A model for residential adoption of photovoltaic systems

Abstract

AcknowledgementsFirst and foremost, I would like to thank my adviser Prof. Mani Chandy. He took a chance on me even though I was by no means qualified. In addition, there were moments along the way when things looked quite stark and Mani's support was invaluable in me getting through it. For this, I will always be in his debt. He is one of a handful of researchers I have met that manages to conduct research that has both academic merit and yet a large societal impact. Still, the most valuable thing I have learned from Mani is how to be a good person. AbstractThe rapid rise in the residential photo voltaic (PV) adoptions in the past half decade has created a need in the electricity industry for a widely-accessible model that estimates PV adoption based on a combination of different business and policy decisions. This work analyzes historical adoption patterns and finds fiscal savings to be the single most important factor in PV adoption, with significantly greater predictive power compared to all other socioeconomic factors including income and education. We can create an application available on Google App Engine (GAE) based on our findings that allows all stakeholders including policymakers, power system researchers and regulators to study the complex and coupled relationship between PV adoption, utility economics and grid sustainability. The application allows users to experiment with different customer demographics, tier structures and subsidies, hence allowing them to tailor the application to the geographic region they are studying. This study then demonstrates the different type of analyses possible with the application by studying the relative impact of different policies regarding tier structures, fixed charges and PV prices on PV adoption. vi

Drivers of renewable technology adoption in the household sector

“…However, these studies provide evidence that costs and monetary benefits are the decisive drivers of adoption. Agarwal et al (2015) base their analysis on a large dataset containing information on the sociodemographic characteristics, location and energy use of households in Southern California.…”

“…Surprisingly few studies consider the role of expected returns on the investment, i.e. the difference between the expected costs of the investment and the revenue it generates, including public subsidies designed to stimulate such investments (Agarwal et al, 2015;Braito et al, 2017).…”

Planning for a Distributed Disruption: Innovative Practices for Incorporating Distributed Solar into Utility Planning

“…Two ongoing questions in this field are how to quantify the elasticity of demand to DPV profitability (Gillingham and Tsvetanov 2016) and how to quantify the importance of non-economic factors, such as socioeconomic status or environmental concern. Agarwal et al (2015) define the probability of adoption as a function of bill savings and existing penetration levels. This model, which was trained on prior adoption history for southern California, finds that demographic factors are a second-order effect compared with system profitability.…”

A model for commercial adoption of photovoltaic systems in California

“…In the work by Agarwal et al 1 and Bass, 4 mf(t i ) was replaced by s i , and mF(t i ) was replaced by P i j¼1 s j . Then, OLS estimation can be applied to (9) to estimate parameters p, q, and m. The OLS approach has two drawbacks.…”

“…1,2,14,21,23 In Refs. 1 and 2, the eventual adoption rate was simply assumed to be 30% for residential customers.…”