源-荷-储协同优化的配电网平衡能力提升方法

A Method for Improving the Balance Ability of Distribution Networks Through Source-load-storage Collaborative Optimization

在新型电力系统建设背景下,针对分布式新能源和柔性负荷大量接入导致配电网供需情况日益复杂、功率和能量平衡愈加困难等问题,该文提出一种源-荷-储协同优化的配电网平衡能力提升方法,从经济性与用能低碳性等方面提高配电网运行水平。首先,建立多场景下的源荷不确定性模型,在有效量化配电网源荷不确定性的基础上提取典型运行场景;其次,提出一种基于信息熵理论的源荷功率时序平衡度量化评估方法;最后,建立源-荷-储协同的两阶段优化模型,第1阶段通过事件驱动机制深入挖掘用户参与需求响应的潜力,第2阶段在储能系统建模时引入平衡度约束以促进新能源消纳,进而使配电网的平衡能力得到最大程度提升。通过浙江某地区电网实例验证所提方法对提升配电网平衡能力和新能源消纳能力的科学性和实用性。

In the context of constructing a new-type power system,this paper proposes a method for improving the balance capacity of the distribution network through source load storage collaborative optimization,addressing the increasingly complex supply and demand situation of the distribution network caused by the large number of connected distributed new energy and flexible loads,and the increasingly difficult balance of power and energy.This method improves the operation level of the distribution network from the aspects of economy and low-carbon energy utilization.First,the deterministic system of source load uncertainty in multiple scenarios is established,and typical operation scenarios are extracted based on effectively quantifying the source load uncertainty of distribution network.Then,a quantitative evaluation method for the temporal balance of source load power based on information entropy theory is proposed.Finally,a two-stage optimization model for source load storage collaboration is established.In the first stage,the potential of user participation in demand response is deeply explored through event-driven mechanisms.In the second stage,balance constraints are introduced in the modeling of the energy storage system to promote new energy consumption,thereby maximizing the balance energy of the distribution network.The scientificity and practicality of the proposed method in improving the balance capacity and new energy consumption capacity of the distribution network are verified through an example of a power grid in a certain region of Zhejiang province.