Energy collaborative optimization strategy for virtual power plants participating in green electricity trading and peak regulation ancillary services

With the growing proliferation of distributed energy resources (DERs) in power systems, virtual power plants (VPPs) face the challenge of underutilizing their flexible regulatory potential when participating in a single electricity service market. This paper proposes an energy collaborative optimization strategy for VPP to simultaneously engage in green electricity trading and peak regulation ancillary services for improving the utilization efficiency of DERs and increasing economic benefits. Firstly, fuzzy chance constrained models with specified confidence levels are established to quantify the adjustable capacities of various DERs, including distributed photovoltaics (PVs), energy storage, electric vehicle charging stations, and thermostatically controlled loads. Subsequently, since the adjustable capacities of different DERs can be represented as high-dimensional adjustable polytopes, a Minkowski sum-based method is developed to aggregate these polytopes, thereby deriving the time-coupled aggregated adjustable capacity of the VPP. To alleviate the computational burden associated with time-coupling constraints, a maximum inscribed polyhedron approach is further employed to approximate the aggregated capacity region in a temporally decoupled manner. On this basis, an energy joint declaration model is formulated for VPPs to participate in green electricity trading and peak-shaving ancillary services. Case studies demonstrate that the proposed strategy enhances the average economic benefit of the VPP by 12.792% through multi-service participation, while effectively leveraging the flexibility of DERs to delay future grid expansion investments and support sustainable integration of DERs.