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考虑虚拟控制参数调节的风储联合调频优化模型预测控制OA北大核心CSTPCD

Combined wind-storage system frequency regulation optimization model predictive control considering virtual control parameter adjustment

中文摘要英文摘要

为提高大规模风电并网后电网频率稳定性,降低风储系统调频成本,提出考虑虚拟控制参数调节的优化模型预测控制策略.首先,计及储能系统对电网惯性水平与阻尼能力的作用,引入双曲正切函数自适应调节储能系统虚拟控制参数,满足不同时段的调频需求.然后,基于风储系统状态方程建立预测模型,以频率偏差与调频成本最小为控制目标,设计自适应模型预测控制策略.最后,搭建仿真模型,在阶跃和连续负荷扰动工况下对不同控制策略的效果进行对比分析.结果表明,所提控制策略能够有效改善电网调频效果,优化储能系统和风电机组的出力,具有更优的协同控制性能.

To improve the frequency stability of the power grid after large-scale wind power integration,and reduce the frequency regulation cost of the wind-storage system,an optimal model predictive control strategy considering virtual control parameter adjustment is proposed.First,considering the effect of the energy storage system on the inertia level and damping capacity of the power grid,the hyperbolic tangent function is adopted to adaptively adjust the virtual control parameters of the energy storage system to meet the frequency regulation requirements in different periods.Secondly,the prediction model is established based on the state equations of the wind-storage system,and the adaptive model predictive control strategy is designed with the minimum frequency deviation and frequency regulation cost as the control objectives.Finally,a simulation model is built to compare and analyze the effects of different control strategies in step and continuous load disturbance conditions.The results show that the proposed control strategy can effectively improve the frequency regulation effect of the power grid,optimize the output power of the energy storage system and wind turbines,and has better cooperative control performance.

王育飞;张文韬;杨铭诚;黄敏丽;于艾清;薛花;林顺富

上海电力大学电气工程学院,上海 200090国网上海市电力公司市北供电公司,上海 200040上海勘察设计研究院(集团)有限公司,上海 200093

风电机组储能系统一次调频虚拟控制参数模型预测控制

wind turbineenergy storageprimary frequency regulationvirtual control parametersmodel predictive control

《电力系统保护与控制》 2024 (016)

37-48 / 12

This work is supported by the National Natural Science Foundation of China(No.51977127). 国家自然科学基金项目资助(51977127);上海市科技创新行动计划项目资助(22010501400)

10.19783/j.cnki.pspc.231363

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