Household Income Levels as a Driver of Residential Solar Panel Adoption
Keywords:
Adoption, distributed generation, photovoltaic, Smart Grid, renewablesAbstract
The traditional electrical grid, also known as Power Grid, is used to carry power from a few central generators to a large number of users. These Power Grids across U.S. are outdated, have reached their capacities, and need to be upgraded with modern technologies to continue delivery of required levels of energy demand. Furthermore, the overall societal costs resulting from grid blackouts are considered unacceptable (North American Reliability Council (NERC), 2009); therefore, the utility industry needs to respond quickly. The issues are compounded by the current trend of consumers adding distributed generation equipment to the grid, including the growth in solar panels’ installations and use of electric vehicles. While presenting a significant societal issue, it also offers a large business opportunity. The global solar power market is growing rapidly, estimated to reach a value of $130.5 billion U.S. dollars by 2021. The global market for total distributed generation as measured by solar panels, wind power, and biofuel was estimated at $246.1 billion in 2011 and is projected to reach $386 billion U.S. dollars in 2021. This research identifies and examines drivers of residential solar panel adoption in California. Regression analysis of 106198 records of solar panel installations in California for the years 2007–2013 was performed against demographic data for 1,733 zip codes. The research has quantified a positive relationship between household income levels and adoption patterns of solar panels in California. The results can be used to better project revenue growth in the renewables sector as well as enhance predictive analysis of utility capital investments. Also, these results could lead to a better understanding of future business models for operating utility assetsReferences
Software Engineering Institute. (2009). SEI smart grid maturity model overview V1.0. Carnegie Mellon University. Retrieved on February 27, 2010 from http://www.sei.cmu.edu/downloads/smartgrid/SGMM_DocumentDefinition.pdf.
U.S. Department of Homeland Security. (2010). National Infrastructure Protection Plan. Retrieved from National Infrastructure Protection Plan: http://www.dhs.gov/files/programs/editorial_0827.shtm#1
North American Electric Reliability Council. (2009). High-impact, low-frequency event risk to the North American bulk power system: A jointly-commissioned summary report of the North American Electric Reliability Corporation and the U.S. Department of Energy’s November 2009 Workshop. NERC.
Kok, J. K., Scheepers, M. J., & Kamphuis, I. G. (2010). Intelligence in electricity networks for embedding renewables and distributed generation. Intelligent Infrastructures, 179–209.
Murphy, Thomas, W. (2008). Home Photovoltaic Systems for Physicists. Physics Today (American Institute of Physics). Retrieved on March 23, 2009 from http://www.jce.divched.org/pick/home-photovoltaic-systems-physicists.
Gruenspecht, H. (2010). International Energy Outlook 2010 With Projections to 2035 Deputy Administrator International, Energy Markets, Forecast. Washington, DC: Department of Energy.
David, A. (2011). U.S. solar photovoltaic (pv) cell and module trade overview. Abstract retrieved from SSRN: http://ssrn.com/abstract=1873251
Bollinger, B., & Gillingham, K. (2010). Environmental preferences and peer effects in the diffusion of solar photovoltaic panels. Kenneth Gillingham.
Drury, E., Miller, M., Macal, C. M., Graziano, D. J., Heimiller, D., Ozik, J., & Perry IV, T. D. (2012). The transformation of southern California's residential photovoltaics market through third-party ownership. Energy Policy, 42, 681-690
CleanEdge. (2012). Clean Energy Trends 2012 (Clean Energy Trends Report Series, 11th Annual Edition).
Ferrelli, R. (2007). Grid Management Delivers Improved Results. Transmission & Distribution World, 59(12), 38, 40–44.
Leiter, D. (2000). Distributed energy resources. U.S. DOE for Fuel Cell Summit IV, 2000. Washington, DC.
He, M. M., Reutzel, E. M., Jiang, X., Katz, R. H., Sanders, S. R., Culler, D. R., et al. (2008). An architecture for local energy generation, distribution, and sharing. IEEE Energy 2030.
Peças Lopesa, J. A., Hatziargyrioub, N., Mutalec, J., Djapicc, P., & Jenkins, N. (2007). Integrating distributed generation into electric power systems: A review of drivers, challenges and opportunities. Electric Power Systems Research, 77(9), 1189–1203.
Carrasco, J. M., Franquelo, L. G., Bialasiewicz, J. T., Galvan, E., Guisado, R. C., Prats, M. A., et al. (2006). Power-electronic systems for the grid integration of renewable energy sources: A survey. Industrial Electronics, IEEE Transactions on, 53(4), 1002–1016. doi: 10.1109/TIE.2006.878356
Barker, P. P., & De Mello, R. W. (2000). Determining the impact of distributed generation on power systems: I. radial distribution systems. Proceedings of the Power Engineering Society Summer Meeting, 2000, 1645–1656.
National Renewable Energy Laboratory (NREL) (1986). Wind energy resource atlas of the United States. Richland, WA: U.S. Department of Energy.
National Renewable Energy Laboratory (NREL) (2009). PVWatts v2.0. Retrieved from: /http://www.nrel.gov/rredc/pvwattsS.
California Solar Initiative. (2008). A Consumer’s Guide to the California Solar Initiative Statewide Incentives for Solar Energy System. Retrieved from California Solar Initiative, Go Solar: http://www.energy.ca.gov/2008publications/CPUC-1000-2008-026/CPUC-1000-2008-026.PDF
U.S. Census Bureau. (2011). Current population survey, 2011 annual social and economic (ASEC) supplement. Retrieved from http://www.census.gov/apsd/techdoc/cps/cpsmar11.pdf. http://factfinder2.census.gov
Berndt, E.R., Savin, N.E. (1977) Conflict among Criteria for Testing Hypotheses in the Multivariate Linear Regression Model Econometrica, Vol. 45, No. 5, pp. 1263-1277
Downloads
Published
How to Cite
Issue
Section
License
- Papers must be submitted on the understanding that they have not been published elsewhere (except in the form of an abstract or as part of a published lecture, review, or thesis) and are not currently under consideration by another journal published by any other publisher.
- It is also the authors responsibility to ensure that the articles emanating from a particular source are submitted with the necessary approval.
- The authors warrant that the paper is original and that he/she is the author of the paper, except for material that is clearly identified as to its original source, with permission notices from the copyright owners where required.
- The authors ensure that all the references carefully and they are accurate in the text as well as in the list of references (and vice versa).
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
- The journal/publisher is not responsible for subsequent uses of the work. It is the author's responsibility to bring an infringement action if so desired by the author.