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1.山东大学智能创新研究院,山东省济南市 250101
2.电网智能化调度与控制教育部重点实验室(山东大学),山东省济南市 250061
曹永吉(1992—),男,博士,副研究员,硕士生导师,主要研究方向:电力系统稳定分析与控制、可再生能源并网及储能技术应用。E-mail:yongji@sdu.edu.cn
张恒旭(1975—),男,通信作者,博士,教授,博士生导师,主要研究方向:电力系统稳定分析与控制、电力系统监测和电力系统数值仿真。E-mail:zhanghx@sdu.edu.cn
李常刚(1984—),男,博士,教授,博士生导师,主要研究方向:电力系统运行与控制。E-mail:lichgang@sdu.edu.cn
收稿:2024-01-11,
修回:2024-06-16,
网络出版:2024-08-01,
纸质出版:2024-10-10
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曹永吉,张恒旭,李常刚.计及扰动不确定性的储能系统容量鲁棒优化配置[J].电力系统自动化,2024,48(19):139-147.
CAO Yongji,ZHANG Hengxu,LI Changgang.Robust Optimization Configuration for Energy Storage System Capacity Considering Uncertainty of Disturbance[J].Automation of Electric Power Systems,2024,48(19):139-147.
曹永吉,张恒旭,李常刚.计及扰动不确定性的储能系统容量鲁棒优化配置[J].电力系统自动化,2024,48(19):139-147. DOI: 10.7500/AEPS20240111001.
CAO Yongji,ZHANG Hengxu,LI Changgang.Robust Optimization Configuration for Energy Storage System Capacity Considering Uncertainty of Disturbance[J].Automation of Electric Power Systems,2024,48(19):139-147. DOI: 10.7500/AEPS20240111001.
针对高比例可再生能源接入下扰动不确定性增加和有功功率控制资源不足的问题,提出一种计及扰动不确定性的储能系统(ESS)容量鲁棒优化配置方法。首先,基于扩展系统频率响应模型,提取ESS运行功率与系统频率动态间的耦合关系,由多面体扰动不确定集构造暂态频率偏移安全约束。其次,综合考虑频率稳定控制能力和扰动不确定性,以投资成本最小和运行收益最大为目标,建立ESS容量鲁棒优化模型。然后,利用线性加权法和鲁棒对等变换将模型转化为确定性三层规划的形式,并基于列与约束生成方法、强对偶理论和Big-M法求解。最后,通过算例分析验证了所提方法的有效性。
Aiming at the problems of increasing disturbance uncertainty and insufficient active power control resources under the integration of high proportion of renewable energy
a robust optimization configuration method for energy storage system (ESS) capacity considering the uncertainty of disturbances is proposed. Firstly
based on the extended system frequency response model
the coupling relationship between ESS operation power and system frequency dynamics is extracted
and the constraints of transient frequency deviation security are constructed from the polyhedral uncertainty set of disturbance. Secondly
taking into account both the frequency stability control capability and the uncertainty of disturbances
a robust optimization model is established with the objectives of minimizing investment costs and maximizing operation benefits. Then
the linear weighted method and robust counterpart are used to transform the optimization model into a deterministic tri-level program
which is solved by using the column-and-constraint generation method
strong duality theorem
and Big-M method. Finally
the effectiveness of the proposed method is verified through case analysis.
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