【目的】在虚拟电厂经济调度领域，当前以集中式算法为主流，忽视了其分布式结构形式，导致系统过度依赖中心节点，处理分布式能源资源效率低下。同时，常规分布式优化算法运行复杂、速度缓慢，难以及时响应电力系统的动态变化，严重制约虚拟电厂的高效运行。并且由于分布式机组的增加导致产生大量的信息交流，严重浪费通信资源。针对此问题，本文提出了一种符合虚拟电厂分布式结构的优化调度策略。【方法】首先，提出了一种基于分布式神经动力学算法的经济调度策略，旨在解决虚拟电厂中的快速一致性问题。其次，设计了一种动态分布式事件触发机制，节约了通信资源。最后，基于Matlab仿真软件进行建模，通过分析一致性快速收敛情况，对比周期性事件触发机制证明经济调度的有效性和动态分布式事件触发机制优越性。【结果】仿真结果表明本文所提出的优化算法能有效、快速地实现增量成本一致性，而且该触发机制与传统机制对比有明显优势，有效节约了资源。【结论】本文针对动态分布式事件触发机制下的虚拟电厂经济调度，提出了一种基于分布式神经动力学的虚拟电厂经济调度优化算法，有效解决了集中式优化算法引发的信息拥堵难题。通过设计适配分布式结构的动态分布式事件触发机制，节约了通信资源。

[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.