Optimizing electricity consumption of buildings in a microgrid through demand response

R. Morales González, S. Shariat Torbaghan, M. Gibescu, J.F.G. Cobben, Martijn A. Bongaerts, M. de Nes-Koedam, W. Vermeiden

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

6 Citations (Scopus)
210 Downloads (Pure)


This paper optimizes the thermodynamic behavior of buildings through demand response (DR) by operating their mechanical heating/cooling systems at 50% or 100% output capacity on a 15-minute basis. The optimization's objective is either minimizing cost or net electricity consumption, considering hourly prices and renewable energy resource availability in the local microgrid. The proposed DR framework combines thermodynamic models with an automated, genetic-algorithm based optimization, resulting in demonstrable benefits in terms of cost and energy efficiency for the end-users. The optimal DR schedule with multiple heating/cooling output capacity is compared against an unoptimized, business-as-usual scenario and against a DR schedule which allows only a binary operation. Results show that flexibility can be harnessed from the buildings' thermal mass, and that a finer temporal granularity not only improves the cost- and energy performance of the system, but also the utilization of renewable energy sources in the microgrid.
Original languageEnglish
Title of host publication2017 IEEE Manchester PowerTech, Powertech 2017
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)978-1-5090-4237-1
ISBN (Print)978-1-5090-4238-8
Publication statusPublished - 13 Jul 2017
Event12th IEEE PES PowerTech Conference - University of Manchester, Manchester, United Kingdom
Duration: 18 Jun 201722 Jun 2017
Conference number: 12


Conference12th IEEE PES PowerTech Conference
Abbreviated titlePowerTech 2017
Country/TerritoryUnited Kingdom
OtherTowards and Beyond Sustainable Energy Systems
Internet address


  • Demand response
  • genetic algorithm
  • local RES integration
  • physical system modeling
  • smart microgrids


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