文献标志码：A

计及风险系数的含风电场电力系统多目标动态优化调度

李晨1，胡志坚1，董骥2，仉梦林1

(1.武汉大学电气工程学院，湖北武汉430072；2.宜昌供电公司，湖北宜昌443000)

Multi-objective Dynamic Dispatching of Power Grid with Wind Farms by Considering Risk Index LI Chen1, HU Zhijian1, DONG Ji2, ZHANG Menglin1

(1.School of Electrical Engineering, Wuhan University, Wuhan 430072,China;

2.Yichang Power Supply Company, Yichang 443000, China)

摘要：随着风电并网容量的不断增加，传统的确定性优化调度方法已难以满足电力系统安全运行要求。本文建立了计及风险系数的含风电场电力系统多目标动态优化调度模型，模型包括风险系数、燃料成本及污染排放量最小3个目标，将风电场出力及负荷的不确定性纳入模型综合考虑。为了对模型中的随机变量进行处理，引入概率性序列理论，并对其运算空间进行扩展,然后提出了一种改进的多目标教与学优化算法对模型进行求解。含风电场的10机系统算例验证了本文模型及算法的可行性和有效性。

关键词：多目标动态优化调度；风险系数；概率性序列理论；教与学算法；帕累托最优解

文章编号：1007-2322(2015)05-0056-10

中图分类号：TM731

基金项目：高等学校博士学科点专项科研

收稿日期：2014-08-28

作者简介：

Abstract：With the increasing of wind capacity integrated into grid, the traditional deterministic optimization method can hardly meet the requirements for the safe operation of the power system. A multi-objective dynamic dispatch model for power grid with wind farms is presented by considering risk index, which includes such three objectives as minimum fuel cost, minimum emissions and minimum risk index, and also takes the uncertainty of load and the power output of wind farms into consideration. To deal with random variables in this model, probabilistic sequence theory is introduced and its operational space is extended. Then, an improved multi-objective teaching-learning-based optimization (IMOTLBO) algorithm is proposed to solve the model. In the end, the validity and effectiveness of proposed model and algorithm are verified through a 10-gnerators test system with wind farms.

Keywords：multi-objective dynamic dispatch; risk index; probabilistic sequence theory; teaching-learning based optimization; pareto optimal solution

0引言

随着能源危机和环境污染的日益严重，风能的开发和利用已受到各国的高度重视。然而风能的随机性和波动性使得大规模风电场并网下的电力系统运行中不确定因素增多[1]。国内外对于含风电场电力系统调度模型方面做了大量研究。

确定性建模方法[2-6]通过将风电出力预测值的一定百分比作为附加的旋转备用需求，来达到保障系统安全可靠运行的目的。但确定性分析方法所得结果容易造成旋转备用不足或浪费，无法使电力系统的经济性最优。……