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A direct load control strategy of centralized air-conditioning systems for building fast demand response to urgent requests of smart grids
Affiliation:1. Department of Building Services Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong;2. Research Institute for Sustainable Urban Development, The Hong Kong Polytechnic University, Kowloon, Hong Kong;1. Department of Building Services Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong;2. Research Institute for Sustainable Urban Development, The Hong Kong Polytechnic University, Kowloon, Hong Kong;1. Department of Building Services Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong;2. Research Institute for Sustainable Urban Development, The Hong Kong Polytechnic University, Kowloon, Hong Kong
Abstract:When receiving an urgent request from a smart grid, shutting down part of operating chillers directly in the air-conditioning system in a building can achieve immediate power reduction. However, no study has addressed how to determine the number of chillers/pumps to be shut down and how to regulate the load of retained equipment systematically during DR events. This paper presents a new approach to address these issues based on three schemes. A power demand optimization scheme predicts the building cooling demand and the power limiting threshold in response to a received DR request. A system sequence control resetting scheme determines the number of operating chillers/pumps to be retained. An online control/regulation scheme ensures the system power following the expected profile by regulating the total chilled water flow delivered to the building and therefore the chiller load. It also employs a cooling distributor to distribute chilled water to individual zones concerning different sensitivities/sacrifices to temperature increases. Case studies are conducted on a simulated dynamic building air-conditioning system. Results show that, during DR events, the proposed strategy can achieve the expected power reduction (i.e., about 23%) and also maintain acceptable zone temperature even though uncertainties exist in the prediction process.
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