Abstract
This work aims to provide an accurate assessment of a demand response program that encourages cyber-physical interactions in residential power distribution systems. The advanced technologies considered in this chapter include Wi-Fi-enabled programmable smart thermostats, high-efficiency and connected water heaters, residential battery storage systems, improved weatherproofing, and advanced metering infrastructure (AMI). We present the design of a field demonstration study, the data collection method, and the data-driven comparative evaluation methodology. In analyzing the impacts of various technologies on a home’s coincident load, we propose a novel day-matching algorithm combined with a paired t-test. In analyzing annual energy savings and efficiency, we propose a two-stage algorithm considering three seasons (shoulder, winter, and summer) and degree-day adjustment factors. The new evaluation methods are implemented on a demand response pilot program with 330 participating homes in a mid-western US municipality, each installed with various technologies. Computational results on the impacts of each technology on the coincident load and annual energy savings show the proposed data-driven methods are effective and scalable.
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Bai, L., Roy, A. (2023). Novel Data-Driven Methods for Evaluating Demand Response Programs in a Smart Grid. In: Fathi, M., Zio, E., Pardalos, P.M. (eds) Handbook of Smart Energy Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-97940-9_152
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DOI: https://doi.org/10.1007/978-3-030-97940-9_152
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