Engineering topics

Sigrin, Ben (ORCID:0000000314599889)

Publications and source records attributed to Sigrin, Ben (ORCID:0000000314599889).

3 records

DOE OSTI2021

The Solar Influencer Next Door: Predicting Low-Income Solar Referrals and Leads

Increasing the adoption of solar among low-to-moderate income (LMI) households remains an important policy goal because of its promise to simultaneously reduce energy burden and support the just distribution of benefits of renewable energy. However, scaling LMI solar remains challenging due to affordability and access issues. Most existing LMI adoption has occurred under public-funded programs, highlighting the importance of increasing the cost-effectiveness of these programs at scale. We develop a new household-level data set on LMI solar lead acquisition, referrals, and adoption to understand the processes through which LMI solar uptake has occurred in California. Then, we develop models to predict two sub-mechanisms in the solar adoption process: whether an otherwise qualified lead becomes "lost" i.e. non-responsive to outreach and, for existing clients, whether they refer solar to others. For the program analyzed, participants received their solar system at no cost, which deemphasizes economic drivers of solar adoption and could differ from other program experiences. Both models substantially improved the accuracy of prediction relative to a baseline. Overall, we find that peer effects and solar economics are important to predicting referrals, and household demographic factors in lead loss prediction. Finally, we find that referrals are both the highest quality and largest source of LMI solar leads, providing a promising mechanism to expand LMI programs further.

customer acquisition costs↗

DOE OSTI2021

Parcel-Scale Assessment of Rooftop Solar Technical Potential

Understanding the potential for rooftop solar and other distributed energy resources (DERs) to contribute to power system planning is increasingly relevant for cities, utilities, and other planning entities. Such planning efforts typically require an estimate of technical potential, or the feasible technology potential independent of economic considerations. Currently, best-in-class rooftop solar technical potential methods use Light Detection and Ranging (LiDAR) data which can identify each roof plane tilt, azimuth, and unshaded area. However, LiDAR data is not universally available and, even when available, obtaining and processing this data can be expensive. In contrast, parcel-level data is easy to use and widely available as it is generated by jurisdictions to levy property taxes. Such data universally reports building footprint area, which is highly correlated with roof area suitable (developable) for rooftop solar. Moreover, parcel data identifies building end-use, tenure, and other building characteristics not provided by LiDAR. To explore the feasibility of using parcel data to assess technical potential more broadly, we compare estimates using parcel data in Orlando, Florida (HIFLD 2020) to those generated using LiDAR data (Koebrich et al. 2021). We find that the parcel-based method results in accurate technical potential estimates at a block and city-scale, though only after accounting for shading and other factors that derate developable roof area. The results of this study demonstrate a scalable, low-effort approach to assess rooftop solar technical potential for every city and community in the U.S.

census blocks↗

DOE OSTI2020

Distributed Generation Market Demand (dGen) model

The Distributed Generation Market Demand (dGen) model simulates customer adoption of distributed energy resources (DERs) for residential, commercial, and industrial entities in the United States or other countries through 2050. The dGen model can be used for identifying the sectors, locations, and customers for whom adopting DERs would have a high economic value, for generating forecasts as an input to estimate distribution hosting capacity analysis, integrated resource planning, and load forecasting, and for understanding the economic or policy conditions in which DER adoption becomes viable, and for illustrating sensitivity to market and policy changes such as retail electricity rate structures, net energy metering, and technology costs.

Array↗