面向快速响应与可调能力恢复的储能系统实时调度-再调度策略

doi:10.19595/j.cnki.1000-6753.tces.250776

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (3718 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract With the increasing penetration of renewable energy, power system is facing more severe challenges related to random power fluctuations and supply-demand imbalances during operation. Energy storage systems (ESS) possess rapid response capability and flexible bidirectional regulation characteristic, enabling them to provide swift and adaptable power regulation and balancing service during real-time power system operation. Currently, ESS primarily participates in power system regulation through electricity market, where economic benefits remain a key factor influencing the extent of ESS involvement. However, this approach often overlooks the actual requirements for efficient adjustment and response speed during real-time emergencies. Moreover, as most real-time emergencies are short-lived issues, there is currently a lack of in-depth research on the rescheduling process for restoring ESS to planned operation and recoverable regulation capability after resolving such issues. To address these gaps, this paper develops a comprehensive real-time ESS response-recovery scheduling framework and proposes a real-time scheduling-rescheduling strategy for ESS, focusing on rapid response and regulation capability recovery.
This paper first establishes a real-time scheduling-rescheduling framework for energy storage systems (ESS), meticulously characterizing the complete operational process encompassing rapid response and adjustable capacity recovery during real-time operation. Secondly, to rapidly mobilize resource response capability during contingency, we develop a real-time power decision-making method from both the dispatch center and energy storage station perspectives. This method comprehensively considers: decision-making time at the dispatch center, decision-making time at energy storage stations, and response time of energy storage resources. By incorporating the power response rates of multiple ESS types and state transition mechanisms, the proposed method achieves rapid mitigation of power fluctuations. Moreover, post-contingency when ESS energy levels often deviate from original operating plans, we construct an energy soft boundary for ESS to maintain subsequent operational effectiveness while considering economic benefits in real-time spot markets. Accounting for power plan failure risks and balancing the advantages of online computation with offline training, we propose a synthesize approximate future cost function (SAFCF) that integrates online information with uncertainty factors. This function supports real-time rescheduling decision-making for adjustable capacity recovery processes. Lastly, case studies verify the effectiveness of the proposed method, which can fully activate the rapid response capability of energy storage systems, achieve stable recovery of their adjustable capacity, and effectively support stable operation of power systems.
Through case study validation and comparative analysis of the proposed model, the following conclusions can be drawn: (1)The real-time dispatch command decomposition model based on state transition avoids repeated resource sorting and optimization processes, significantly improving the decision-making speed of energy storage stations. The fast response model under emergency prioritizes allocating power deficits to the energy storage stations with the fastest response rates, enabling more rapid compensation of power deviations compared to traditional methods. (2)The proposed SAFCF effectively integrates the advantages of both online computation and offline training, achieving real-time decision-making that balances economic efficiency with stable recovery of adjustable capacity. The rescheduling model based on SAFCF can effectively perceive future information for real-time decisions, ensuring stable recovery of adjustable capacity while reducing ESS operating costs. Moreover, its high decision-making efficiency meets real-time operational requirements.

Key words： Energy storage system real-time scheduling rescheduling model power response speed adjustable capacity restoration

Received: 09 May 2025

PACS:

TM734

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Hu Junjie
	Chen Quanxi
	Pan Yi

Cite this article:

Hu Junjie,Chen Quanxi,Pan Yi. Real-Time Scheduling-Rescheduling Strategy of Energy Storage System for Fast Response and Adjustable Capacity Restoration[J]. Transactions of China Electrotechnical Society, 2026, 41(9): 3022-3037.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.250776 OR https://dgjsxb.ces-transaction.com/EN/Y2026/V41/I9/3022

