Department of Energy (DOE) reports produced after 1991 and a growing number of pre-1991 documents are available free via www.OSTI.gov.

The global energy system is undergoing a major transformation. Renewable energy generation is growing and is projected to accelerate further with the global emphasis on decarbonization. Furthermore, distributed generation is projected to play a significant role in the new energy system, and energy models are playing a key role in understanding how distributed generation can be integrated reliably and economically. The deployment of massive amounts of distributed generation requires understanding the interface of technology, economics, and policy in the energy modeling process. In this work, we present an end-to-end computational framework for distributed energy resource (DER) modeling, REopt Lite ™ , which addresses this need effectively. We describe the problem space, the building blocks of the model, the scaling capabilities of the design, the optimization formulation, and the accessibility of the model. We present a framework for accelerating the techno-economic analysis of behind-the-meter distributed energy resources to enable rapid planning and decision-making, thereby significantly boosting the rate the renewable energy deployment. Lastly, but equally importantly, this computation framework is open-sourced to facilitate transparency, flexibility, and wider collaboration opportunities within the worldwide energy modeling community.

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Department of Energy (DOE) reports produced after 1991 and a growing number of pre-1991 documents are available free via www.OSTI.gov.

Distributed photovoltaics (DPV) are a growing source of electricity generation in the United States, and with adoption driven by customer behavior and localized economics, projecting the deployment of this technology is a challenging analytical problem. Moreover, understanding the sources of uncertainty in customer adoption models and how they can be reduced is important to a range of stakeholders that use their outputs, including grid planners, regulators, and industry. Most prior studies have used top-down methods, such as the use of population central tendencies to project aggregate adoption. In contrast, a growing field of work seeks to use bottom-up methods (i.e., individual-level decision-making).We explore trade-offs of top-down and bottom-up methods in their precision and computational burden using the National Renewable Energy Laboratory's (NREL's) Distributed Generation Market Demand (dGen) model, an agent-based model of residential and nonresidential distributed PV adoption. In particular, we assess the role of agent resolution in instantiating statistically-representative populations in the model-and the resulting variance of model projections at the state, sector, and county levels. At low sampling rates, the model resembles a top-down model, whereas as sampling rates increase dGen converges to a bottom-up structure by simulating more unique customer types. Though sampling-based models such as dGen can be operated with many agents to ensure accuracy, doing so greatly increases the computational burden of the simulation. This report lends insight into whether high-resolution results can be approximated sufficiently well using fewer computational resources.vii