四川大学 电气信息学院，四川省 成都市 610065

提出了一种用于求解机组组合优化问题的蚁群粒子群混合优化算法。通过将机组组合解编码为机组操作序列，降低了蚁群算法搜索的难度，使其空间复杂度由指数型降为线性型，使采用蚁群算法求解更大规模的机组组合问题成为可能。采用协同粒子群算法求解多时段负荷的经济分配问题时，用一个粒子群处理一个时段的优化问题，通过共享粒子群间的惩罚项解决了机组爬升率的约束问题。10机和20机系统的仿真实验和分析结果验证了该方法正确性、有效性和优越性。

机组组合   蚁群算法(ACO)   粒子群优化(PSO)   操作编码

A hybrid algorithm for unit commitment problem based on ant colony optimization (ACO) and particle swarm optimization (PSO) is proposed. By means of coding the solution of unit commitment into unit operation sequence, the searching difficulty of ACO is reduced and the space complexity is reduced from exponential type to linear type. In this way it becomes possible to solve larger scale unit commitment by ACO. When cooperative PSO is used to solve the economic load dispatching within multi time intervals, the optimization within one time interval is processed by one particle swarm, and through sharing the penalty terms among particle swarms the constraints of units’ ramping rates are settled. Simulation results of 10-unit system and 20-unit system show that the proposed method is correct, effective and predominant.

unit commitment   ant colony algorithm (ACO)   particle swarm optimization (PSO)   operate coding