NREL's dGen Analysts Team Up With Orlando Utilities Commission To Forecast Household Solar Adoption

Analysts Tailor Distributed Generation Market Demand (dGen) Model With Higher-Resolution Projections To Support Orlando’s Distributed Resource Planning and Clean Energy Goals

Feb. 11, 2021 | Contact media relations
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Aerial photo of a cluster of houses in Orlando, Florida. The roofs are shown, which NREL analysts assessed using the dGen model to determine if there is potential for solar rooftop PV adoption.
NREL’s dGen modelers customized the model for Orlando energy planners to simulate the potential for household rooftop solar PV adoption through 2050. Photo courtesy of iStock

As Orlando, Florida, targets 100% clean energy by 2050, attention has turned to solar energy—but rooftop solar photovoltaic (PV) adoption in the Sunshine State has been slow compared to early adopters like California.

To understand the potential for customer-owned solar in their city, the Orlando Utilities Commission (OUC) sought out the team of analysts at the National Renewable Energy Laboratory (NREL) who run the Distributed Generation Market Demand (dGen™) model.

The dGen team used the model in a novel way by individually simulating solar adoption potential for every building in the OUC service territory through the year 2050. The findings highlight key technical and economic drivers that influence customer decisions and pinpoint areas with greater adoption potential.

"We customized the dGen model for the OUC's needs," said Ben Sigrin, chief dGen scientist at NREL. "The detailed spatial resolution of the model revealed that there is great potential for rooftop solar PV adoption in the OUC service territory concentrated in a small number of distribution feeders. These insights can help Orlando energy planners more efficiently plan for grid upgrades."

Same Bottom-Up, Agent-Based Modeling, New Spatial Resolution

The dGen model uses a unique bottom-up, agent-based approach to simulate customer decisions about adopting and using solar, wind, and storage technologies for residential and commercial entities in the United States through 2050. Agents, or independent decision-making entities, are based on real data for a given area.

Open-source as of October 2020, dGen provides public users with access to model methodology, instructions, and code. The dGen team can also provide technical support and customize the model as needed. For the OUC, the team prepared datasets for residential, commercial, and industrial buildings within the OUC service area, including electricity consumption, property assessment, and light detection and ranging (LiDAR) roof scans—measurements that determine if a roof is suitable for solar.

An agent, or potential solar adopter, was designated for each building and fell into three categories: those who do not find it economic to adopt solar, those who can adopt a system that offsets some of their consumption, and those who can adopt solar that offsets 100% of their annual consumption. The agents also included socio-demographic attributes of the building occupants and factored in decision-making peer effects, or the influence of solar adoption by neighbors.

dGen then individually modeled agents in scenarios with different costs of solar and tariff structures through the year 2050 to see if they would choose to adopt solar. The resulting adoption potential was then mapped to distribution feeders.

When, Where, and How Much Rooftop Solar PV Could Be Adopted in Orlando?

dGen simulations show there is substantial potential for rooftop solar PV adoption in single-family homes in the OUC service territory—248 megawatts projected by 2030 and 370 megawatts by 2050.

Renter-occupied and multi-family homes, which traditionally have lower adoption rates, also have substantial potential and could increase the overall projection to 355 megawatts by 2030 and 585 megawatts in 2050 if they were to adopt at levels similar to owner-occupied buildings. Pockets of the highest potential for these buildings–particularly for larger systems–were found near Holden Heights and Lee Vista Boulevard and scattered throughout downtown Orlando.

Twenty-five percent of all projected adoption through 2050 is concentrated on just 5% of distribution feeders and 88% of projected adoption on 50% of feeders—the highest-resolution solar projections to date for the OUC, enabling them to effectively plan grid upgrades.

The payback period for the rooftop solar PV systems varies, according to model results, but is largely driven by solar costs and changes to solar valuation by OUC, as well as the expiration of the Federal Investment Tax Credit. By 2030, most residential systems are profitable after 8 years.

Across all scenarios, much of the growth in rooftop solar adoption is projected to occur by 2035, and adoption is sensitive to both rate reform and lower PV costs. While a lower cost of PV significantly increases the amount of adoption by 2030 and 2050, adoption also decreases by a similar degree with tariff reform that replaces net metering with net billing and introduces residential time-of-use rates.

Elevate Your Distributed Generation Studies

"As customers increasingly adopt distributed solar, storage, electric vehicles, and other distributed energy resources, bottom-up, agent-based solar adoption modeling at the household-level will be integral to long-term resource planning," said Paul Schwabe, dGen financial analyst.

The detailed spatial resolution of the dGen model can assist energy planners with load forecasting, distribution system planning, or integrated resource planning to anticipate future distributed energy resource growth.

Are you interested in developing forecasts for adoption of distribution energy resources to inform your planning? The dGen team can customize the model for your needs to project adoption and use of solar, storage, or wind technologies across residential, commercial, and industrial sectors in the United States and countries around the world. Contact the dGen team or visit the dGen website to learn more.

Tags: Solar Market Research,Energy Analysis,Partnerships