Abstract
The purpose of this study is to evaluate the adoption and aggregated diffusion of solar electric systems in the residential sector. The goal of this paper is to try answer the following questions using an Agent-Based Model (ABM):
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1.
Is there evidence of a delay in the aggregate adoption of solar electric systems? If so, how can the adoption be improved?
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What is the relationship between increasing electricity prices, price preference, and rate of adoption?
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What impact does changing the incentive structure have on the overall electricity savings?
The model could be used by electric utility companies, energy program administrators, and government and state agencies for planning purposes.
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N. Meade, T. Islam, Modelling and forecasting the diffusion of innovation—a 25-year review. Int. J. Forecasting 22(3), 519–545 (2006)
H. Rahmandad, J. Sterman, Heterogeneity and network structure in the dynamics of diffusion: comparing agent-based and differential equation models. Manag. Sci. 54(5), 998–1014 (2008)
J.D. Bohlmann, R.J. Calantone, M. Zhao, The effects of market network heterogeneity on innovation diffusion: an agent-based modeling approach. J. Prod. Innov. Manag. 27(5), 741–760 (2010)
C.E. Laciana, S.L. Rovere, Ising-like agent-based technology diffusion model: adoption patterns vs. seeding strategies. Phys. A: Stat. Mech. Appl. 390(6), 1139–1149 (2011)
T. Zhang, W.J. Nuttall, Evaluating government’s policies on promoting smart metering diffusion in retail electricity markets via agent-based simulation. J. Prod. Innov. Manag. 28(2), 169–186 (2011)
E. Beinhocker, Origin of Wealth (Harvard Business Press, Boston, 2006)
N. Gilbert, K. Troitzsch, Simulation for the Social Scientist, 2nd edn. (Open University Press, New York, 2005)
J. Epstein, R. Axtell, Growing Artificial Societies: Social Science from the Bottom Up (Brookings Institution Press, Washington, DC, 1996)
G.A. Moore, Crossing the Chasm: Marketing and Selling High-Tech Products to Mainstream Customer (HarperCollins, New York, 1999)
U.S. Energy Information Administration, Electric Price Monthly [Online] (2013), http://www.eia.gov/electricity/monthly/epm_table_grapher.cfm?t=epmt_5_6_a. Accessed 1 Dec 2013
Energy Trust of Oregon, Solar Electric for Homes: Your Home Resource for Clean Energy [Online] (2013), http://energytrust.org/residential/incentives/solar-electric/SolarElectric/. Accessed 1 Dec 2013
G. Barbose, N. Darghouth, S. Weaver, R. Wiser, Tracking the Sun VI: An Historical Summary of the Installed Price of Photovoltaics in the United States from 1998 to 2012 (Lawrence Berkeley National Laboratory, Berkeley, 2013)
National Renewable Energy Lab, Dynamic Maps, GIS Data & Analysis Tools: Solar Maps [Online] (2013), http://www.nrel.gov/gis/solar.html. Accessed 1 Dec 2013
D. Baylon, P. Storm, K. Geraghty, B. Davis, Residential Building Stock Assessment: Single-Family Characteristics and Energy Use (Northwest Energy Efficiency Alliance, Seattle, 2012)
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Appendix: Model Parameters
Appendix: Model Parameters
8.1.1 Agents and Environment
The global variables, agents, and patches for the solar electric system adoption model.
Global variables (user adjustable variables) | Housing density: The percentage of total patches that are occupied by houses |
Incentives: The incentives offered by utility companies and others to encourage the adoption of the technology | |
Energy price: The price of electricity (kWh) | |
Solar price: The price of a solar electric system per kWh | |
Inequality: The distribution of wealth for one of the communities under evaluation. This distribution of wealth is defined by the equation min-income × exp(random − exponential(1/inequality)). The intention of this equation is to try creating the Pareto distribution | |
Solar hotspots: The percentage of patches that will have the highest solar intensity level. Random patches are chosen and the solar intensity diffuses to the neighborhood patches | |
Initial adopters: The percentage of households that will already have a solar electric system before the simulation starts | |
Maximum interaction radius: Each household can influence other households within a random radius between 0 and maximum interaction radius | |
Maximum random interactions: Each household can influence a random number of other households between 0 and maximum random interactions | |
Maximum budget: Each household has a random percentage of their income, between 0 and 100 %, which they can spend | |
Maximum price preference: A household will decide to move from aware to persuade if electricity monthly payments × price preference ≥ solar monthly payments. This is a random value between 0 and 3 | |
Hide-links: Hide the links that are connecting the households | |
Show-solar: Show the sun hours by using a scaled yellow color for each patch |
Procedures | Setup global: Initializes all the global variables |
Update plot: Update the plots displaying the number of households in each stage of adoption, the distribution of in and out links, and the distribution of income | |
Update display: Observes the status of show-solar? and hide-links? to determine whether the display should be updated, even when the simulation is running |
Agent | Households: Houses are randomly placed on patches. The number of households is controlled by the global housing-density variable |
Characteristics | Income: The total monthly income from all members of the household |
Budget: Percentage of income which the households can spend per month | |
Electricity consumption: The amount of electricity consumed by the household per month (kWh). This is calculated by multiplying the average electricity consumption in Oregon with the ratio of the households income to the median income | |
Adoption stage: A household can be unaware, aware, persuaded, or decided on the technology | |
Price preference: A household will decide to move from aware to persuaded if electricity monthly payments × price preference ≥ solar monthly payments | |
Awareness threshold: How many households need to mention the technology to this household before they change their adoption stage from unaware to aware | |
Persuasion threshold: How many households need to mention the technology to this household before they change their adoption stage from aware to persuaded | |
Interaction radius: The radius of the circle in which a household can influence other households, or be influenced | |
Random interaction: How many random households outside the radius can be influenced? This resembles the random friends discussed by Beinhocker | |
Solar size-required: The size of the solar electric system required. This is calculated by electricity consumption × 12 × 1,000/(365 × [sun-hours] of patch-here × derate-factor). The derate factor for solar electric systems is generally assumed to be 0.77 | |
Own incentives: The amount of incentives the household can obtain for their solar electric system. The maximum amount defined by Energy Trust of Oregon is $5,000 |
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van Blommestein, K.C., Daim, T.U. (2015). Technology Adoption: Residential Solar Electric Systems. In: Daim, T., Kim, J., Iskin, I., Abu Taha, R., van Blommestein, K. (eds) Policies and Programs for Sustainable Energy Innovations. Innovation, Technology, and Knowledge Management. Springer, Cham. https://doi.org/10.1007/978-3-319-16033-7_8
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