电力系统自动化2025，Vol.49Issue(21)：160-170,11.DOI:10.7500/AEPS20250226003
港口车-桥互联直流组网系统功率-电压模型预测控制方法
Power-Voltage Model Predictive Control Method for Port Vehicle-Crane Interconnected DC Networking System
摘要
Abstract
The quay crane load,influenced by container weight and lifting acceleration,exhibits the characteristics of short-duration high-power impacts.Increasing hoisting force to improve the efficiency of quay crane exacerbates power impacts.To address the trade-off between operation efficiency enhancement and power impact mitigation of the quay crane,first,a DC microgrid interconnecting quay cranes and electric container trucks is constructed.On this basis,a model predictive control method for vehicle-crane coordinated efficiency and voltage optimization is developed.By coordinating container lifting acceleration and the energy storage droop coefficient of container trucks,the proposed method improves quay crane operation efficiency and optimizes the DC voltage.Then,a state-space equation integrating bus voltage,quay crane efficiency,and container truck energy storage output in vehicle-crane interconnected system is developed.A weight adaptive calculation method based on the transient recovery process of DC voltage is designed,and a cost function aiming to minimize DC voltage deviation and optimize quay crane operation efficiency is formulated to rapidly suppress the power impacts caused by efficiency enhancement.Finally,RT-LAB hardware-in-the-loop simulation demonstrates that the proposed method effectively improves DC voltage stability and improves the quay crane operation efficiency by 1.7％∼3.0％under different container weights and container truck quantities.关键词
车/岸桥/直流微网/模型预测控制/功率冲击/运行效率/电压稳定性Key words
vehicle/quay crane/DC microgrid/model predictive control/power fluctuation/operation efficiency/voltage stability引用本文复制引用
梅琪,黄文焘,王良秀,杨欢红,王杰,张宇阳..港口车-桥互联直流组网系统功率-电压模型预测控制方法[J].电力系统自动化,2025,49(21):160-170,11.基金项目
国家自然科学基金资助项目(52177100) （52177100）
上海市"科技创新行动计划"自然科学基金资助项目(24ZR1436700). This work is supported by National Natural Science Foundation of China(No.52177100)and Natural Science Foundation of Shanghai under Shanghai Action Plan for Science,Technology and Innovation(No.24ZR1436700). （24ZR1436700）
