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HVAC systems, as the largest commercial and household power consumers, play an important role in demand response programs. In this research, using weather forecast data and using electricity price forecast, optimal scheduling for HVAC systems set points is performed in such a way that the user’s electricity consumption cost is minimized. Electricity price forecasting is achieved using an ARIMA model. A Markov’s chain has been employed for temperature forecasting. As these forecasts are not deterministic, the risk must be considered. To accomplish this, several scenarios were considered and CVaR was utilized to handle the risk aspect. In this research, the above-mentioned data was employed, and GWO algorithm was invoked to schedule the optimal set points of the HVAC systems and symmetrical dead band width. The results show that this planning method can have an important effect on the participation of the HVAC systems in demand response programs. Also, it has been shown that the dead-band width management plays an important role in energy consumption management. Different users with different attitudes toward the risk were analyzed; the results show that risk-averse users are more involved in demand response programs; and hence, they bear lower energy costs.

Keywords: Risk management, HVAC systems, Optimized scheduling, GWO algorithm, Dead band

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