Trusted Quantification of Robust Scheduling Characteristics and Coordinated Scheduling Methods for Virtual Power Plants

ObjectivesAggregating distributed resources within the regional grid based on virtual power plant (VPP) technology can effectively improve system flexibility with low marginal cost. However, data barriers arising from factors such as information security, the computational efficiency of distributed coordination, and the uncertainty of regulation resources pose difficulties for VPP auxiliary service decisions. Accordingly, this paper analyzes the external characteristics of VPP as a whole, including interconnection power, reserve capacity, and operating cost. It proposes a trusted quantification method for robust scheduling characteristics of VPP and constructs a collaborative scheduling model for multiple VPPs.MethodsFirst, considering the uncertainty factors in distributed power and demand response, the source-load reserve potential under the network constraint is analyzed to establish a VPP mathematical model. Second, combining robust optimization and multi-parameter programming theory, the VPP interconnection power regulation range, flexible reserve capacity, and optimal cost robust feasible domain trusted quantification are realized. An affine relationship is established between the internal resource regulation strategy of the VPP and the external transaction results, thereby completing the VPP equivalence aggregation. Further, a cooperative game model is constructed for the effective interaction between multiple VPPs and the grid. Finally, the effectiveness of the model and method is verified by three test cases.ResultsThe quantified encapsulation model participates in scheduling, has higher computing efficiency, and also protects the privacy of internal information within the VPP. By designing parallel programs, the computational efficiency of the encapsulation model quantification process is further improved.ConclusionsThe quantified encapsulation model participates in scheduling, has higher computing efficiency, and also protects the privacy of internal information within the VPP. By designing parallel programs, the computational efficiency of the encapsulation model quantification process is further improved.