Harnessing the Flexibility of Thermostatic Loads in Microgrids with Solar Power Generation

Rosa Morales González, Shahab Shariat Torbaghan, Madeleine Gibescu and Sjef Cobben
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Rosa Morales González: Electrical Energy Systems Group, Department of Electrical Engineering, Eindhoven University of Technology, 5612AP Eindhoven, The Netherlands
Shahab Shariat Torbaghan: Electrical Energy Systems Group, Department of Electrical Engineering, Eindhoven University of Technology, 5612AP Eindhoven, The Netherlands
Madeleine Gibescu: Electrical Energy Systems Group, Department of Electrical Engineering, Eindhoven University of Technology, 5612AP Eindhoven, The Netherlands
Sjef Cobben: Electrical Energy Systems Group, Department of Electrical Engineering, Eindhoven University of Technology, 5612AP Eindhoven, The Netherlands

Energies, 2016, vol. 9, issue 7, 1-24

Abstract: 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.

Keywords: commercial and industrial areas; demand response; genetic algorithm; microgrids; mixed-integer optimization; physical system modeling; local RES integration; smart grid; thermostatic load modeling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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