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

Author

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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
    Alliander N.V., Groningensingel 1, 6835EA Arnhem, The Netherlands)

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.

Suggested Citation

  • Rosa Morales González & Shahab Shariat Torbaghan & Madeleine Gibescu & Sjef Cobben, 2016. "Harnessing the Flexibility of Thermostatic Loads in Microgrids with Solar Power Generation," Energies, MDPI, vol. 9(7), pages 1-24, July.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:7:p:547-:d:73989
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    References listed on IDEAS

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    Cited by:

    1. Kaiye Gao & Tianshi Wang & Chenjing Han & Jinhao Xie & Ye Ma & Rui Peng, 2021. "A Review of Optimization of Microgrid Operation," Energies, MDPI, vol. 14(10), pages 1-39, May.
    2. Delaram Azari & Shahab Shariat Torbaghan & Hans Cappon & Karel J. Keesman & Madeleine Gibescu & Huub Rijnaarts, 2020. "On the Sensitivity of Local Flexibility Markets to Forecast Error: A Bi-Level Optimization Approach," Energies, MDPI, vol. 13(8), pages 1-18, April.
    3. Postnikov, A. & Albayati, I.M. & Pearson, S. & Bingham, C. & Bickerton, R. & Zolotas, A., 2019. "Facilitating static firm frequency response with aggregated networks of commercial food refrigeration systems," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    4. Rosa Francesca De Masi & Antonio Gigante & Valentino Festa & Silvia Ruggiero & Giuseppe Peter Vanoli, 2021. "Effect of HVAC’s Management on Indoor Thermo-Hygrometric Comfort and Energy Balance: In Situ Assessments on a Real nZEB," Energies, MDPI, vol. 14(21), pages 1-30, November.
    5. Omid Abrishambaf & Pedro Faria & Zita Vale, 2020. "Ramping of Demand Response Event with Deploying Distinct Programs by an Aggregator," Energies, MDPI, vol. 13(6), pages 1-18, March.

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