Demand Response of a TCL population using Switching-Rate Actuation

Publication: Research - peer-reviewJournal article

Abstract

This work considers the problem of actively managing the power consumption of a large number of thermostically controlled loads (TCLs), namely a TCL population, and a case-study of household refrigerators. Control is performed using a new randomized actuation that consists of switching units on and off
at given rates, while at the same time respecting the nominal constraints on each individual unit. Both the free and the controlled behavior of individual TCLs can be aggregated, making it possible to handle a TCL population as if it were a single system. The aggregation method uses the distribution of the TCLs individual states across the population. The distribution approach has two main advantages. It scales excellently since the computational requirements do not increase with the number of units, and it allows data from individual units to be used anonymously, which solves privacy concerns relevant for consumer adoption.
Close

Details

This work considers the problem of actively managing the power consumption of a large number of thermostically controlled loads (TCLs), namely a TCL population, and a case-study of household refrigerators. Control is performed using a new randomized actuation that consists of switching units on and off
at given rates, while at the same time respecting the nominal constraints on each individual unit. Both the free and the controlled behavior of individual TCLs can be aggregated, making it possible to handle a TCL population as if it were a single system. The aggregation method uses the distribution of the TCLs individual states across the population. The distribution approach has two main advantages. It scales excellently since the computational requirements do not increase with the number of units, and it allows data from individual units to be used anonymously, which solves privacy concerns relevant for consumer adoption.
Original languageEnglish
JournalI E E E Transactions on Control Systems Technology
ISSN1063-6536
DOI
StateE-pub ahead of print - 2017

    Keywords

  • Smart Grid, thermostatic loads, Model Based Control, stochastic hybrid systems

Projects

Download statistics

No data available
ID: 207142278