This report is one in a series of Electrification Futures Study (EFS) publications. The EFS is a multi-year research project to explore widespread electrification in the future energy system of the United States. This report documents a new model, the demand-side grid (dsgrid) model, which was developed for the EFS and in recognition of a general need for a more detailed understanding of electricity load. dsgrid utilizes a suite of bottom-up engineering models across all major economic sectors-transportation, residential and commercial buildings, and industry-to develop hourly electricity consumption profiles for every county in the contiguous United States (CONUS). The consumption profiles are available by subsector and end use as well as in aggregate. This report documents a bottom-up modeling assessment of historical ( 2012) consumption and explains the key inputs, methodology, assumptions, and limitations of dsgrid.The EFS is specifically designed to examine electric technology cost advancement and adoption for end uses across all major economic sectors as well as electricity consumption growth and load profiles, future power system infrastructure development and operations, and the economic and environmental implications of electrification. Because of the expansive scope and the multiyear duration of the study, research findings and supporting data will be published as a series of reports, with each report released on its own timeframe. Future research to be presented in future planned EFS publications will rely on dsgrid to analyze the hourly electricity consumption under scenarios with various levels of electrification. In addition to providing electricity consumption data for the planned EFS analysis, dsgrid can be used for other analysis outside the EFS research umbrella.More information and the supporting data associated with this report, links to other reports in the EFS study, and information about the broader study are available at www.nrel.gov/efs.

Fault detection and diagnosis (FDD) algorithms for building systems and equipment represent one of the most active areas of research and commercial product development in the buildings industry. However, far more e↵ort has gone into developing these algorithms than into assessing their performance. As a result, considerable uncertainties remain regarding the accuracy and e↵ectiveness of both research-grade FDD algorithms and commercial products-a state of a↵airs that has hindered the broad adoption of FDD tools. This article presents a general, systematic framework for evaluating the performance of FDD algorithms. The article focuses on understanding the possible answers to two key questions: in the context of FDD algorithm evaluation, what defines a fault and what defines an evaluation input sample? The answers to these questions, together with appropriate performance metrics, may be used to fully specify evaluation procedures for FDD algorithms.

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The electric grid is rapidly evolving as small-scale, demand-side resources play increasingly important roles in grid operations and decarbonization. Maximizing the potential of demand-side resources involves incentivizing electricity customers to use those resources in ways that benefit the broader electric grid. These incentives depend largely on the electricity cost savings that customers can realize by adopting demand-side resources. Determining these potential cost savings is a complex task. Cost savings depend on numerous factors, including the characteristics of different technologies, the algorithms that control these devices, system performance, customer behavior, electricity rate structures, and climatic factors. Another challenge is that estimated cost savings are frequently based on modeled rather than observed system performance, particularly in the literature.In this study, we begin to fill the gap in empirical research of demand-side resources using data from a new-construction residential community equipped with rooftop solar and storage (S+S) in Arizona. We use these data to analyze the factors that determine customer electricity cost savings and emissions impacts of S+S in the real world. We then compare these data to modeled system performance to understand how models deviate from real-world outcomes. Based on these findings, we explore ways to improve such models and, conversely, use modeled results to suggest improvements to actual S+S deployment. The results of these analyses can be summarized in four key findings. Rate structures play a central role in the grid and customer value of demand-side resources.In the Arizona case study, the local utility enrolled all households in the community in an experimental rate designed for customers with demand-side resources. The data show that the distributed generation rate benefited the grid by reshaping customer grid demand profiles, especially by reducing demand during grid peak periods. At the same time, the challenge associated with reducing demand charges in the pilot rate plan eroded the customer cost savings from S+S adoption. The resulting erosion of customer value caused at least some community members to switch back to a time-of-use rate plan that was less beneficial to grid operations. In this case study and in other circumstances, there is a tension between designing rates that benefit the electric grid and providing incentives that induce customers to adopt demand-side resources.Certain customers can benefit more from demand-side resource adoption than others. Electricity cost savings varied significantly across households in our case study, even though the newly constructed, energy efficient homes were all equipped with similar S+S systems. Household-level factors that drive cost savings include total electricity demand, demand profiles (e.g., more use during on-peak hours), and differences in home square footage. Modeled battery dispatch and sizing reveals opportunities for additional cost savings.Modeled results show that current batte...