Risk-Averse Optimal Scheduling of Virtual Power Plants Based on the Cloud-Edge Collaborative Framework

Virtual power plant (VPP) has been introduced into the distribution network to manage the renewable distributed generation (RDG) and energy storage system. The inherent uncertainty of RDG incurs the power balance problem and puts the income of VPP at risk. A distributed stochastic programming model is proposed to optimize the scheduling of VPPs. On the one hand, the conditional value-at-risk is selected as the objective function to minimize the risk of high operation cost. On the other hand, the power sharing among VPPs is implemented based on the cloud-edge collaborative framework where the distribution system operator (DSO) collects the bidding information from VPPs and determines the practical power for them. This framework not only minimizes the cost of VPPs but also protects their privacy. The model is based on mixed integer linear programming and solved by GUROBI. Finally, the case study demonstrates the effectiveness of the model.