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1.现代电力系统仿真控制与绿色电能新技术教育部重点实验室(东北电力大学),吉林省吉林市 132012
2.内蒙古电力(集团)有限责任公司,内蒙古自治区呼和浩特市 010020
3.天津大学未来技术学院,天津市 300350
Received:27 March 2024,
Revised:2024-05-28,
Published Online:29 July 2024,
Published:25 May 2025
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李军徽,宋清龙,郭琦等.计及可用容量和调频成本的网侧多储能电站双层优化策略[J].电力系统自动化,2025,49(10):101-111.
LI Junhui,SONG Qinglong,GUO Qi,et al.Bi-level Optimization Strategy of Grid-side Multiple Energy Storage Stations Considering Available Capacity and Frequency Regulation Cost[J].Automation of Electric Power Systems,2025,49(10):101-111.
李军徽,宋清龙,郭琦等.计及可用容量和调频成本的网侧多储能电站双层优化策略[J].电力系统自动化,2025,49(10):101-111. DOI: 10.7500/AEPS20240327002.
LI Junhui,SONG Qinglong,GUO Qi,et al.Bi-level Optimization Strategy of Grid-side Multiple Energy Storage Stations Considering Available Capacity and Frequency Regulation Cost[J].Automation of Electric Power Systems,2025,49(10):101-111. DOI: 10.7500/AEPS20240327002.
针对区域内储能资源因调频指令分配不均
部分储能电站提前丧失充/放电能力,导致后续调频需求响应能力不足的问题,提出计及可用容量和调频成本的网侧多储能电站双层优化策略。所提策略包括储能电站调频功率分配层和储能单元调频功率分配层:上层引入可用容量状态系数和关于储能资源调节能力的自适应系数,设计了储能电站调频功率分区优化分配模型,实现调频功率在各储能电站的均衡分配。下层计及健康状态(SOH)和抗拒系数,与储能单元荷电状态(SOC)均衡恢复构成目标函数,实现SOH高的储能单元优先恢复SOC。仿真结果表明,所提策略在保障一定经济性的同时可提高区域内储能资源响应调频指令的能力,实现储能资源的尽限利用。
Aiming at the problem that some energy storage stations in a region lose charging/discharging capability in advance due to uneven distribution of frequency regulation instructions
resulting in insufficient response ability of subsequent frequency regulation demand
a bi-level optimization strategy of grid-side multiple energy storage stations considering available capacity and frequency regulation cost is proposed. The proposed strategy includes the frequency regulation power distribution layers of energy storage stations and energy storage units. The upper layer introduces the available capacity state coefficient and the adaptive coefficient about the adjustment ability of energy storage resources
and designs the optimal distribution model of frequency regulation power partition of energy storage station to realize the balanced distribution of frequency regulation power in each energy storage station. The lower layer takes into account the state of health (SOH)
the resistance coefficient
and the balanced recovery of the state of charge (SOC) for the energy storage unit to construct the objective function
so as to prioritize the recovery of SOC for energy storage units with high SOH. The simulation results show that the proposed strategy can improve the response ability of the energy storage resources in the region and realize the maximum utilization of energy storage resources while ensuring a certain economy.
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