Harnessing the flexibility of thermostatic loads in microgrids with Solar Power Generation

R. Morales González, S. Shariat Torbaghan, M. Gibescu, S. Cobben

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This paper presents a demand response (DR) framework that intertwines thermodynamic building models with a genetic algorithm (GA)-based optimization method. The framework optimizes heating/cooling schedules of end-users inside a business park microgrid with local distributed generation from renewable energy sources (DG-RES) based on two separate objectives: net load minimization and electricity cost minimization. DG-RES is treated as a curtailable resource in anticipation of future scenarios where the infeed of DG-RES to the regional distribution network could be limited. We test the DR framework with a case study of a refrigerated warehouse and an office building located in a business park with local PV generation. Results show the technical potential of the DR framework in harnessing the flexibility of the thermal masses from end-user sites in order to: (1) reduce the energy exchange at the point of connection; (2) reduce the cost of electricity for the microgrid end-users; and (3) increase the local utilization of DG-RES in cases where DG-RES exports to the grid are restricted. The results of this work can aid end-users and distribution network operators to reduce energy costs and energy consumption.
Original languageEnglish
Article number547
Number of pages24
Issue number7
Publication statusPublished - 15 Jul 2016


  • commercial and industrial areas
  • demand response
  • genetic algorithm
  • microgrids
  • mixed-integer optimization
  • physical system modeling
  • local RES integration
  • smart grid
  • thermostatic load modeling


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