基于遗传算法的风-光互补发电系统的动态优化

风-光互补发电是一种新型的清洁能源,将为能源结构的调整和环境保护做出巨大贡献。但是,风-光互补发电系统具有强非线性和多目标性,很难建立精确的数学模型,用传统的方法很难对其进行动态管理。采用一种新的遗传算法,以风-光互补发电系统的发电成本最低为目标,并根据控制规则,可对风-光互补电场进行动态优化管理。结果表明,该算法能很好地跟踪负荷、风速及光照强度的变化,能有效地动态管理风-光互补发电系统,说明遗传算法在风-光互补发电系统的动态管理中是有效的。

Dynamic optimization of wind/photovoltaic hybrid power systems based on genetic algorithms

As a clean and renewable energy, wind/photovoltaic hybrid generation, which will contribute to adjusting energy structures and protecting environments, has attracted lots of countries and organizations. But it is very difficult to model with strong nonlinearity and multiple objective, and hard to manage it dynamically with traditional methods. This paper designed a genetic algorithm, which according to the control rules, optimizes wind/photovoltaic hybrid power systems dynamically with the lowest running cost. The optimization result shows this genetic algorithm can trace varieties of load, wind velocity, and solar radiation, and can control wind/photovoltaic hybrid power systems effectively when it works.