A novel approach for unit commitment problem via an effective hybrid particle swarm optimization
A novel approach for unit commitment problem via an effective hybrid particle swarm optimization
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DOI：
10.1109/tpwrs.2005.860907
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发表时间：
2006-01
影响因子：
6.6
通讯作者：
Tiew On Ting;M. Rao;C. K. Loo
中科院分区：
文献类型：
--
作者：
Tiew On Ting;M. Rao;C. K. Loo
提出了一种基于混合粒子群算法的机组组合优化方法。本文提出的HPSO算法是二进制粒子群算法（BPSO）和真实的编码粒子群算法（RCPSO）的混合算法。BPSO用于求解UC问题，RCPSO用于求解经济负荷分配问题。这两种算法同时运行，调整它们的解决方案以寻找更好的解决方案。UC的问题制定考虑了最小的上下时间约束，启动成本，和旋转储备，并被定义为最小化的总目标函数，同时满足所有相关的约束。问题的制定，表示，并为十发电机调度问题的仿真结果。结果清楚地表明，HPSO是非常称职的解决UC问题相比，其他现有的方法。
This paper presents a new approach via hybrid particle swarm optimization (HPSO) scheme to solve the unit commitment (UC) problem. HPSO proposed in this paper is a blend of binary particle swarm optimization (BPSO) and real coded particle swarm optimization (RCPSO). The UC problem is handled by BPSO, while RCPSO solves the economic load dispatch problem. Both algorithms are run simultaneously, adjusting their solutions in search of a better solution. Problem formulation of the UC takes into consideration the minimum up and down time constraints, start-up cost, and spinning reserve and is defined as the minimization of the total objective function while satisfying all the associated constraints. Problem formulation, representation, and the simulation results for a ten generator-scheduling problem are presented. Results clearly show that HPSO is very competent in solving the UC problem in comparison to other existing methods.