Real-Time Decision Making Model for Thermostatically Controlled Load Aggregators by Natural Aggregation Algorithm

Chenxi Li, Yingying Chen, Fengji Luo, Zhao Xu, Yu Zheng

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

7 Citations (Scopus)


With the development of Building-To-Grid (B2G) technology, Thermostatically Controlled Loads (TCLs) have been receiving increasing attention because promising candidates for Demand Response (DR) programs. This paper studies real-Time operation schemes of TCL aggregators in the power market. The contribution of this paper is to consider TCL aggregators as bidding players participating in the day-Ahead market, unlike existing works which often treat TCL aggregators as ancillary service providers. We investigate the real-Time operations of TCL aggregators after the determinations of day-Ahead load shedding volumes. The proposed model aims to minimize the operation costs of the TCL aggregator in the real-Time regulation market. A Rolling Horizon Optimization (RHO) technique is utilized to mitigate the impacts of forecasting errors. And a new metaheuristic algorithm, i.e. Natural Aggregation Algorithm (NAA), is applied here to solve the proposed model. Experiments are conducted to validate the proposed method.
Original languageEnglish
Title of host publicationProceedings - 1st IEEE International Conference on Energy Internet, ICEI 2017
Number of pages6
ISBN (Electronic)9781509057597
Publication statusPublished - 11 May 2017
Event1st IEEE International Conference on Energy Internet, ICEI 2017 - Beijing, China
Duration: 17 Apr 201721 Apr 2017


Conference1st IEEE International Conference on Energy Internet, ICEI 2017


  • building-Togrid
  • demand response
  • natural aggregation algorithm
  • Thermostatically controlled load

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Computer Networks and Communications
  • Control and Optimization

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