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In this paper, we investigate the problem of minimizing the long-term totalcost (i.e., the sum of energy cost and thermal discomfort cost) associated witha Heating, Ventilation, and Air Conditioning (HVAC) system of a multizonecommercial building under smart grid environment. To be specific, we firstformulate a stochastic program to minimize the time average expected total costwith the consideration of uncertainties in electricity price, outdoortemperature, the most comfortable temperature level, and external thermaldisturbance. Due to the existence of temporally and spatially coupledconstraints as well as unknown information about the future system parameters,it is very challenging to solve the formulated problem. To this end, we proposea realtime HVAC control algorithm based on the framework of Lyapunovoptimization techniques without the need to predict any system parameters andknow their stochastic information. The key idea of the proposed algorithm is toconstruct and stabilize virtual queues associated with indoor temperatures ofall zones. Moreover, we provide a distributed implementation of the proposedrealtime algorithm with the aim of protecting user privacy and enhancingalgorithmic scalability. Extensive simulation results based on real-worldtraces show that the proposed algorithm could reduce energy cost effectivelywith small sacrifice in thermal comfort.

Distributed Real-Time HVAC Control for Cost-Efficient Commercial Buildings under Smart Grid Environment