摘要
提出一种启发式调整策略和粒子群优化相结合的新方法求解电力系统中的机组组合(UC)问题。算法将UC问题分解为具有整型变量和连续变量的两个优化子问题,采用离散粒子群优化和等微增率相结合的双层嵌套方法对外层机组启、停状态变量和内层机组功率经济分配子问题进行交替迭代优化求解。同时构造了关机调整和替换调整两个启发式搜索策略对优化结果进行进一步局部微调以提高算法解决UC问题的全局寻优能力和计算效率,从而有效改善解的质量。以10～100台机组组成的5个测试系统为算例,通过与其他算法结果进行比较分析,验证了该方法的可行性和有效性。仿真结果表明该方法解决大规模机组组合问题具有求解精度高和收敛速度快的优势。
This paper proposes a new approach combining of particle swarm optimization （PSO） and heuristic-adjusted strategies to solve unit commitment （UC） problem in power system. The UC problem is decomposed into two embedded optimization sub-problems： one the unit on/off status schedule problem with integer variables that could be solved by the discrete binary particle swarm optimization method and the other load economic dispatch problem with continuous variables that could be solved by the equal Lambda-iteration method. At the same time, shutdown-adjusted and replacement-adjusted strategies are performed on the optimal results to raise solution quality, which could be effectively enhanced the algorithm＇s global optimization performance and computational efficiency. The feasibility and effectiveness of the proposed method are demonstrated for five test systems with the number of generating units in the range of 10 to 100, and the computational results are compared with those previously reported in literature. Simulation results show that the proposed method has advantages for solving UC problem with high precision and quickly convergence speed.
出处
《电工技术学报》
EI
CSCD
北大核心
2009年第12期137-141,共5页
Transactions of China Electrotechnical Society
基金
国家自然科学基金资助项目(50779020
50539140)
关键词
粒子群优化
机组组合
负荷经济分配
启发式调整策略
Particle swarm optimization
unit commitment
load economic dispatch
heuristicadjusted strategies
