电力系统机组组合问题的动态双种群粒子群算法

Dynamic double-population particle swarm optimization algorithm for power system unit commitment

针对标准粒子群优化算法易陷入局部最优点的缺点,提出了动态双种群粒子群优化算法(DDPSO)。该算法中两个子种群规模随进化过程不断变化,进化中分别采用不同的学习策略且相互交换信息。将该算法应用于机组组合问题中,采用实数矩阵编码方法对发电计划进行编码,将两层优化问题转化为单层优化问题,直接运用DDPSO算法求解。仿真结果表明,用该方法解决机组组合问题具有良好的精度和鲁棒性。

Dynamic Double-population Particle Swarm Optimization （DDPSO） algorithm was presented to solve the problem that the standard PSO algorithm easily fell into a locally optimized point, where the population was divided into two sub-populations varying with their own evolutionary learning strategies and exchanging between them. The algorithm had been applied to power system Unit Commitment （UC）. The DDPSO particle consisted of a two-dimensional real number matrix representing the generation schedule. According to the proposed coding manner, the DDPSO algorithm could directly solve UC. Simulation results show that the proposed method performs better in term of precision and convergence property,