Smart Agent-Based Direct Load Control of Air Conditioner Populations in Demand Side Management

Yazdkhasti, Pegah; Diduch, Chris P.

doi:10.1109/iccad57653.2023.10152358

Cited by 1 publication

(2 citation statements)

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“…Figure 1: A block diagram for direct load control system [1] In Figure 1, the power consumption of the load population (Aggregated Load) is input into the load aggregator at the current time denoted by t 0 . Historical data from prior measurements (P(t 0 −k) for times preceding t 0 ) is utilized by the 'Learning Model' to establish a mathematical model for the system (H a and H s ).…”

Section: System Architecturementioning

confidence: 99%

“…This paper builds upon the research initially introduced at ICCAD-2023 [1], where the authors presented a smart agent to make a mathematical model for a population of air conditioners and forecast their power consumption and control it. This paper expands the mathematical formulation for modeling and provides justification for the linear approximation.…”

Section: Introductionmentioning

confidence: 99%

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Smart Agent-Based Direct Load Control of Air Conditioner Populations in Demand Side Management

Yazdkhasti,

Cárdenas–Barrera,

Diduch

2024

Adv. sci. technol. eng. syst. j.

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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.

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Section: System Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%