[Objective] In the field of economic scheduling of virtual power plants, centralized algorithms are currently dominant, neglecting their distributed structural forms, leading to an over-reliance on central nodes and inefficient handling of distributed energy resources. At the same time, conventional distributed optimization algorithms are complex and slow in operation, makeing it difficult to respond to the dynamic changes in the power system in a timely manner, which seriously restricts the efficient operation of virtual power plants. And the increase in distributed units leads to the generation of a large number of information exchanges, which is a serious waste of communication resources. Aiming at this problem, this paper proposes an optimal scheduling strategy that conforms to the distributed structure of virtual power plant. [Methods] Firstly, an economic scheduling strategy based on distributed neural dynamics algorithm was proposed, aiming to solve the fast consistency problem in virtual power plants. Secondly, a dynamic distributed event-triggered mechanism was designed to save communication resources. Finally, modelling was performed based on Matlab simulation software. The effectiveness of economic scheduling and the superiority of dynamic distributed event-triggered mechanism were proved by analysing the consistency of fast convergence and comparing the periodic event-triggered mechanism. [Results] Simulation results showed that the optimization algorithm proposed in this paper can effectively and quickly achieve incremental cost consistency, and the triggered mechanism has obvious advantages over the traditional mechanisms, effectively saving resources. [Conclusion] This paper focuses on the economic scheduling of virtual power plants under the dynamic distributed event-triggered mechanism. An economic scheduling optimization algorithm of virtual power plant based on distributed neural dynamics was proposed, which effectively solved the problem of information congestion caused by centralized optimization algorithm. By designing a dynamic distributed event-triggered mechanism that adapts to the distributed structure, the communication resource is saved.