A model for residential adoption of photovoltaic systems

Due to the growth in the number of residential photo voltaic (PV) adoptions in the past five years, there is a need in the electricity industry for a widely-accessible model that predicts the adoption of PV based on different business and policy decisions. We analyze historical adoption patterns and find that monetary savings is the most important factor in the adoption of PV, superseding all socioeconomic factors. On the basis of the findings from our data analysis, we created an application available on Google App Engine (GAE), that allows researchers, policymakers and regulators to study the complex relationship between PV adoption, grid sustainability and utility economics. This application allows users to experiment with a variety of scenarios including different tier structures, subsidies and customer demographics. We showcase the type of analyses that are possible with this application by using it to study the impact of different policies regarding tier structures, fixed charges and PV prices.

[1]  Kenneth Gillingham,et al.  Peer Effects in the Diffusion of Solar Photovoltaic Panels , 2012, Mark. Sci..

[2]  Gary L. Lilien,et al.  A market entry timing model for new techniques , 1986 .

[3]  G. Perakis,et al.  Consumer Choice Model for Forecasting Demand and Designing Incentives for Solar Technology , 2011 .

[4]  Farrokh Albuyeh,et al.  Grid of the future , 2009, IEEE Power and Energy Magazine.

[5]  M. Guidolin,et al.  Cross-country diffusion of photovoltaic systems: Modelling choices and forecasts for national adoption patterns , 2010 .

[6]  Andrew Ford,et al.  System Dynamics and the Electric Power Industry , 1997 .

[7]  J. Sweeney,et al.  Learning-by-Doing and the Optimal Solar Policy in California , 2008 .

[8]  Ryan Wiser,et al.  The Impact of Rate Design and Net Metering on the Bill Savings from Distributed PV for Residential Customers in California , 2011 .

[9]  Winfried Hoffmann,et al.  PV solar electricity industry: Market growth and perspective , 2006 .

[10]  S. Kalish,et al.  A MARKET ENTRY TIMING MODEL FOR NEW TECHNOLOGIES * , 2004 .

[11]  Donna Heimiller,et al.  The transformation of southern California's residential photovoltaics market through third-party ownership , 2012 .

[12]  Adam Faiers,et al.  Consumer attitudes towards domestic solar power systems , 2006 .

[13]  F. Bass,et al.  A diffusion theory model of adoption and substitution for successive generations of high-technology products , 1987 .

[14]  K. M. Chandy,et al.  Impact of residential PV adoption on Retail Electricity Rates , 2013 .

[15]  Charles Neame,et al.  Towards a contemporary approach for understanding consumer behaviour in the context of domestic energy use , 2007 .

[16]  Ryan Wiser,et al.  Customer-economics of residential photovoltaic systems (Part 1): The impact of high renewable energy penetrations on electricity bill savings with net metering , 2014 .

[17]  A. Créti,et al.  Let the sun shine: Optimal deployment of photovoltaics in Germany , 2012 .

[18]  Varun Rai,et al.  Diffusion of environmentally-friendly energy technologies: buy versus lease differences in residential PV markets , 2013 .

[19]  V. Rai,et al.  Effective information channels for reducing costs of environmentally- friendly technologies: evidence from residential PV markets , 2013 .