摘要
机组组合是电力系统日发电计划中主要的优化任务,在满足各种约束条件下求得全局最优解是一个比较困难的问题。传统遗传算法的二进制编码和随机遗传操作不适合于求解大规模机组组合问题。针对电力系统日发电计划的特点,提出了一种混合智能messy遗传算法(HIMGA),该算法实现简单,大大减小了求解问题的规模,保证了群体的多样性,提高了算法的搜索效率,改善了算法的收敛性。仿真计算结果表明了该算法的有效性和实用性。
Unit commitment (UC) is the main optimization task in the daily generation scheduling in power systems. However, UC is also one of the most difficult optimization problems in a power system. It is unsatisfactory when used to solve the large scale UC problem with the conventional genetic algorithm, which uses binary coding and stochastic operators. A hybrid intelligent messy genetic algorithm (HIMGA) for daily generation scheduling is proposed, which can improve the diversity of evolution population and guarantee the convergence and rapidity. The effectiveness and practicality of the method proposed are shown by the simulation results.
出处
《电力系统自动化》
EI
CSCD
北大核心
2004年第15期30-33,38,共5页
Automation of Electric Power Systems
关键词
混合智能messy遗传算法
日发电计划
机组组合
优化
hybrid intelligent messy genetic algorithm (HIMGA)
daily generation scheduling
unit commitment
optimization
