Optimizing electricity consumption of buildings in a microgrid through demand response

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

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

2 Citations (Scopus)
105 Downloads (Pure)

Abstract

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 publicationIEEE PES Powertech 2017, 18-22 June 2017, Manchester, United Kingdom
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages1-6
Number of pages6
ISBN (Electronic)978-1-5090-4237-1
ISBN (Print)978-1-5090-4238-8
DOIs
Publication statusPublished - 2017
Event12th IEEE PES PowerTech Conference - University of Manchester, Manchester, United Kingdom
Duration: 18 Jun 201722 Jun 2017
Conference number: 12
http://ieee-powertech.org/

Conference

Conference12th IEEE PES PowerTech Conference
Abbreviated titlePowerTech 2017
CountryUnited Kingdom
CityManchester
Period18/06/1722/06/17
OtherTowards and Beyond Sustainable Energy Systems
Internet address

Fingerprint

Electricity
Thermodynamics
Heating
Costs
Renewable energy resources
Cooling systems
Energy efficiency
Genetic algorithms
Availability
Cooling
Industry
Hot Temperature

Keywords

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

Cite this

Morales González, R., Shariat Torbaghan, S., Gibescu, M., Cobben, J. F. G., Bongaerts, M., de Nes-Koedam, M., & Vermeiden, W. (2017). Optimizing electricity consumption of buildings in a microgrid through demand response. In IEEE PES Powertech 2017, 18-22 June 2017, Manchester, United Kingdom (pp. 1-6). Piscataway: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/PTC.2017.7980983
Morales González, R. ; Shariat Torbaghan, S. ; Gibescu, M. ; Cobben, J.F.G. ; Bongaerts, M. ; de Nes-Koedam, M. ; Vermeiden, W. / Optimizing electricity consumption of buildings in a microgrid through demand response. IEEE PES Powertech 2017, 18-22 June 2017, Manchester, United Kingdom. Piscataway : Institute of Electrical and Electronics Engineers, 2017. pp. 1-6
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abstract = "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.",
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Morales González, R, Shariat Torbaghan, S, Gibescu, M, Cobben, JFG, Bongaerts, M, de Nes-Koedam, M & Vermeiden, W 2017, Optimizing electricity consumption of buildings in a microgrid through demand response. in IEEE PES Powertech 2017, 18-22 June 2017, Manchester, United Kingdom. Institute of Electrical and Electronics Engineers, Piscataway, pp. 1-6, 12th IEEE PES PowerTech Conference, Manchester, United Kingdom, 18/06/17. https://doi.org/10.1109/PTC.2017.7980983

Optimizing electricity consumption of buildings in a microgrid through demand response. / Morales González, R.; Shariat Torbaghan, S.; Gibescu, M.; Cobben, J.F.G.; Bongaerts, M.; de Nes-Koedam, M.; Vermeiden, W.

IEEE PES Powertech 2017, 18-22 June 2017, Manchester, United Kingdom. Piscataway : Institute of Electrical and Electronics Engineers, 2017. p. 1-6.

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

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N2 - 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.

AB - 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.

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Morales González R, Shariat Torbaghan S, Gibescu M, Cobben JFG, Bongaerts M, de Nes-Koedam M et al. Optimizing electricity consumption of buildings in a microgrid through demand response. In IEEE PES Powertech 2017, 18-22 June 2017, Manchester, United Kingdom. Piscataway: Institute of Electrical and Electronics Engineers. 2017. p. 1-6 https://doi.org/10.1109/PTC.2017.7980983