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考虑经济性和网架性能的抗灾型骨干网架多目标规划

韩畅 林振智 杨莉 蔡景东 吕云锋 张素明

电力系统自动化2019,Vol.43Issue(2):34-41,104,9.
电力系统自动化2019,Vol.43Issue(2):34-41,104,9.DOI:10.7500/AEPS20180313002

考虑经济性和网架性能的抗灾型骨干网架多目标规划

Multi-objective Planning for Anti-disaster Backbone Grid Considering Economics and Network Frame Performance

韩畅 1林振智 1杨莉 1蔡景东 2吕云锋 2张素明2

作者信息

  • 1. 浙江大学电气工程学院, 浙江省杭州市 310027
  • 2. 广东电网有限责任公司惠州供电局, 广东省惠州市 516001
  • 折叠

摘要

Abstract

In order to enhance the power supply capability and disaster resistance ability of power systems under extreme natural disasters, a multi-objective planning method of anti-disaster backbone grid is proposed with the guarantee rate of the load requirements, security operation constraints of the power system and connectivity of the network topology satisfied.In the proposed strategy, a multi-objective planning model of anti-disaster backbone grid, in which the reinforce cost of differential planning, the efficiency of power resupply after a disaster and the ability of the backbone grid to resist the disaster are considered comprehensively, is constructed for maximizing economics, resilience and network survivability.The graph repair strategy is utilized in comprehensive learning particle swarm optimization algorithm, which increases the feasible solution space of the algorithm.Then, the mixed strategy Nash equilibrium, which can balance the benefit of each objective function, is adopted to extract the best compromise solution with the optimal equilibrium value from the Pareto fronts obtained by the algorithm.The feasibility of the proposed method is verified by the numerical results of a regional power grid in Guangdong Province.

关键词

抗灾型骨干网架/可恢复性/网络抗毁性/全面学习粒子群优化/纳什均衡

Key words

anti-disaster backbone grid/resilience/network survivability/comprehensive learning particle swarm optimization/Nash equilibrium

引用本文复制引用

韩畅,林振智,杨莉,蔡景东,吕云锋,张素明..考虑经济性和网架性能的抗灾型骨干网架多目标规划[J].电力系统自动化,2019,43(2):34-41,104,9.

基金项目

国家重点研发计划资助项目 (2016YFB0900100) (2016YFB0900100)

国家自然科学基金资助项目 (51377005).This work is supported by National Key R&D Program of China (No. 2016YFB0900100) and National Natural Science Foundation of China (No. 51377005). (51377005)

电力系统自动化

OA北大核心CSCDCSTPCD

1000-1026

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