Industrial and Systems Engineering

2 British Colombia


The growth of two important technologies in solar energy throughout the world, including North America, i.e. the photovoltaic (PV) and the solar thermal (ST), has been dependent on government supports and financial incentives for investment in these technologies. In this paper, huge sets of autoregressive (AR) and vector AR models are developed as candidates for analysis of growth trends of both PV and ST technologies in the US. Then the best models are selected via a systematic model selection method. For the lack of exact data, the effects of incentives on these technologies are estimated by the Monte-Carlo simulation technique. Both real data and simulation results reveal the probability of a hype (herd) behavior in the diffusion of the technologies, both in past and in the present. Moreover, ST technology has already lost its growth speed because of drops in the substituting energy, i.e. natural gas prices. It is shown that the financial incentives had no more influence on ST growth after 2008. Although PV diffusion is accelerating fast, the simulation shows it also may partly lose its current tremendous growth rate, if the incentives are removed. This can be a valuable lesson for the development of renewable energies in


-    دفتر آگاه‏سازی سازمان انرژی‏های نو ایران (سانا). از انرژی‏های نو چه می‏دانید؟ انرژی خورشیدی 2.
-    شمس، محمدحسین؛ خاوری، فرشاد؛ محمدی، مسعود؛ نوری، جلال. (1392)، مروری بر فناوری‏های تولید برق از انرژی خورشیدی و مقایسه آماری بزرگ‏ترین نیروگاه‏های خورشیدی جهان، دوفصلنامهتوسعهتکنولوژیصنعتی، شماره 21، 1-22.
-         Anderson, P. (1985), Adaptive forgetting in recursive identification through multiple models, International Journal of Control 42, 1175– 1193.
-         Blommestein, K.V., Daim, T.U, Cho, Y., Sklar, P. (2018), Structuring financial incentives for residential solar electric systems, Renewable Energy 115, 28-40.
-         Bowman, A.W., Azzalini, A. (1997), Applied Smoothing Techniques for Data Analysis. New York: Oxford University Press.
-         Chediak, M., Martin C., Wells, K. (2013), Utilities Feeling Rooftop Solar Heat Start Fighting Back, Renewable Energy World, 31.
-         Database of State Incentives for Renewable Energy (DSIRE), Available at:
-         Energy Information Administration (2012 EIA), Annual Energy Review 2013. Available at:
-         Geroski, P.A. (2000), Models of technology diffusion, Research Policy 29(4), 603-625.
-         Guo, X., Guo, X. (2015), China's photovoltaic power development under policy incentives: A system dynamics analysis, Energy 93, 589-598.
-         International Renewable Energy Agency (2015), Renewable Energy Prospects, United States of America.
-         Jacobs, D., Marzolf, N., Paredes, J.R., Rickerson, W., Flynn, H., Becker-Birck, C., Solano-Peralta, M. (2013), Analysis of renewable energy incentives in the Latin America and Caribbean region: The feed-in tariff case, Energy Policy 60, 601–610.
-         Ljung, L. (1997), System Identification: Theory for the user, Prentice Hall.
-         Matisoff, D.C., Johnson, E.P. (2017), The comparative effectiveness of residential solar incentives, Energy Policy 108, 44–54.
-         Norberto, C., Gonzalez-Brambila, C.N., Matsumoto, Y. (2016), Systematic analysis of factors affecting solar PV deployment, Journal of Energy Storage 6, 163–172.
-         Pablo-Romero, M.P., Sanchez-Braza, A., Perez, M. (2013), Incentives to promote solar thermal energy in Spain, Renewable and Sustainable Energy Reviews 22, 198–208.
-         Sarzynski, A., Larrieu, J., Shrimali, G. (2012), The impact of state financial incentives on market deployment of solar technology, Energy Policy 46, 550–55.
-         Sawhney, R., Thakur, K., Venkatesan, B., Ji, S., Upreti, G., Sanseverino, J. (2014), Empirical analysis of the solar incentive policy for Tennessee solar value chain, Applied Energy 131, 368–376.
-         Shakouri G., H., Menhaj, M.B. (2008), A Systematic Fuzzy Decision-Making Process to Choose the Best Model Among a Set of Competing Models, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 38(5), 1118–1128.
-         Tsilingiridis, G., Ikonomopoulos, A. (2013), First results of incentives policy on grid interconnected photovoltaic systems development in Greece, Energy Policy 58, 303–311.
-         US Department of Energy (2012 DOE), Photovoltaic (PV) Pricing Trends: Historical, Recent, and Near-Term Projections.
-         Veryard, R. (2005), Systems thinking for demanding change, Available at:
-         World Bank (2013). Available at:
-         Zellner, A. (1962). An efficient method of estimating seemingly unrelated regression equations and tests for aggregation bias. Journal of the American Statistical Association, 57, 348–368.
Volume 5, Issue 4
February 2017
Pages 43-77
  • Receive Date: 18 January 2017
  • Revise Date: 22 September 2017
  • Accept Date: 14 December 2017
  • First Publish Date: 20 February 2018