求解电力系统经济调度问题的改进粒子群优化算法

电力系统经济调度问题是电力系统中的一个重要的研究课题,针对该问题,提出一种改进粒子群优化(ODPSO)算法.改进算法在搜索前期,采用广义的反向学习策略,使算法能够快速地靠近较优的搜索区域,从而提高收敛速度;在搜索后期,借鉴差分进化算法的进化机制设计改进的变异和交叉策略,对当前种群的最优粒子进行更新,从而提高种群的多样性,进而协助算法获得全局最优解.为了验证改进粒子群优化算法的有效性,对CEC2006提出的22个基准约束测试函数进行仿真,结果表明改进算法相比其他算法在寻优精度和稳定性上更具优势.最后,将改进算法应用于考虑机组爬坡速率约束、机组禁行区域约束以及电力平衡约束的两个电力系统经济调度问题,取得了令人满意的结果.

Improved particle swarm optimization algorithm for solving power system economic dispatch problem

The power system economic dispatch problem is an important research topic in power systems. To solve this problem, an improved particle swarm optimization(ODPSO) algorithm is proposed. In the early stage of the improved algorithm, the generalized opposition-based learning strategy is used to make the algorithm quickly close to the potential search area and improve the convergence speed. In the later stage of searching, inspired by evolutionary process of differential evolution, an improved mutation and crossover strategy is developed to update the optimal particle of the current population, thus improving the population diversity and assisting the algorithm to obtain global optimal solution. In order to validate the effectiveness of the improved algorithm, 22 constraint test functions presented in CEC2006 are simulated. Experimental results show that the improved algorithm is superior to other compared algorithms in terms of the accuracy and stability. Finally, the improved algorithm is applied to two economic dispatch problems of power systems, which takes into account the ramp rate limits of the generating units, prohibited operating zones and power balance constraint, and satisfying results are obtained.