Performance optimization of VPP in fast frequency control ancillary service provision

This paper proposes a computation offloading method for performance optimization in the fast frequency control ancillary service (FFCAS) provision of a virtual power plant (VPP). VPP aggregates massive demand-side resources to provide power systems with the FFCAS. It faces excessive communication and computation tasks. Edge computing addresses these issues through computation offloading. Heavy tasks can be partially offloaded to the edge to relieve the burden of the central server. Existing offloading methods, however, are not specific for the VPP. To fill the gap, first, an age of information (AoI) model is introduced to characterize the data flow of an edge-enabled VPP. Next, an AoI-based FFCAS performance evaluation model is proposed considering the impacts of communication delay, communication failures, and computational delay. Then an FFCAS performance optimization model is formulated. It aims to maximize VPP’s profit through communication offloading and the DSR portfolio determination. Simulation results show that the proposed method can efficiently improve VPP’s profit. •Develop an age of information (AoI) model to characterize the data flow of an edge-enabled VPP. AoI has been widely applied in the communication field recently, but few works have applied AoI to VPP.•Propose an AoI-based FFCAS performance evaluation model considering the impacts of communication delay, communication failures, and computational delay.•Formulate an FFCAS performance optimization model. It aims to maximize VPP’s profit through communication offloading and the DSR portfolio determination.•Diverse scenarios are considered in case studies, including variations in offloading ratios, cost coefficients of edge computing resources, computing power, etc. Results demonstrated that edge computing could improve VPP’s profit efficiently.•The benefits of edge computing become small when the related operating cost is too high or the computing power of the VPP cloud control center is strong enough. The proposed method can serve as a tool for the VPP to determine the implementation of edge computing whether or not.