含风电场电力系统环境经济动态调度建模与算法  

Environmental/Economic Dynamic Dispatch Modeling and Method for Power Systems Integrating Wind Farms

作　　者：陈功贵[1] 陈金富[1] 

机构地区：[1]强电磁工程与新技术国家重点实验室(华中科技大学),湖北省武汉市430074

出　　处：《中国电机工程学报》2013年第10期27-35,22,共9页Proceedings of the CSEE

基　　金：国家重点基础研究发展计划(973计划)项目(2009CB219701);国家自然科学基金重点项目(50937002)~~

摘　　要：为应对风电功率的不确定性,提出考虑风电渗透功率的增、减出力旋转备用量化模型。在旋转备用量化模型基础上,考虑常规发电机阀点效应及爬坡速率约束,采用价格罚因子法嵌入废气排放目标到发电机燃料费用目标函数中,将环境经济动态调度多目标优化问题转化为单目标优化问题。将约束条件的处理与目标函数完全分离,建立了含风电场电力系统环境经济动态调度模型。针对量子粒子群算法存在早熟的问题,引入早熟判断机制,对早熟粒子进行混沌搜索,从而提出改进量子粒子群优化算法求解所建立的调度模型。在10机系统上采用所提出的方法,仿真结果表明,与量子粒子群和粒子群算法比较,所提出的方法能较好地处理风电功率不确定性条件下的环境经济动态调度问题。Increase and decrease spinning reserve quantization models in consideration of wind penetration power were proposed to tackle the uncertain nature of wind power. The bi-objective environmental/economic dynamic dispatch （EEDD） problem was converted into single optimization problem by introducing price penalty factor （PPF） The constraints and objective function was completely separated. Based on spinning reserve quantization model, EEDD model, which took generation unit valve-point effects and ramp rate limits into account, was established. An improved quantum particle swarm optimization （IQPSO） algorithm was proposed to solve EEDD problem of power systems integrating wind farms. The mechanism of chaotic mutation was introduced to overcome the drawback of prematurity in QPSO when prematurity of particle swarm took place. Numerical simulation results show that the proposed method is effective for solving EEDD problem in wind power integrated systems compared with those obtained from QPSO and PSO algorithm.

关 键 词：风力发电 环境经济动态调度 旋转备用 调度模型 改进量子粒子群算法 

分 类 号：TM732[电气工程—电力系统及自动化] TM614