Harnessing the Flexibility of Thermostatic Loads in Microgrids with Solar Power Generation
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 renewabl...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2016-07-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1996-1073/9/7/547 |
| Summary: | 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. |
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| ISSN: | 1996-1073 |