面向海量灵活性资源的两阶段分布式协同调度方法

Two-stage Distributed Collaborative Dispatching Method for Massive Flexible Resources

电动汽车和5G基站作为柔性负荷资源,为电网提供了充足的备用容量,采用一定策略对其进行调控,可参与电网的辅助服务。但在未来海量分布式柔性资源参与的情况下,集中式的优化调度方法会存在隐私泄露和计算负担沉重的缺点。基于此,首先建立5G基站备用电池和电动汽车可调度容量的实时评估模型,利用无向图网络对通信系统和储能元件建模,提出一种基于均衡荷电状态的调度策略,采用分布式共识算法实现了隐私保护和海量资源的调度控制。然后,提出一种先评估功率边界、后进行分布式共识调度的两阶段实时调度流程方案。最后,通过仿真验证了该方案的有效性。

As flexible load resources,electric vehicles and 5th generation(5G)base stations provide abundant reserve capacity for the power grid,which can be regulated by certain strategies and participate in the auxiliary services of the power grid.However,in the case of the participation of massive distributed flexible resources in the future,the centralized optimization dispatching method will have the shortcomings of privacy leakage and heavy computing burden.On this basis,a real-time evaluation model of 5G base station backup battery and electric vehicle dispatchable capacity is first established.Then,an undirected graph network is used to model the communication system and energy storage components.And a dispatching strategy based on balanced state of charge is proposed.The privacy protection and dispatching control of massive resources are realized by adopting the distributed consensus algorithm.Furthermore,a two-stage real-time dispatching process scheme is proposed,which first evaluates the power boundary and then distributes consensus dispatching.Finally,the effectiveness of the scheme is verified by simulation.