摘要
提出一种同时优化机组启动、系统分区与网架恢复时间的多目标输电网架重构模型,引入了带精英策略的快速非支配排序遗传算法(NSGA-Ⅱ),以避免求解时的目标偏好性。设计了基于优先级的基因编码,以快速非支配排序、个体拥挤距离构成虚拟适应度协调各目标函数的关系,实现了带分区优化与计及热启动时间约束的网架重构算法,采用精英个体校验策略校验和完善了Pareto最优方案。IEEE 30节点算例比较结果表明,该算法在目标空间上分布均匀,收敛性好,计算复杂度显著降低。山东电网实例仿真结果进一步验证了算法的有效性。
A multi-objective optimization model is proposed for power system reconstruction. Objective functions of the model are generating units＇ starting-up sequence, system-partitioning strategy and time requirements for system reconstruction. Fast and elitist non-dominated sorting genetic algorithm （NSGA-Ⅱ） is applied in order to avoid artificially balanced solutions. Priority-based genetic encoding and decoding are designed for NSGA-Ⅱ . With non-dominated sorting and crowding-distance calculation the means of dummy fitness are used to find optimal solutions in different objectives. The system-partitioning and time requirements for unit start-up are considered in power system reconstruction algorithms. An elitist checking strategy is adopted to check Pareto-optimal solutions. Simulation results on IEEE 30-bus system show that the proposed algorithms are able to find better spread of solutions, better convergence and lower computational complexity compared to other power system reconstruction methods based on genetic algorithms. The effectiveness of the proposed algorithms is further validated by the numerical results on the power system of Shandong Province, China.
出处
《电力系统自动化》
EI
CSCD
北大核心
2009年第23期14-18,共5页
Automation of Electric Power Systems
基金
国家自然科学基金资助项目(50877044)
山东省优秀中青年科学家科研奖励基金资助项目(2008BS01006)~~
关键词
电力系统恢复
多目标优化
输电网架重构
遗传算法
power system restoration
multi-objective optimization
power system reconstruction
genetic algorithm
