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Energies 2016, 9(7), 547; doi:10.3390/en9070547

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

1
Electrical Energy Systems Group, Department of Electrical Engineering, Eindhoven University of Technology, 5612AP Eindhoven, The Netherlands
2
Alliander N.V., Groningensingel 1, 6835EA Arnhem, The Netherlands
*
Author to whom correspondence should be addressed.
Academic Editor: G.J.M. (Gerard) Smit
Received: 21 April 2016 / Revised: 15 June 2016 / Accepted: 7 July 2016 / Published: 15 July 2016
(This article belongs to the Special Issue Decentralized Management of Energy Streams in Smart Grids)
View Full-Text   |   Download PDF [779 KB, uploaded 15 July 2016]   |  

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. View Full-Text
Keywords: commercial and industrial areas; demand response; genetic algorithm; microgrids; mixed-integer optimization; physical system modeling; local RES integration; smart grid; thermostatic load modeling commercial and industrial areas; demand response; genetic algorithm; microgrids; mixed-integer optimization; physical system modeling; local RES integration; smart grid; thermostatic load modeling
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Morales González, R.; Shariat Torbaghan, S.; Gibescu, M.; Cobben, S. Harnessing the Flexibility of Thermostatic Loads in Microgrids with Solar Power Generation. Energies 2016, 9, 547.

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