含废旧矿井抽蓄电站的源-荷-储协同控制调频研究OA北大核心CSTPCD
Frequency regulation of source-load-storage collaborative control with waste mine-pumped storage power stations
针对新能源在电网中渗透率不断提高导致系统调频容量和频率响应能力不足的问题,提出一种含废旧矿井抽蓄电站的源-荷-储协同控制调频方法.首先,建立了电解铝工业负荷、废旧矿井抽蓄电站和超级电容频率响应模型.然后,基于模型预测控制理论,以系统状态量和控制量的加权函数最小为目标,设计区域信息互动的分布式模型预测控制器,并提出含废旧矿井抽蓄电站的源-荷-储协同控制调频方法.最后,将分布式模型预测控制器应用在所提源-荷-储协同控制方法中,并在改进的IEEE标准两区域LFC模型中进行仿真.结果表明所提方法可以缩短频率恢复时间,减小最大频差值,改善系统调频动态性能.
In response to the problem of insufficient frequency regulation capacity and frequency response ability caused by the increasing penetration rate of new energy in the power grid,this paper proposes a source-load-storage coordinated control frequency regulation method with waste mine-pumped storage power stations.First,the frequency response model is established for the industrial load of electrolytic aluminum,waste mine pumped storage power stations,and super capacitors.Then,based on the theory of model predictive control,a distributed model predictive controller with regional information interaction is designed with the goal of minimizing the weighted functions of system state and control variables,and a source-load-storage coordinated control frequency regulation method with waste mine-pumped storage power stations is proposed.Finally,the distributed model predictive controller is applied to the proposed source-load-storage collaborative control method,and simulated in an improved IEEE standard two-region LFC model.The results show that this method can shorten frequency recovery time,reduce maximum frequency difference,and improve the system's frequency regulation dynamic performance.
骆钊;田肖;莫熙;聂灵峰;沈鑫;雷元庆
昆明理工大学电力工程学院,云南 昆明 650500云南电力调度控制中心,云南 昆明 650051云南电网有限责任公司计量中心,云南 昆明 650051
负荷频率控制废旧矿井抽蓄电站超级电容分布式模型预测控制
load frequency controlwaste mine pumped and storage power stationsuper capacitorsdistributed model predictive control
《电力系统保护与控制》 2024 (008)
134-144 / 11
This work is supported by the National Natural Science Foundation of China(No.52277104). 国家自然科学基金项目资助(52277104);国家重点研发计划项目资助(2022YFB2703500);云南省重点研发计划项目资助(202303AC100003)
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