基于系统生存性的骨干网架搜索方法  

A method of searching backbone grid based on survivability of power system

作　　者：林潇 刘洋[1] 许立雄[1] 马晨霄 朱嘉远 Lin Xiao;Liu Yang;Xu Lixiong;Ma Chenxiao;Zhu Jiayuan(School of Electrical Engineering and Information,Sichuan University,Chengdu 610065,China)

机构地区：[1]四川大学电气信息学院

出　　处：《电测与仪表》2019年第12期49-56,共8页Electrical Measurement & Instrumentation

摘　　要：构建骨干网架,是差异化规划电网以提高其抵抗自然灾害能力的关键手段。基于系统生存性理论并考虑风机出力不确定性的影响,提出一种可以平衡以线路与节点数量之和为代表的经济性和系统综合生存能力的骨干网架构建方法。文章采用改进的生物地理学优化算法搜索骨干网架,通过改进算法迁移模型,引入随机扰动算子和基于最小生成树理论的连通性修复策略以提高算法收敛速度和精度,并与传统的生物地理学算法、遗传算法、二进制粒子群算法对比分析。算例结果表明,该方法能够快速搜索出综合经济性和生存性最优且满足连通性约束及系统安全运行条件的骨干网架,且最优骨干网架会限制风电出力比例以提高系统生存性。Identifying backbone grid has great significance to the differential planning of power grid, which can improve the ability to withstand natural disasters effectively. Based on the theory of system survivability, this paper presents an approach to construct backbone grid by taking the uncertainty of wind power into account. The approach can balance the survivability and cost, which represented by the sum of lines and busses. The biogeography-based optimization algorithm (BBO) is applied to search for backbone grid. In order to improve the convergence rate and precision, modified migration model, random perturbation operator, clear duplicate operator and repairing strategy which is based on the theory of the minimum spanning tree are employed in the algorithm. Compared to the traditional BBO, genetic algorithm (GA) and binary particle swarm optimization (BPSO), the improved algorithm can figure out backbone grid effectively, which has optimal indices of survivability and cost. Meanwhile, the optimal case satisfies constraint conditions of power grid safe operation and connectivity. The backbone can weaken the influence of wind power output uncertainty to a certain extent and improve the survivability of system.

关 键 词：骨干网架 电网生存性 风电不确定性 改进生物地理学优化算法 连通性 

分 类 号：TM761[电气工程—电力系统及自动化]