摘要
水光互补是解决电力能源供应问题、构建低碳能源体系、实现“双碳”目标的重要举措。采用集合卡尔曼滤波对线性调度函数的参数进行动态更新,构建了水光互补中长期适应性调度规则,并以龙羊峡水光互补电站为研究对象,在同化过程中引入预测误差、观测误差及集合规模3个不确定性参数,分析了不确定性参数取值对模型时变参数和运行结果的影响,并对参数的敏感性进行了定性分析。结果表明,预测误差、观测误差及集合规模对模型时变参数和运行结果没有显著影响,三者均不是敏感性参数。研究结果可为集合卡尔曼滤波参数不确定性分析及多能互补系统适应性调度研究提供参考。
The hydro-photovoltaic(PV)hybrid power system is an important measure to solve the power supply problem,build a low-carbon energy system,and realize the goal of″dual carbon″.The parameters of linear scheduling function are dynamically updated by ensemble Kalman filter,and the medium and long period adaptive scheduling rules for hydro-PV hybrid power system are constructed.Taking Longyangxia hydro-PV hybrid power station as the research object,three uncertainty parameters,namely,prediction error,observation error and ensemble size,are introduced in the assimilation process.The effects of uncertainty parameter values on the time-varying parameters and operational results of the model are analyzed,and the sensitivity of the parameters is qualitatively analyzed.The results are as follows.Prediction error,observation error and set size have no significant effect on time-varying parameters and operation results of the model,and none of the three are sensitivity parameters.The research results can provide a reference for the uncertainty analysis of ensemble Kalman filter parameters and the study of adaptive scheduling of multi-energy complementary systems.
作者
李赫
张圣杰
丁雨婷
左其亭
LI He;ZHANG Shengjie;DING Yuting;ZUO Qiting(School of Water Conservancy and Transportation,Zhengzhou University,Zhengzhou 450001,China;Yellow River Ecological Protection and Regional Coordinated Development Research Institute,Zhengzhou University,Zhengzhou 450001,China)
出处
《华北水利水电大学学报（自然科学版）》
北大核心
2024年第5期1-14,共14页
Journal of North China University of Water Resources and Electric Power：Natural Science Edition
基金
国家自然科学基金项目(52209037)
中国博士后科学基金项目(2022M722880,2023T160595)
河南省高等学校重点科研项目(23A570002)。
关键词
集合卡尔曼滤波
水光互补
变化环境
适应性调度
敏感性分析
ensemble Kalman filter
hydro-photovoltaic hybrid power systems
changing environment
adaptive operation
sensitivity analysis
