Scalable model predictive control of demand for ancillary services

Mahnoosh Alizadeh, Anna Scaglione, George Kesidis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

In this paper, we develop an integrated decision making framework for the planning and real-time control decisions made by a Load Serving Entity (LSE) providing ancillary services to the wholesale market. Due to the multi-settlement structure of the energy market, planning decisions by the LSE are naturally made at multiple temporal stages. The tight interdependence among decisions demands an integrated approach to minimize the overall costs of operation. In order to model the dynamics of the load at large-scales when making these decisions, we propose a classification-based model that captures the effect of scheduling decisions made for individual appliances at aggregate levels, with reasonable effort. To provide a tangible example of how this load aggregation technique can be applied, we study the case of Electric Vehicle (EV) charging in detail.

Original languageEnglish (US)
Title of host publication2013 IEEE International Conference on Smart Grid Communications, SmartGridComm 2013
Pages684-689
Number of pages6
DOIs
StatePublished - Dec 1 2013
Event2013 IEEE International Conference on Smart Grid Communications, SmartGridComm 2013 - Vancouver, BC, Canada
Duration: Oct 21 2013Oct 24 2013

Publication series

Name2013 IEEE International Conference on Smart Grid Communications, SmartGridComm 2013

Other

Other2013 IEEE International Conference on Smart Grid Communications, SmartGridComm 2013
CountryCanada
CityVancouver, BC
Period10/21/1310/24/13

ASJC Scopus subject areas

  • Hardware and Architecture

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  • Cite this

    Alizadeh, M., Scaglione, A., & Kesidis, G. (2013). Scalable model predictive control of demand for ancillary services. In 2013 IEEE International Conference on Smart Grid Communications, SmartGridComm 2013 (pp. 684-689). [6688038] (2013 IEEE International Conference on Smart Grid Communications, SmartGridComm 2013). https://doi.org/10.1109/SmartGridComm.2013.6688038