Abstract
Abstract This paper establishes a transactive coordination model of heating–ventilation–air-conditioning (HVAC) loads for demand response purpose. The thermal inertia of industrial thermostatically controlled loads may provide verities of ancillary service in the power system by adjusting their temperature set-points. The HVAC aggregator bids temperature set-points to minimize opportunity cost subject to crucial operational constraints. The cost due to changing temperature set-point from reference is captured by a piece-wise linear function. A dual decomposition based distributed optimization framework is used to optimize the coolant flow rate in the facility for a given temperature set-point. By optimizing temperature set-point and coolant flow, the HVAC system can modify the electrical power consumption at peak hours. The effectiveness of the model is verified by simulating several plausible cases. It is found that overall energy consumption cost significantly reduces; drop the electricity prices while having little effect on the operation of the cold-storage plants.
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