A Two-Stage Optimization Model of Capacity Allocation and Regulation Operation for Virtual Power Plant

Abstract

Capacity allocation and optimal scheduling of virtual power plants (VPP) are important aspects to ensure the effectiveness of system investment and operational economy. In this study, a two-stage optimization model for capacity planning and regulation operation of VPPs considering source-load-storage resources is constructed. In the first stage, a capacity optimization model is constructed for the VPP with the lowest annual economic cost under the refinement constraints of source-storage resources. In the second stage, based on the capacity allocation results and load characteristics, a source-load interactive operation optimization model with the lowest typical daily operating cost and incentive-based demand response is constructed under the resource capacity constraint, so as to realize the capacity allocation and energy control of the VPPs in all stages of source-load-storage resources. Finally, a planning solver is applied to solve the algorithm. The proposed model is validated. The results show that the presence or absence of demand response, the form of demand response, and the charge state of energy storage all have an impact on the allocation and operation results. Adequate consideration of the source-side, load-side, and storage-side interactions can provide a reference for more accurate planning and optimization. The research results are intended to be able to provide VPPs investors and operators with a full process of construction and operation solutions.