山东电力技术2024，Vol.51Issue(11)：39-47,9.DOI:10.20097/j.cnki.issn1007-9904.2024.11.004
多时空尺度风电集群爬坡的模型预测控制分层策略
Hierarchical Model Predictive Control Strategy for Slope Climbing of Wind Power Clusters Based on Multi-temporal Scales
摘要
Abstract
A hierarchical strategy based on model predictive control is proposed in the paper.Firstly,the wind power cluster is dynamically divided,and different time scales of numerical weather prediction(NWP)are utilized as references.Deep learning is employed to model each subset,followed by clustering,partitioning,and judgment for each time period.The control layer is spatially divided into three parts,while the wind power is temporally subdivided using model predictive control.In the rolling optimization phase,climbing constraints of the wind power cluster are incorporated,and in the feedback correction phase,the output state of the wind power cluster at the current moment serves as an initial value for a new round of rolling optimization scheduling.关键词
集群风电场/模型预测控制/滚动优化/爬坡Key words
cluster wind farm/model predictive control/rolling-horizon optimization/slope climbing分类
信息技术与安全科学引用本文复制引用
王京,徐诒玥,房毅,杨海翔,王晨旭..多时空尺度风电集群爬坡的模型预测控制分层策略[J].山东电力技术,2024,51(11):39-47,9.基金项目
山西省发展改革委大众创业万众创新专项(137541005) （137541005）
山西省研究生创新项目(2022Y156) （2022Y156）
教育部产学合作协同育人项目(221002262073019) （221002262073019）
山西省高校学校科技创新项目(2023L002). Shanxi Development and Reform Commission Mass Entrepreneurship and Innovation Project(137541005) （2023L002）
Shanxi Graduate Innovation Project(2022Y156) （2022Y156）
Ministry of Education Industry-University Cooperative Education Project(221002262073019) （221002262073019）
Science and Technology Innovation Project of Colleges and Universities in Shanxi Province(2023L002). （2023L002）
