Reduced-Order Load Models for Large Populations of Flexible Appliances

Mahnoosh Alizadeh, Anna Scaglione, Andy Applebaum, George Kesidis, Karl Levitt

Research output: Contribution to journalArticle

27 Scopus citations

Abstract

To respond to volatility and congestion in the power grid, demand response (DR) mechanisms allow for shaping the load compared to a base load profile. When tapping on a large population of heterogeneous appliances as a DR resource, the challenge is in modeling the dimensions available for control. Such models need to strike the right balance between accuracy of the model and tractability. The goal of this paper is to provide a medium-grained stochastic hybrid model to represent a population of appliances that belong to two classes: deferrable or thermostatically controlled loads. We preserve quantized information regarding individual load constraints, while discarding information about the identity of appliance owners. The advantages of our proposed population model are 1) it allows us to model and control load in a scalable fashion, useful for ex-ante planning by an aggregator or for real-time load control; 2) it allows for the preservation of the privacy of end-use customers that own submetered or directly controlled appliances.

Original languageEnglish (US)
Article number6898044
Pages (from-to)1758-1774
Number of pages17
JournalIEEE Transactions on Power Systems
Volume30
Issue number4
DOIs
StatePublished - Jul 1 2015
Externally publishedYes

Keywords

  • Clustering
  • deferrable loads
  • electric vehicles
  • load management
  • load modeling

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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