电力系统保护与控制2026，Vol.54Issue(11)：61-71,11.DOI:10.19783/j.cnki.pspc.251194
基于积分型线性二次最优控制的直流配电网优化控制方法
Optimal control method for DC distribution networks based on integral linear quadratic optimal control
摘要
Abstract
In DC distribution networks,a large number of converters lead to significant challenges in coordinated control.Traditional global optimization algorithms suffer from slow computational speeds and cannot meet the real-time converter control requirements,making it difficult to quickly respond to voltage deviations and converter load imbalances caused by fluctuations in load and renewable energy output.To address the decoupling problem between global optimization and real-time control for multiple converters in DC distribution networks,a coordinated optimal control method based on integral linear quadratic optimal control is proposed,which transforms the complex global optimization problem into a fast real-time tracking control task.First,a multi-objective optimization model for DC distribution networks is established,aiming at voltage restoration and load balancing among converters.By applying the principle of minimum,the optimal operating point of the system is determined.Second,an integral linear quadratic optimal control model is established based on the linear power flow of DC distribution networks.By solving the algebraic Riccati equation offline,optimal feedback and integral gains are obtained,enabling fast real-time control online.Finally,it is simulated and verified on the PSCAD/EMTDC platform.The results demonstrate that the proposed control method enables the system to rapidly converge to the globally optimal operating point within two control cycles,significantly reducing node voltage deviations and converter load imbalance.关键词
直流配电系统/线性二次最优控制/电压恢复/负载均衡Key words
DC distribution network/linear quadratic optimal control/voltage recovery/power balance引用本文复制引用
刘琪,王天资,王丽娜,张晓,陈贺,王世友..基于积分型线性二次最优控制的直流配电网优化控制方法[J].电力系统保护与控制,2026,54(11):61-71,11.基金项目
This work is supported by the National Natural Science Foundation of China(No.52307115). 国家自然科学基金项目资助(52307115) （No.52307115）
山东自然科学基金项目资助(ZR2023QE238) （ZR2023QE238）
