Abstract—Air conditioners, as the largest commercial and domestic consumers, play an important role in demand response programs. It should be noted that unplanned participation of these loads in demand response programs can lead to a new peak in the power grid; this effect is called the rebound phenomenon. In this study, using weather forecasting data, and using electricity load profile prediction, the optimal 24-hour scheduling of the thermostat set-point of the air conditioners has been implemented in a way that HVAC systems contribute to demand response programs and no rebound phenomenon could be observed due to proper scheduling. The weather forecast has been obtained through a Markov chain. Since this forecast has uncertainties, various scenarios have been developed to manage the risk of these uncertainties. The electricity load profile is forecasted by using GMDH Shell software load forecasting tool. The GWO met heuristic algorithm is used to solve the optimization problem. The effectiveness of the above method has been compared with the direct load control method and the uncontrolled method, and the results show that this scheduling method can have a significant effect on the air conditioning systems' participation in demand response programs. Also using this method, the rebound coefficient is measured 3.5% which is very desirable.
