NDLI: Quantifying flexibility of residential thermostatically controlled loads for demand response: a data-driven approach

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Quantifying flexibility of residential thermostatically controlled loads for demand response: a data-driven approach

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Quantifying flexibility of residential thermostatically controlled loads for demand response: a data-driven approach

Content Provider	ACM Digital Library
Author	Callaway, Duncan S. MacDonald, Jason S. Kiliccote, Sila Tabone, Michaelangelo D. Kara, Emre Can
Abstract	Power systems are undergoing a paradigm shift due to the influx of variable renewable generation to the supply side. The resulting increased uncertainty has system operators looking to new resources, enabled by smart grid technologies, on the demand side to maintain the balance between supply and demand. This study uses a unique data set to estimate and validate models of demand response from residential thermostatically controlled loads (TCLs)---specifically, HVAC units---and quantifies the extent to which a population of TCLs can provide demand response (DR). We use measured temperature setpoints, internal temperatures, compressor cycling ratio and metered energy data collected from over 4200 homes in Texas during the summer of 2012. Using autoregressive moving average (ARMA) models for individual households, we investigate the instantaneous power shed, the duration of the power shed, steady state energy savings and total energy savings. Specifically, we provide insight into the dependency of household DR availability to the temperature setpoint schedule, outdoor air temperature and time of the day.
Starting Page	140
Ending Page	147
Page Count	8
File Format	PDF
ISBN	9781450331449
DOI	10.1145/2674061.2674082
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-11-03
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Thermostatically controlled loads Load shedding Demand response Inverse building model
Content Type	Text
Resource Type	Article

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Quantifying Flexibility of Residential Thermostatically Controlled Loads for Demand Response: A Data-driven Approach

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