Bi-level decentralized control of electric heating loads considering wind power accommodation in real-time electricity market

Flexible resources on demand-side, such as electric heating loads (EHLs), are perceived as an effective way of supporting the power balance of power grids with large scale renewable energy integration. However, it is difficult to quickly, effectively and evenly aggregate and schedule a large population of these loads because of their heterogeneity and geographic dispersion. The efficiency and effectiveness of optimal control are important for achieving the power balance between power source and load, especially in real-time scheduling. Moreover, the even load allocation between these heterogeneous loads also needs to motivate users participation in realtime power balance. To follow wind power in the real-time electricity market, this study presents a bi-level control method for EHLs based on distributed optimization computing, which considers the bi-level queuing method, different load control modes and multi-time scales between different control levels. First, each load group, which includes a large population of EHLs is controlled by a distributed server (DS); all the load groups are scheduled by the central management server (CMS), which deals with the wind farm or electricity retailer. Next, a distributed optimization model is built based on the bi-level control structure, in which the different power balance statuses and multi-time scales between the electricity market and EHLs are considered. Furthermore, considering the power balance on the multi-time scales, layer-by-layer load allocation optimization through the bi-level time-varying temperature queuing method, and the load-leveling optimization, serve as the sequential optimization problems. Finally, a simulation including 12,500 EHLs based on measured data in northern China is carried out. The results show that the proposed approach can effectively realize real-time power balance between wind power and EHLs, while maintaining uniform load allocation between the EHLs.