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考虑风场高维相依性的电网动态经济调度优化算法
引用本文:谢敏,柯少佳,胡昕彤,韦薇,杜余昕,刘明波.考虑风场高维相依性的电网动态经济调度优化算法[J].控制理论与应用,2019,36(3):353-362.
作者姓名:谢敏  柯少佳  胡昕彤  韦薇  杜余昕  刘明波
作者单位:华南理工大学电力学院,广东广州,510640;国网福建省电力有限公司福州供电公司,福建福州,350009;国网江西省电力有限公司赣州供电公司,江西赣州,341000
基金项目:国家重点基础研究发展计划(973计划:2013CB228205)、国家自然科学基金青年基金项目(50907023)
摘    要:大规模风电并网给电力系统的调度运行带来了巨大的挑战.本文提出改进的二阶段带补偿随机优化算法,用于考虑风场出力高维相依性的电网动态经济调度问题求解.首先,利用Copula函数描述多风场出力的高维相依性,获得多风场出力的联合分布;随后,引入二阶段带补偿随机优化算法解耦求解动态经济调度模型中的常规变量与随机变量;求解过程中,针对补偿费用期望值的计算受限于相依性风场维数,且对迭代方向指导不明确,导致算法收敛耗时长的问题,引入基于整体最小二乘的递推动态多元线性回归法对二阶段带补偿随机优化算法进行改进,通过补偿费用期望值的动态更新,促使两阶段模型的迭代求解快速收敛,克服了传统随机优化方法的"维数灾"弊端,使该算法能够用于考虑风场高维相依性的电网动态经济调度模型求解.最后利用IEEE 118节点系统和某省级实际电网系统验证了所提算法的有效性和实用性.

关 键 词:Copula  风场高维相依性  最小二乘法  递推动态多元线性回归法  二阶段带补偿随机优化算法
收稿时间:2018/3/7 0:00:00
修稿时间:2018/8/19 0:00:00

Optimization algorithm of dynamic economic dispatching considering the high-dimensional correlation of multiple wind farms
XIE Min,KE Shao-ji,HU Xin-tong,Wei Wei,DU Yu-xin and LIU Ming-bo.Optimization algorithm of dynamic economic dispatching considering the high-dimensional correlation of multiple wind farms[J].Control Theory & Applications,2019,36(3):353-362.
Authors:XIE Min  KE Shao-ji  HU Xin-tong  Wei Wei  DU Yu-xin and LIU Ming-bo
Affiliation:School of Electric Power, South China University of Technology,School of Electric Power, South China University of Technology,School of Electric Power, South China University of Technology,School of Electric Power, South China University of Technology,School of Electric Power, South China University of Technology,School of Electric Power, South China University of Technology
Abstract:Large-scale wind power connected to power grid has brought great challenges to power system scheduling operation. In this paper, an improved two-stage compensation stochastic optimization algorithm is proposed to solve the dynamic economic dispatching problem considering the high-dimensional correlation of multiple wind farms. Firstly, Copula function is used to describe the correlation of high-dimensional wind farms, and the joint distribution of wind output is obtained. Secondly, an improved two-stage compensation stochastic optimization algorithm is proposed to decouple the conventional and stochastic variables in the dynamic economic scheduling model. The calculation of the expected value of compensation cost is usually limited by the dimension of the correlated wind farms, and the direction of the iterative is not clear enough to lead the convergence, all this lead to long computation time consumed. So the recursive dynamic multivariable linear regression method based on global least squares is introduced to improve the proposed algorithm. By dynamic updating of compensation penalty expectation, computation time is greatly shortened. This improved two-stage compensation algorithm proposed in this paper overcomes the dimensional disaster of the traditional stochastic optimization method, and is capable of solving the dynamic economic dispatching problem considering the high-dimensional correlation of multiple wind farms. Finally, the practicability and efficiency of the proposed algorithm is verified by the examples of IEEE118 system and an actual provincial system.
Keywords:copula  high-dimensional correlation of multiple wind farms  least squares method  recursive dynamic multivariable linear regression  two-stage compensation stochastic optimization algorithm
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