摘要
针对分布式电源并网对配电网系统的影响,以电压质量和网络损耗为目标解决分布式电源并网的规划问题,将其转化为多目标寻优模型,采用GPC算法实现目标优化。为解决GPC算法在求解非线性约束时的失效性问题,将遗传算法与GPC结合形成GA-GPC改进算法,将有约束广义预测控制性能指标优化的极小值问题转化为遗传算法优化的极大值问题,经过遗传迭代计算得到满足约束的最优控制量。IEEE33节点系统仿真结果表明,采用GA-GPC改进算法可以优化分布式电源并网配置,加强分布式电源并网时系统的稳定性,兼顾提高各节点电压并有效降低网络损耗。
Aiming at the influence of distributed power grid-connection in distribution network system,the rationality of distributed power grid-connection plan is solved by taking voltage quality and network loss as objectives,which is transformed into a multi-objective optimization model,and GPC algorithm is used to achieve the target optimization.In order to solve the failure problem of GPC algorithm in solving nonlinear constrained problems,an improved GA-GPC algorithm is formed by combining genetic algorithm with GPC.The minimum problem of performance index optimization of constrained generalized predictive control is transformed into the maximum problem of genetic algorithm optimization.By genetic iterative calculation,the optimal control quantity that satisfies the constraint is finally obtained.The simulation results of IEEE33 node system show that the improved GA-GPC algorithm can optimize the grid-connection configuration of DG,thus strengthening the stability of the system when DG is connected to the grid.At the same time,it can not only improve the voltage of each node,but also reduce the network loss effectively.
作者
徐建军
邓凡良
张晓雷
孙雨凝
闫丽梅
XU Jianjun;DENG Fanliang;ZHANG Xiaolei;SUN Yuning;YAN Limei(School of Electrical Engineering and Information,Northeast Petroleum University,Daqing,Heilongjiang 163318,China;China Petroleum Electric Energy Company Limited,CNPC,Daqing,Heilongjiang 163458,China)
出处
《东北石油大学学报》
CAS
北大核心
2020年第4期105-112,I0008,I0009,共10页
Journal of Northeast Petroleum University
基金
黑龙江自然科学基金项目(LH2019E016)。
关键词
配电网
分布式电源并网
广义预测控制
遗传算法
网络损耗
节点电压
distribution network
distributed generation grid-connected
generalized predictive control
genetic algorithm
network loss
node voltage
作者简介
徐建军(1971-),男,博士后,博士生导师,教授,主要从事电力系统安全稳定方面的研究;通信作者:闫丽梅,E-mail:yanlimeidaqing@163.com。
