基于改进粒子群算法的风光水互补发电系统短期调峰优化调度

风光水互补发电系统优化调度需要考虑风光电源的间歇性及波动性,同时还要处理梯级水库复杂的水力联系及不同电源之间的电力联系,因而建立风光水互补发电系统短期调峰优化调度模型,并采用粒子群算法进行求解,针对粒子群算法的早熟及后期收敛速度慢等问题,从惯性因子和种群拓扑结构两方面对粒子群算法进行改进,并对福建省电力调控中心管辖的12座常规水电站、木兰溪1座抽水蓄能电站、31座风电场、5座光伏电站组成的风光水多种电源互补系统进行数值分析。结果表明,所建模型能较好地实现对电网负荷的削峰填谷,所提算法显著提高了求解效率和求解质量,是一种解决风光水互补发电系统短期联合优化调峰调度实用性很强的有效算法。

Short-term Peak Shaving for Wind-Photovoltaic-Hydro System Optimization Based on Improved Particle Swarm Optimization

The optimal operation of wind photovoltaic hydro system should consider the intermittency and instability of wind and photovoltaic power sources. Besides, it is necessary to deal with complex hydraulic connections of cascade reservoirs and the electric connections of different power sources. In this paper, short term peak shaving optimal model of wind photovoltaic hydro multi power system was established, and particle swarm algorithm was applied to solve the model. In order to solve the problem of premature and slow convergence of the particle swarm optimization, inertia weights and population topological construction were presented to improve the algorithm. A wind photovoltaic hydro multi power system that contains 12 hydropower stations, a pump storage power station, 31 wind power plants and 5 photovoltaic power plants in Fujian Province was adopted to test the proposed model. The results show that this model can successfully resolve peak shaving problem, and the proposed algorithm significantly improve the solution efficiency and quality, which indicates that it is a practical and effective method to solve short term peak shaving for wind photovoltaic hydro multi power joint optimization.