A Model For Residential Adoption of Photovoltaic Systems

Agarwal, 2015

Category: EE

Overall Rating

1.7/5 (12/35 pts)

This paper provided empirical evidence for financial savings as the primary driver of residential PV adoption and modeled this within a diffusion framework incorporating utility rate feedback.
However, its reliance on outdated data and policy contexts from pre-2015 California critically limits its current relevance.
While the abstract concept of empirically identifying a dominant economic driver and modeling system feedback is broadly applicable, modern researchers can achieve superior insights using current datasets and more sophisticated modeling techniques without needing to leverage this specific paper's methods or findings.

The paper builds upon the well-established Bass diffusion model, extending it by segmenting the population based on calculated economic savings rather than solely socioeconomic factors.
While integrating economic drivers and system-level feedback (utility rate changes impacting future savings) is a valuable application, the core modeling techniques are not fundamentally new.
The concept of modeling technology adoption driven primarily by a quantifiable, system-dependent economic benefit, identified through empirical analysis, has relevance beyond solar PV and utility economics.
Modern access to larger, more granular consumer data (e.g., smart meter data linked with financial/demographic info), advanced machine learning for identifying complex, non-linear drivers, and more powerful/flexible cloud computing platforms would allow for more sophisticated segmentation, more accurate parameter fitting, and the simulation of richer feedback loops and heterogeneous agent behaviors within the diffusion model.

The core of this paper's analysis and model calibration relies on data from Southern California Edison (SCE) residential customers primarily from 2012-2013, with Bass diffusion parameter fitting based on adoption data up to 2011. This is arguably the most significant flaw when considering its relevance today.
California has since moved through NEM 2.0 and, critically, NEM 3.0. NEM 3.0 drastically altered the export credit mechanism... This invalidates any savings calculations or adoption drivers derived from the earlier NEM regime.
The paper's model completely omits storage.
The calculation of savings assumes constant annual usage (based on a single year of historical data), relies on the rate schedule at the time of installation, and uses a linear price model fitted to old data.

Ignore