Ratner S.V.Trapeznikov Institute of Control Sciences, Russian Academy of Sciences, Moscow, Russian Federation lanaratner@gmail.com
Importance Understanding the logic of changes in the cost of energy that is generated using various technologies is a vital aspect in making decisions on the future development of energy systems and working-out the State policy to bolster renewable energy. In recent years, thanks to the State support, several successful solar energy project have been implemented, however, there are none in the wind energy. Objectives The aim of the paper is to review constraining factors in the wind energy development. Methods To test the hypothesis on constraining factors in the wind energy, I employ the methodology of learning curves. Results I performed a meta-analysis of data on learning rates in the wind energy obtained by building the single- and dual-factor learning curve models with specification by countries and technology development periods. I also performed the analysis of cross-country differences. Multiple case studies were used to interpret the results. Conclusions The study reveals that maximal learning rates in the wind energy are achieved via enhanced State support to R&D at early stages of technological development, and via involvement of large manufacturers of wind generation equipment at later stages. Given the well-developed technologies of wind generating equipment manufacturing and highly competitive global market of wind turbines, the tactic of obtaining technologies in exchange for access to the domestic market may prove successful even with small domestic market capacity. Therefore, reducing the requirements for wind projects localization index for the coming 3–5 years seems appropriate.
Keywords: wind energy, learning curve, power engineering, economic analysis
References:
Romer P.M. Increasing returns and long-run growth. Journal of Political Economy, 1986, vol. 94, no. 5, pp. 1002–1037.
Rout U.K., Blesl M., Fahl U., Emme U., Voß A. Uncertainty in the learning rates of energy technologies: An experiment in a global multi-regional energy system model. Energy Policy, 2009, vol. 37, iss. 12, pp. 4927–4942.
Rubin E.S., Azevedo I.M.L., Jaramillo P., Yeh S. A review of learning rates for electricity supply technologies. Energy Policy, 2015, no. 86, pp. 198–218.
Miketa A., Schrattenholzer L. Experiments with a methodology to model the role of R&D expenditures in energy technology learning processes: First results. Energy Policy, 2004, no. 32(15), pp. 1679–1692.
Söderholm P., Sundqvist T. Empirical challenges in the use of learning curves for assessing the economic prospects of renewable energy technologies. Renewable Energy, 2007, no. 32, pp. 2559–2578. doi: 10.1016/j.renene.2006.12.007
Söderholm P., Klaassen G. Wind Power in Europe: A Simultaneous Innovation–Diffusion Model. Environmental and Resource Economics, 2007, no. 36, pp. 163–190. doi: 10.1007/s10640-006-9025-z
Jamasb T. Technical change theory and learning curves: Patterns of progress in electricity generation technologies. Energy Journal, 2007, vol. 28, no. 3, pp. 51–72.
Yeh S., Rubin E.S. A review of uncertainties in technology experience curves. Energy Economics, 2012, no. 34(3), pp. 762–771. doi: 10.1016/j.eneco.2011.11.006
Ratner S.V., Panchenko Yu.M. [Diffusion of new technologies in the energy sector: International standardization as an instrument to minimize non-technical barriers]. Innovatsii = Innovation, 2014, no. 1, pp. 70–76. (In Russ.)
Krohn S. Wind Energy Policy in Denmark: 25 Years of Success – What Now? Copenhagen, Danish Wind Industry Association, 2002.
Bolinger M. Community Wind Power Ownership Schemes in Europe and their Relevance to the United States. Available at: Link.
Klaassen G., Miketa A., Larsen K., Sundqvist T. The impact of R&D on innovation for wind energy in Denmark, Germany and the United Kingdom. Ecological Economics, 2005, no. 54(2), pp. 227–240.
Trappey A.J.C., Trappey C.V., Liu P.H.Y., Lin L.-C., Ou J.J.R. A hierarchical cost learning model for developing wind energy infrastructures. InternationalJournal of Production Economics, 2013, no. 146, pp. 386–391.
Qiu Y., Anadon L.D. The price of wind power in China during its expansion: Technology adoption, learning-by-doing, economies of scale, and manufacturing localization. Energy Economics, 2012, no. 34(3), pp.772–785.
Jacobsson S., Lauber V. The Politics and Policy of Energy System Transformation – Explaining the German Diffusion of Renewable Energy Technology. Energy Policy, 2006, vol. 34, no. 3, pp. 256–276.
Ratner S.V., Iosifov V.V. [Lean production in power engineering]. Materialy mezhdunarodnoi nauchno-prakticheskoi konferentsii Upravlenie innovatsiyami – 2014 [Proc. Int. Sci. Conf. Management of Innovation – 2014]. Novocherkassk, Platov South-Russian State Polytechnic University (NPI) Publ., 2014, pp. 214–218.
Mitchell C., Connor P. Renewable energy policy in the UK 1990–2003. Energy Policy, 2004, vol. 32, no.17, pp.1935–1947. doi: 10.1016/j.enpol.2004.03.016
Graves F., Hanser P., Basheda G. PURPA: Making the Sequel Better than the Original. Available at: Link.
Parsons B. Grid-Connected Wind Energy Technology: Progress and Prospects. Presented at the North American Conference of the International Association of Energy Economists. Albuquerque, NM, 1998. Available at: Link.
Li Junfeng et al. China Wind Energy Outlook, 2012. Available at: Link.
Neij L., Andersen P.D., Durstewitz M., Helby P., Hoppe-Kilpper M., Morthorst P. Experience Curves: A Tool for Energy Policy Assessment. Available at: Link.
Ibenholt K. Explaining learning curves for wind power. Energy Policy, 2002, vol. 30, iss. 13, pp. 1181–1189.
Experience Curves for Energy Technology Policy. OECD, International Energy Agency. France, Paris, 2000. Available at: Link.
McDonald A., Schrattenholzer L. Learning rates for energy technologies. Energy Policy, 2001, no. 29(4), pp. 255–261.
