Demand response of medical freezers in a Business Park Microgrid

R.M.D.G. Morales Gonzalez, 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)
111 Downloads (Pure)

Abstract

This paper presents a demand response (DR) framework that utilizes the flexibility inherent to the thermodynamic behavior of four groups of independently-controlled medical freezers in a privately-owned business park microgrid that contains rooftop photovoltaics (PV). The optimization objectives may be chosen from the following 3 options: minimizing electricity exchanges with the public grid; minimizing costs by considering prices and RES availability; and minimizing peak load. The proposed DR framework combines thermodynamic models with automated, genetic-algorithm-based optimization, resulting in demonstrable benefits in terms of cost, energy efficiency, and peak power reduction for the consumer, local energy producer, and grid operator. The resulting optimal DR schedules of the freezers are compared against unoptimized, business-as-usual scenarios with- and without PV. Results show that flexibility can be harnessed from the thermal mass of the freezers and their contents, improving the cost- and energy performance of the system with respect to the business-as-usual scenarios.

Original languageEnglish
Title of host publicationSMARTGREENS 2018 - Proceedings of the 7th International Conference on Smart Cities and Green ICT Systems
EditorsCornel Klein, Brian Donnellan, Markus Helfert
PublisherSCITEPRESS-Science and Technology Publications, Lda.
Pages120-129
Number of pages10
ISBN (Electronic)9789897582929
DOIs
Publication statusPublished - 5 Apr 2018
Event7th International Conference on Smart Cities and Green ICT Systems, SMARTGREENS 2018 - Funchal, Madeira, Portugal
Duration: 16 Mar 201818 Mar 2018

Conference

Conference7th International Conference on Smart Cities and Green ICT Systems, SMARTGREENS 2018
Country/TerritoryPortugal
CityFunchal, Madeira
Period16/03/1818/03/18

Keywords

  • Demand Response
  • Genetic Algorithm
  • Local RES Integration
  • Physical System Modeling
  • Smart Grid

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