Modeling the Diffusion of Residential Photovoltaic Systems in Italy: An Agent-based Simulation
We propose an agent-based model to simulate the diffusion of small PV systems among single- or two-family homes in Italy over the 2006-2026 period. To this end,we explicitly model the geographical distribution of the agents in order to account for regional differences across the country. The adoption decision is assumed to be influenced predominantly by (1) the payback period of the investment, (2) its environmental benefit, (3) the household’s income, and (4) the influence of communication with other agents. For the estimation of the payback period, the model considers investment costs, local irradiation levels, governmental support, earnings from using self-produced electricity vs. buying electricity from the grid, as well as various administrative fees and maintenance costs. The environmental benefit is estimated by a proxy for the CO2 emissions saved. The level of the household income is associated with the specific economic conditions of the region where the agent is located, as well as the agent’s socio-economic group (age group, level of education, household type). Finally, the influence of communication is measured by the number of links with other households that have already adopted a PV system. In each simulation step, the program dynamically updates the social system and the communication network, while the evolution of the PV system’s investment costs depend on a one-factor experience curve model that is based on the exogeneous development of the global installed PV capacity. Our results show that Italy’s domestic PV installations are already beyond an initial stage of rapid growth and, though likely to spread further, they will do so at a significantly slower rate of diffusion.
|Date of creation:||May 2013|
|Date of revision:|
|Contact details of provider:|| Web page: http://www.eonerc.rwth-aachen.de/fcn|
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Crescenzio Gallo & Michelangelo De Bonis, 2011. "Forecasting Photovoltaic Deployment with Neural Networks," Quaderni DSEMS 02-2011, Dipartimento di Scienze Economiche, Matematiche e Statistiche, Universita' di Foggia.
- Meade, Nigel & Islam, Towhidul, 2006. "Modelling and forecasting the diffusion of innovation - A 25-year review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 519-545.
- Werker, C. & Brenner, T., 2004.
"Empirical calibration of simulation models,"
04.13, Eindhoven Center for Innovation Studies.
- Claudia Werker & Thomas Brenner, 2004. "Empirical Calibration of Simulation Models," Papers on Economics and Evolution 2004-10, Philipps University Marburg, Department of Geography.
- Thomas Brenner & Claudia Werker, 2004. "Empirical Calibration of Simulation Models," Computing in Economics and Finance 2004 89, Society for Computational Economics.
- Malte Schwoon, 2005. "Simulating the Adoption of Fuel Cell Vehicles," Working Papers FNU-59, Research unit Sustainability and Global Change, Hamburg University, revised Feb 2006.
- Giorgio Fagiolo & Alessio Moneta & Paul Windrum, 2007. "A Critical Guide to Empirical Validation of Agent-Based Models in Economics: Methodologies, Procedures, and Open Problems," Computational Economics, Society for Computational Economics, vol. 30(3), pages 195-226, October.
- Jager, Wander, 2006. "Stimulating the diffusion of photovoltaic systems: A behavioural perspective," Energy Policy, Elsevier, vol. 34(14), pages 1935-1943, September.
- Malte Schwoon, 2006. "Simulating the adoption of fuel cell vehicles," Journal of Evolutionary Economics, Springer, vol. 16(4), pages 435-472, October.
- Faber, Albert & Valente, Marco & Janssen, Peter, 2010. "Exploring domestic micro-cogeneration in the Netherlands: An agent-based demand model for technology diffusion," Energy Policy, Elsevier, vol. 38(6), pages 2763-2775, June.
- Faiers, Adam & Neame, Charles & Cook, Matt, 2007. "The adoption of domestic solar-power systems: Do consumers assess product attributes in a stepwise process?," Energy Policy, Elsevier, vol. 35(6), pages 3418-3423, June.
- Zhai, Pei & Williams, Eric D., 2012. "Analyzing consumer acceptance of photovoltaics (PV) using fuzzy logic model," Renewable Energy, Elsevier, vol. 41(C), pages 350-357.
- Ayompe, L.M. & Duffy, A. & McCormack, S.J. & Conlon, M., 2010. "Projected costs of a grid-connected domestic PV system under different scenarios in Ireland, using measured data from a trial installation," Energy Policy, Elsevier, vol. 38(7), pages 3731-3743, July.
- Thomas Brenner & Claudia Werker, 2007. "A Taxonomy of Inference in Simulation Models," Computational Economics, Society for Computational Economics, vol. 30(3), pages 227-244, October.
- Elmar Kiesling & Markus Günther & Christian Stummer & Lea Wakolbinger, 2012. "Agent-based simulation of innovation diffusion: a review," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(2), pages 183-230, June.
- Sorda, G. & Sunak, Y. & Madlener, R., 2013. "An agent-based spatial simulation to evaluate the promotion of electricity from agricultural biogas plants in Germany," Ecological Economics, Elsevier, vol. 89(C), pages 43-60.
- Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
- Yuan, Xueliang & Zuo, Jian & Ma, Chunyuan, 2011. "Social acceptance of solar energy technologies in China--End users' perspective," Energy Policy, Elsevier, vol. 39(3), pages 1031-1036, March.
- Yang, Chi-Jen, 2010. "Reconsidering solar grid parity," Energy Policy, Elsevier, vol. 38(7), pages 3270-3273, July.
- Dawid, Herbert, 2006. "Agent-based Models of Innovation and Technological Change," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 25, pages 1235-1272 Elsevier.
- Zheng, Tian & Salganik, Matthew J. & Gelman, Andrew, 2006. "How Many People Do You Know in Prison?: Using Overdispersion in Count Data to Estimate Social Structure in Networks," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 409-423, June.
When requesting a correction, please mention this item's handle: RePEc:ris:fcnwpa:2013_009. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Hendrik Schmitz)
If references are entirely missing, you can add them using this form.