Optimal price based control of HVAC systems in multizone office buildings for demand response

Abstract Optimizing the scheduling of heating, ventilation, and air-conditioning (HVAC) systems in multizone buildings is a challenging task, as occupants in various zones have different thermal preferences dependent on time-varying indoor and outdoor environmental conditions and price signals. Price-based demand response (PBDR) is a powerful technique that can be used to handle the aggregated peak demand, energy consumption, and cost by controlling HVAC thermostat settings based on time-varying price signals. This paper proposes an intelligent and new PBDR control strategy for multizone office buildings fed from renewable energy sources (RESs) and/or utility grid to optimize the HVAC operation considering the varying thermal preferences of occupants in various zones as a response of real-time pricing (RTP) signals. A detailed mathematical model of a commercial building is presented to evaluate the thermal response of a multizone office building to the operation of an HVAC system. The developed thermal model considers all architectural and geographical effects to provide an accurate calculation of the HVAC load demand for analyses. Further, Occupants’ varying thermal preferences represented as a coefficient of a bidding price (chosen by the occupants) in response to price signals are modeled using an artificial neural network (ANN) and integrated into the optimal HVAC scheduling. Furthermore, a control mechanism is developed to determine the varying HVAC thermostat settings in various zones based on the ANN prediction model results. The effect of the proposed strategy on aggregator utility with wider implementation of the developed mechanism is also considered. The optimization problem for the proposed PBDR control strategy is formulated using a building’s thermal model and an occupant’s thermal preferences model, and simulation results are obtained using MATLAB/Simulink tool. The results indicate that the proposed strategy with realistic parameter settings shows a reduction in peak demand varying from 7.19% to 26.8%, contingent on the occupant’s comfort preferences in the coefficient of the bidding price compared to conventional control. This shows that the proposed approach successfully optimizes the HVAC operation in a multizone office building while maintaining the preferred thermal conditions in various zones. Moreover, this technique can help in balancing the energy supply and demand due to the stochastic nature of RESs by cutting electricity consumption.