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A novel PSO (Particle Swarm Optimization)-based approach for optimal schedule of refrigerators using experimental models

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  • Farzamkia, Saleh
  • Ranjbar, Hossein
  • Hatami, Alireza
  • Iman-Eini, Hossein

Abstract

Refrigerators have considerable share of residential consumption. They can be, however, flexible loads because their operating time and consumption patterns can be changed to some extent. Accordingly, they can be selected as a target for the study of Demand Side Management plans. In this paper, two experimental models for a refrigerator are derived. In obtaining the first model, following assumptions are made: the ambient temperature of refrigerator is assumed to be constant and the refrigerator door is remained closed. However, in the second model the variation of ambient temperature and door-opening effects are considered according to some general patterns. Further, two strategies are proposed to reduce the annual electricity cost and electric power consumption at peak-load times. These strategies together with the aforementioned models form an optimization problem which is, then, solved by Particle Swarm Optimization algorithm. Simulation results indicate a reduction of more than 28.61% in the annual cost. Also, the annual electricity consumption has decreased more than 20.46% and load shifting from the peak periods has achieved about 40%. In addition, these approaches are implemented in laboratory and their performance is confirmed by experimental results.

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  • Farzamkia, Saleh & Ranjbar, Hossein & Hatami, Alireza & Iman-Eini, Hossein, 2016. "A novel PSO (Particle Swarm Optimization)-based approach for optimal schedule of refrigerators using experimental models," Energy, Elsevier, vol. 107(C), pages 707-715.
    • Handle: RePEc:eee:energy:v:107:y:2016:i:c:p:707-715
    DOI: 10.1016/j.energy.2016.04.069
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    References listed on IDEAS

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    3. Maytham S. Ahmed & Azah Mohamed & Raad Z. Homod & Hussain Shareef, 2016. "Hybrid LSA-ANN Based Home Energy Management Scheduling Controller for Residential Demand Response Strategy," Energies, MDPI, vol. 9(9), pages 1-20, September.

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