Robust Optimization Scheduling Strategy for Electrothermal Cogeneration Virtual Power Plant Considering Auxiliary Services

In the Northeast region of China, where the proportion of renewable energy is high and the climate is cold, there is a high demand for renewable energy integration and heating stability. Aggregating electric-thermal flexibility resources in the form of virtual power plants and participating in the ancillary service market can help address these challenges. This paper first establishes an operational benefit model for virtual power plants in cold regions based on the characteristics of their resource endowments. The model considers the compensation mechanism in the Northeast's ancillary service market. Next, considering the synergistic interaction between peak regulation in the ancillary service market and virtual power plants in cold regions, the model comprehensively considers the scheduling potential of various types of flexible adjustable devices, the demand response capability of flexible electric-heat loads, and the peak shaving and valley filling effect of energy storage devices. A two-stage robust optimization scheduling model is constructed for electric-heat integrated virtual power plants to obtain the economically optimal dispatch plan under the worst-case scenario. A robust coefficient is introduced to flexibly adjust the conservatism of the optimized dispatch plan. The C&CG algorithm and strong duality theory are used for solving. Finally, a case study is conducted using the measured data from a continuous 7-day period in December 2022 in a specific area of Jilin Province. The results demonstrate that the coordinated operation of electric-heat integrated virtual power plants can effectively reduce the peak-valley difference in the system and bring incremental operational benefits to the system.