With the growth of intermittent renewable energy generation in power grids, there is an increasing demand for controllable resources to be deployed to guarantee power quality and frequency stability. The flexibility of demand response (DR) resources has become a valuable solution to this problem. However, existing research indicates that problems on flexibility prediction of DR resources have not been investigated. This study applied the temporal convolution network (TCN)-combined transformer, a deep learning technique to predict the aggregated flexibility of two types of DR resources, that is, electric vehicles (EVs) and domestic hot water system (DHWS). The prediction uses historical power consumption data of these DR resources and DR signals (DSs) to facilitate prediction. The prediction can generate the size and maintenance time of the aggregated flexibility. The accuracy of the flexibility prediction results was verified through simulations of case studies. The simulation results show that under different maintenance times, the size of the flexibility changed. The proposed DR resource flexibility prediction method demonstrates its application in unlocking the demand-side flexibility to provide a reserve to grids.

随着电网中间歇性可再生能源发电的增长，对可控资源的部署需求不断增加，以保证电能质量和频率稳定性。需求响应 (DR) 资源的灵活性已成为解决此问题的宝贵方法。然而，现有的研究表明，DR资源的灵活性预测问题尚未得到研究。本研究应用时间卷积网络 (TCN) 组合变压器，这是一种深度学习技术，用于预测两种类型的 DR 资源的聚合灵活性，即电动汽车 (EV) 和家用热水系统 (DHWS)。该预测使用这些 DR 资源和 DR 信号 (DS) 的历史功耗数据来促进预测。预测可以生成聚合灵活性的大小和维护时间。通过案例研究的模拟验证了灵活性预测结果的准确性。仿真结果表明，在不同的维修次数下，柔性的大小发生了变化。所提出的 DR 资源灵活性预测方法展示了其在解锁需求侧灵活性以向电网提供储备方面的应用。