风-光-水联合发电系统优化调度研究  

RESEARCH OF OPTIMAL SCHEDULING OF HYBRID PV AND WIND POWER SYSTEM

作　　者：罗毅[1] 张若含 

机构地区：[1]华北电力大学控制与计算机工程学院,北京102206

出　　处：《太阳能学报》2015年第10期2492-2498,共7页Acta Energiae Solaris Sinica

基　　金：国家自然科学基金(61273144);北京市自然科学基金(4122071)

摘　　要：为使风-光-水联合发电系统达到经济效益最大化优化调度且平抑功率波动的目的,以功率波动最小为目标的函数引入到经济效益最大化模型中,针对粒子群算法在进化过程中易早熟、后期收敛速度慢且精度较低的特点,提出一种动态调整学习因子的免疫粒子群算法。该算法对学习因子进行非对称线性动态调整,增强前期的全局搜索能力以及后期的局部搜索能力,快速得到全局最优解。该算法在该多目标联合优化调度系统的求解中搜索精度显著提高,表明模型和算法的有效性。In order to make hybrid PV and wind power generation reach optimized dispatching of the maximum economic benefit and stabilize the power fluctuation, the objective of power fluctuation minimum was introduced to the maximization of economic benefit model. An immune particle swarm optimization algorithm based on dynamically adjusting learning factor was proposed to overcome the defects, such as the premature, low precision and slow convergence in the late term of PSO algorithm. The asymmetric linear dynamical adjustment for learning factors was carried out to enforce the global searching ability in the prophase and the upper local global optimum. The algorithm could be used well in the multi-objective hybrid PV improve the accuracy of the solution and optimize the maximum economic benefit, and algorithm. searching ability to get more precise and wind power generation system to show the effectiveness of the model

关 键 词：学习因子 免疫粒子群算法 风-光-水联合系统 经济性调度 功率波动 

分 类 号：TM734[电气工程—电力系统及自动化] TP312[自动化与计算机技术—计算机软件与理论]