计及不确定性的含分布式发电并网的配电网故障区段定位方法

主动配电网作为分布式发电的高效集成和管理平台,其故障区段定位是一门重要课题。针对含分布式发电并网的主动配电网故障区段定位问题,计及分布式发电出力的不确定性,基于随机机会约束规划建立配电网故障区段定位模型。所建立的模型以概率形式的评价函数为目标函数,计及控制变量定义域约束,故障类型限值约束等约束条件构建。通过对基本帝国竞争算法进行改进得到二进制帝国竞争算法,并采用改进帝国竞争算法设计模型求解流程。仿真算例表明,所建立的模型适用于含分布式发电并网的主动配电网故障区段定位求解,并计及分布式发电出力不确定性对定位结果的影响,改进帝国竞争算法在模型的求解中性能较优。

Fault section location method for distribution networks with distributed generation andgrid connection considering uncertainty

With the development of energy storage application technology in recent years, the installed scale of energy storage has experienced an explosive growth in China''s power market. The accurate quantification of commercial value of energy storage projects will help energy storage products or services to participate in the power market competition and promote the sustainable development of energy storage industry. Based on the theory of scope economy and cost benefit, a stochastic model of total factor discharging electricity price is As an efficient integration and management platform for distributed generation, fault section location on an active distribution network is an important issue. Considering the uncertainty of distributed generation output, a fault location model of a distribution network is established based on stochastic chance-constrained programming. The objective function of the model is the evaluation function in the form of probability. This takes into account the constraints of control variable definition domain and fault type limit. A basic imperial competition algorithm is improved to get the binary imperial competition algorithm, and this algorithm is used to design the model problem solving process. The simulation results show that the proposed model is suitable for fault location on an active distribution network with distributed generation and that is grid-connected. The effect of the uncertainty of distributed generation output on location results is taken into account. The improved imperial competition algorithm has better performance in solving the model problem.This work is supported by National Natural Science Foundation of China (No. 61873225), National Key Research Development Program of Hebei Province (No. 20314301D) and Project of State Grid Hebei Electric Power Company Project (No. kj2019-068).