[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.