摘要
面对复杂多样的负荷时空增长模式,为提升配电网规划方案的经济性和可行性,提出一种基于地理信息系统的配电网动态规划方法。该方法基于密度峰值聚类的等效负荷点位置信息,采用布雷森汉姆-粒子群优化算法驱动的线路规划方法形成待规划线路集合,并运用混合整数二阶锥规划模型结合动态规划的方法对配电网规划这一非凸非线性问题进行求解。所提方法建立的聚类分区下的负荷时空分布模型简化了问题规模,并以最小化线路综合成本为目标,囊括全寿命周期内线路建设成本、运维成本及损耗成本。同时,动态规划方法分阶段制定规划策略解决了阶段性网架增长带来的结构性调整,契合实际应用需求。最后,以某城市实际规划区域为算例验证了所提方法有利于降低网架规划建设成本、运维成本以及提高规划可行性。
As for the complex and diverse spatio-temporal load growth patterns, this paper proposes a dynamic planning method for distribution network based on geographic information system so as to improve the economics and feasibility of distribution network planning. Based on the equivalent location information of load point clustering by fast search and find of density peak, the method uses a line planning method driven by the Bresenham-particle swarm optimization algorithm to form the set of lines to be planned,and uses a mixed integer second-order cone planning model combined with a dynamic planning approach to solve the nonconvex nonlinear problem of distribution network planning. The proposed method simplifies the problem size by modeling the spatiotemporal distribution of load under clustered partitioning, and aims to minimize the comprehensive line cost, including line construction cost, operation and maintenance cost, and loss cost over the whole life cycle. At the same time, the dynamic planning method formulates the planning strategy in phases to solve the structural adjustment brought by the phased network growth, which is suitable for the practical application requirements. Finally, the actual planning area of a city is used as an example to verify that the proposed method can help reduce the construction cost, operation and maintenance cost and improve the planning feasibility.
作者
李争博
刘友波
任鹏哲
向月
李秋燕
祁浩南
LI Zhegnbo;LIU Youbo;REN Pengzhe;XIANG Yue;LI Qiuyan;QI Haonan(College of Electrical Engineering,Sichuan University,Chengdu 610065,China;Economic Research Institute of State Grid Henan Electric Power Co.,Ltd.,Zhengzhou 450000,China)
出处
《电力系统自动化》
EI
CSCD
北大核心
2022年第14期38-45,共8页
Automation of Electric Power Systems
基金
国家自然科学基金资助项目(51977133)。
