基于温控负荷变化特性的冷负荷启动参数量化及应用研究

通常，在发生大停电事故后，由于负荷多样性的丧失，温控负荷在恢复供电的短时间内会出现负荷需求量激增，这会导致负荷恢复缓慢甚至造成二次停电，该现象称为冷负荷启动。因此，准确提取温控负荷并对冷负荷启动参数进行量化分析具有一定的实际意义。首先，利用负荷分解得到的春季和秋季的非温控负荷作为全年的基准负荷，再从夏季负荷中提取出温控负荷。该方法相较于现有的温控负荷提取方法，提取结果更加准确。另外，为了探究负荷与温度的深层次关系，除了分解负荷外，还对温度进行了分解。其次，根据提取出的温控负荷，采用蒙特卡洛抽样法，以空调为例分析了停电时间和环境温度对冷负荷启动的影响。最后，基于PJM电力市场的负荷温度数据和IEEE33节点系统算例，验证了所提温控负荷提取方法和对冷负荷启动参数量化分析的有效性和准确性。

Quantification and application of cold load pick-up parameters based on temperature-controlled load variation characteristics

Generally, after a power outage, because of the loss of load diversity, the temperature-controlled load will experience a surge in load demand within a short period of time when the power supply is restored. This will lead to slow load recovery and even secondary power outages. This kind of phenomenon is called cold load pick-up. Therefore, it has certain practical significance to accurately extract the temperature-controlled load and analyze the influencing factors of the cold load pick-up. First, in this paper, the non-temperature-controlled loads in spring and autumn obtained by load decomposition are used as the reference load for the whole year. Then we strip the temperature-controlled load from the summer load. Compared with the existing methods, the method in this paper has more accurate extraction results. At the same time, to explore the deep-level relationship between load and temperature, in addition to decomposing load, this paper also decomposes temperature. According to the extracted temperature-controlled load, we use the Monte Carlo sampling method, taking an air conditioner as an example, to analyze the influence of the power outage time and ambient temperature on the cold load pick-up. Finally, through the load and temperature data of the PJM power market and the IEEE33 node system, the validity and accuracy of the extraction method of temperature-controlled load and the analysis of the influencing factors of cold load pick-up proposed in this paper are verified. This work is supported by the National Natural Science Foundation of China (No. 51777058).