含随机风电的大规模多目标机组组合问题的向量序优化方法  

A Vector Ordinal Optimization Method for Large-Scale Multi-Objective Unit Commitment Considering Stochastic Wind Power Generation

作　　者：谢敏[1] 闫圆圆[1] 刘明波[1] 赵文猛[1] 

机构地区：[1]华南理工大学电力学院,广东省广州市510640

出　　处：《电网技术》2015年第1期215-222,共8页Power System Technology

基　　金：国家重点基础研究发展计划项目(973项目)(2013CB228205);国家自然科学基金青年科学基金项目(50907023)~~

摘　　要：向量序优化是一种新的解决多目标、超大计算量、复杂优化问题的有效方法。文中以煤耗量、购电费用、SO2排放量为优化目标,首次引入向量序优化理论求解含大规模随机风电的多目标机组组合问题。以含大型风电场及水、火、核、气、生物质能等复杂电源的某省级电力系统为例,将所提算法的优化结果与基于GAMS-BARON求解器的混合整数非线性规划法(mixed integer nonlinear programming,MINLP)进行对比分析,结果表明所提算法的求解速度大大优于传统的MINLP且优化结果偏差很小,验证了采用向量序优化方法求解含随机风电的大规模多目标机组组合问题的有效性。Vector ordinal optimization is a new and effective method to solve multi-objective optimization problems with heavy calculation burden. Taking the coal consumption, the power purchase cost and the emission of SO2 as optimization objectives, the vector ordinal optimization theory is led in to solve the multi-objective unit commitment the first time while the large-scale stochastic wind power generation is taken into account. Taking a certain provincial power grid containing large-scale wind farm and such complex energy sources as hydropower, thermal power, nuclear power, gas turbines and biomass energy for example, the optimization results obtained by the proposed algorithm are compared with those obtained by GAMS-BARON solver based mixed integer nonlinear programming （MINLP）, and comparison results show that the solution speed of the proposed algorithm is much faster than traditional MINLP method and there is very small deviation in the optimization results, thus, the effectiveness of adopting vector ordinal optimization method to solve large-scale multi-objective unit commitment, in which the stochastic wind power generation is taken into account, is validated.

关 键 词：随机风电 多目标机组组合 向量序优化 足够好解 场景法 BP神经网络 

分 类 号：TM721[电气工程—电力系统及自动化]