## 1 Introduction  
Insufficient peak load regulating capacity of the power grid leads to low utilization of renewable energy. The reason is that the diversity of the power supply is insufficient in some areas, so an amount of renewable energy cannot be consumed efficiently. The demand-side load has strong scheduling potential due to its very large quantity. Therefore, the problem of renewable energy consumption can be solved if the demand-side loads consume renewable energy actively. On the one hand, air conditioning loads, electric water heaters, and other thermostatically-controlled loads have great potential for scheduling [1; 2; 3; 4], and they can temporarily store electrical energy in the form of thermal energy [5; 6; 7; 8; 9; 10]. On the other hand, for the users, fine-tuning the working time and temperature, the thermal control loads have little impact on user comfort; on the power supply side, they can quickly respond to power dispatching [11]. Recently, there have been a large amount of studies on renewable energy consumption by thermostatically-controlled loads [12; 13; 14; 15; 16; 17]. [PERSON] et al. proposed a tracking method that does not need to provide status information of thermostatically-controlled loads to the central control system in real-time, but it is unsuitable for high-precision tracking [8]. In addition, demand-side loads can also provide a variety of ancillary services for the power supply side [18]. A two-level optimization dispatching model has been established in Gao [19]. This paper aims to maximize the benefits of ancillary services and minimize the dispatching costs. Before the demand-side loads are used for renewable energy consumption or ancillary services, they need to be aggregated firstly. At present, there are two main kinds of control methods for aggregating loads. One is switch control, which changes the power consumption by controllingthe switching of the aggregate load. The other is temperature control, i.e., changing the power consumption by the adjustment of temperature settings. [PERSON] has developed a state-queuing model of thermostatically-controlled loads (TCLs) [20]. Based on the state-queuing model, it controls the aggregate loads by switching TCLs. An improved state-queuing model is proposed in [21], enhancing the stability of the model. Switch control is easy to operate and has great potential to participate in power dispatching. However, the method has many drawbacks, notably its load fluctuation. The different temperature settings will cause the load fluctuation. In a steady state, a small variation of the temperature settings will cause load state diversity loss and cause a significant load fluctuation. Additionally, switch control has a large influence on the users' comfort. Thus, the stability of this control method needs to be improved and the impact on the users should be taken into account as an important factor. [PERSON] proposed an approach that adjusts the air conditioning loads' temperature settings in real-time to change the load with an application to wind energy. It realizes renewable energy consumption and tracking ancillary services in a new way [22]. Based on [22], [PERSON] established a bilinear differential equation model for the aggregate loads and designed a sliding mode controller to realize wind power tracking based on the existing load model [23]. This control method is highly accurate and has little effect on user comfort. However, it needs to obtain information extracted from the states of loads and constantly adjust the deviation. This is a closed-loop control and the method is too complicated [21]. This paper develops a control algorithm based on state-queuing model of aggregate air conditioning loads on the power grid, with the goal of consuming renewable energy and tracking renewable energy output. We establish a virtual load curves set based on the state-queuing model.