U.S. Department of Energy

Understanding the Evolution of Customer Motivations and Adoption Barriers in Residential Photovoltaics Markets

Logos of the National Renewable Energy Laboratory, the University of Arizona, the University of Michigan, the University of Colorado Boulder, Michigan State University, the Social Environmental Research Institute, Lawrence Berkeley National Laboratory, Portland State University, and Clean Power Finance. Graph of a square with lines and different colors.

This project focuses on solar diffusion data synthesis. An expanding collection of data streams are being mined to identify and interpret patterns of residential solar adoption across the United States.

National Renewable Energy Laboratory, along with Portland State University, the University of Arizona, Clean Power Finance, and other partners, under the Solar Energy Evolution and Diffusion Studies (SEEDS) program, is performing micro-level studies in four representative U.S. regions to identify generalizable household-level motivations for adopting residential photovoltaics (PV), and to refine computational modeling frameworks for simulating current and future solar adoption trends.


Decisions to adopt new technologies are influenced by more than price alone. Whether a household adopts a rooftop PV system may depend on access to trusted information, established social norms, context-specific motivations and concerns, and the experiences from a customer's peer group. Each of these factors offers a potential leverage point for accelerating solar adoption. This research project is focused on understanding and simulating the complex household-level decision making processes that underlie residential PV market demand, and capturing the interactions between decision variables and their evolution over time.


First, the project team will select four representative solar markets for in-depth investigation and form partnerships with local solar businesses and decision makers. Rich datasets will be collected to characterize PV market dynamics within each region. These data will be synthesized into a computational framework that will simulate the impact of specific market modifications on PV demand, such as introducing innovative new financing structures, new methods for framing the benefits or risks associated with a solar investment, or new ways to bundle solar with energy efficiency technologies. Finally, pilot experiments will be performed to test new strategies for accelerating solar diffusion processes.


Reducing the price of renewable energy technologies is necessary but not sufficient to support wide-scale adoption. This research will quantify the other key variables that drive innovation diffusion processes.