Smart Agent-Based Direct Load Control of Air Conditioner Populations in Demand Side Management
Volume 9, Issue 1, Page No 114-123, 2024
Author’s Name: Pegah Yazdkhastia), Julian Luciano Cárdenas–Barrera, Chris Diduch
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Department of Electrical & Computer Engineering, Smart Grid Research Center, University of New Brunswick, Fredericton, Canada
a)whom correspondence should be addressed. E-mail: pegah.ykh@unb.ca
Adv. Sci. Technol. Eng. Syst. J. 9(1), 114-123 (2024); 
DOI: 10.25046/aj090111
Keywords: Demand-Side Management, Smart Grid, System Identification, Direct Load Control, Load Forecast, Rebound Effect
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The integration of fluctuating renewable resources such as wind and solar into existing power systems poses challenges to grid reliability and the seamless incorporation of these resources. To address the inherent variability in renewable generation, direct load control emerges as a promising method for demand-side management. Thermostatically controlled appliances, like air conditioners, hold a significant role in this approach. However, effective direct load control necessitates accurate load magnitude estimation and the potential for load shifting. In this paper, we introduce a smart-agent architecture that employs a mathematical model to forecast aggregated power consumption behavior, even when changes are introduced by the controller. To assess system performance, a numerical simulator was developed, demonstrating the system’s adaptability to changes, its self-retraining capability, and its continuous improvement in predicting aggregated power consumption.
Received: 10 November 2023, Revised: 12 January 2024, Accepted: 12 January 2024, Published Online: 06 February 2024
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