Novel Data-Driven Methods for Evaluating Demand Response Programs in a Smart Grid

Lihui Bai () and Arnab Roy ()
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Lihui Bai: University of Louisville
Arnab Roy: Procter and Gamble Co.

A chapter in Handbook of Smart Energy Systems, 2023, pp 287-306 from Springer

Abstract: 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.

Keywords: Demand response; Energy savings; Coincident load; Internet of things; Power distribution (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-97940-9_152

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DOI: 10.1007/978-3-030-97940-9_152

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