[1] 袁铁江, 张文达. 新型电力系统源-储-荷协同规划研究综述[J]. 中国电机工程学报, 2025, 45(17): 6664-6683.
Yuan Tiejiang, Zhang Wenda.A review of research on source-storage-load coordinated planning for new power system[J]. Proceedings of the CSEE, 2025, 45(17): 6664-6683.
[2] 程林, 索克兰, 许鹤麟. 新能源侧电池储能系统运行评价: 现状与展望[J]. 电力系统自动化, 2025, 49(15): 1-19.
Cheng Lin, Suo Kelan, Xu Helin.Operation evaluation of battery energy storage systems on renewable energy side: current status and prospects[J]. Automation of Electric Power Systems, 2025, 49(15): 1-19.
[3] Calero F, Cañizares C A, Bhattacharya K, et al.A review of modeling and applications of energy storage systems in power grids[J]. Proceedings of the IEEE, 2023, 111(7): 806-831.
[4] 刘德旭, 杨迎, 黄宏旭, 等. 新型电力系统大规模抽水蓄能调度运行与控制综述及展望[J]. 中国电机工程学报, 2025, 45(1): 80-98.
Liu Dexu, Yang Ying, Huang Hongxu, et al.An overview and outlook of the operation and control of large-scale pumped hydro storages in modern power systems[J]. Proceedings of the CSEE, 2025, 45(1): 80-98.
[5] 李更丰, 孙少华, 别朝红, 等. 面向新型电力系统弹性提升的储能优化配置与灵活调度研究综述[J]. 高电压技术, 2023, 49(10): 4084-4095.
Li Gengfeng, Sun Shaohua, Bie Zhaohong, et al.Review on optimal configuration and flexible scheduling research of energy storage for resilience improvement of new power system[J]. High Voltage Engineering, 2023, 49(10): 4084-4095.
[6] 谢小荣, 马宁嘉, 刘威, 等. 新型电力系统中储能应用功能的综述与展望[J]. 中国电机工程学报, 2023, 43(1): 158-169.
Xie Xiaorong, Ma Ningjia, Liu Wei, et al.Functions of energy storage in renewable energy dominated power systems: review and prospect[J]. Proceedings of the CSEE, 2023, 43(1): 158-169.
[7] 李军徽, 安晨宇, 李翠萍, 等. 计及调峰市场交易的储能-新能源-火电多目标优化调度[J]. 电工技术学报, 2023, 38(23): 6391-6406.
Li Junhui, An Chenyu, Li Cuiping, et al.Multi-objective optimization scheduling method considering peak regulating market transactions for energy storage-new energy-thermal power[J]. Transactions of China Electrotechnical Society, 2023, 38(23): 6391-6406.
[8] 刘自发, 李昆阳, 郑司琪. 光-储电站参与多应用场景的日前-日内协同滚动优化策略[J]. 电网技术, 2025, 49(1): 198-208.
Liu Zifa, Li Kunyang, Zheng Siqi.Daily intra day collaborative rolling optimization strategy for solar energy storage power stations participating in multiple application scenarios[J]. Power System Technology, 2025, 49(1): 198-208.
[9] 冯艺萱, 边晓燕, 陈雯, 等. 新型电力系统灵活性资源成本回收机制分析及挑战[J]. 电工技术学报, 2025, 40(21): 7013-7028.
Feng Yixuan, Bian Xiaoyan, Chen Wen, et al.Analysis and challenges of new power system flexibility resource cost recovery mechanisms[J]. Transactions of China Electrotechnical Society, 2025, 40(21): 7013-7028.
[10] 吴珊, 边晓燕, 张菁娴, 等. 面向新型电力系统灵活性提升的国内外辅助服务市场研究综述[J]. 电工技术学报, 2023, 38(6): 1662-1677.
Wu Shan, Bian Xiaoyan, Zhang Jingxian, et al.A review of domestic and foreign ancillary services market for improving flexibility of new power system[J]. Transactions of China Electrotechnical Society, 2023, 38(6): 1662-1677.
[11] 徐业琰, 姚良忠, 廖思阳, 等. 基于多智能体Actor-double-critic深度强化学习的源-网-荷-储实时优化调度方法[J]. 中国电机工程学报, 2025, 45(2): 513-527.
Xu Yeyan, Yao Liangzhong, Liao Siyang, et al.Real-time optimal dispatch method of source-grid-load-storage based on multi-agent actor-double-critic deep reinforcement learning[J]. Proceedings of the CSEE, 2025, 45(2): 513-527.
[12] Li Hepeng, He Haibo.Learning to operate distribution networks with safe deep reinforcement learning[J]. IEEE Transactions on Smart Grid, 2022, 13(3): 1860-1872.
[13] Du Pengbo, Huang Bonan, Liu Ziming, et al.Real-time energy management for net-zero power systems based on shared energy storage[J]. Journal of Modern Power Systems and Clean Energy, 2024, 12(2): 371-380.
[14] Liu Chunyang, Zhang Hengxu, Shahidehpour M, et al.A two-layer model for microgrid real-time scheduling using approximate future cost function[J]. IEEE Transactions on Power Systems, 2022, 37(2): 1264-1273.
[15] Xue Xizhen, Ai Xiaomeng, Fang Jiakun, et al.Continuous approximate dynamic programming algorithm to promote multiple battery energy storage lifespan benefit in real-time scheduling[J]. IEEE Transactions on Smart Grid, 2024, 15(6): 5744-5760.
[16] 鲍谚, 石锦凯, 陈世豪. 考虑负荷预测不确定性的快充站储能鲁棒实时控制策略[J]. 电力系统自动化, 2023, 47(10): 107-116.
Bao Yan, Shi Jinkai, Chen Shihao.Robust real-time control strategy for energy storage in fast charging station considering load forecasting uncertainty[J]. Automation of Electric Power Systems, 2023, 47(10): 107-116.
[17] Li Zhengmao, Wu Lei, Xu Yan, et al.Multi-stage real-time operation of a multi-energy microgrid with electrical and thermal energy storage assets: a data-driven MPC-ADP approach[J]. IEEE Transactions on Smart Grid, 2022, 13(1): 213-226.
[18] 崔杨, 李崇钢, 赵钰婷, 等. 考虑风-光-光热联合直流外送的源-网-荷多时段优化调度方法[J]. 中国电机工程学报, 2022, 42(2): 559-573.
Cui Yang, Li Chonggang, Zhao Yuting, et al.Source-grid-load multi-time interval optimization scheduling method considering wind-photovoltaic-photothermal combined DC transmission[J]. Proceedings of the CSEE, 2022, 42(2): 559-573.
[19] Liu Ruiwen, Hui Hongxun, Chen Xia, et al.Distributed frequency control of heterogeneous energy storage systems considering short-term ability and long-term flexibility[J]. IEEE Transactions on Smart Grid, 2024, 15(6): 5693-5705.
[20] 高帆, 包道日娜, 赵明智, 等. 多场景规划下混合储能对风光耦合出力波动的平抑特性[J]. 电工技术学报, 2025, 40(9): 2827-2839.
Gao Fan, Bao Daorina, Zhao Mingzhi, et al.Smoothing method of wind-solar coupled output fluctuations by hybrid energy storage under multi-scenario planning[J]. Transactions of China Electro-technical Society, 2025, 40(9): 2827-2839.
[21] 李翠萍, 司文博, 李军徽, 等. 基于集合经验模态分解和多目标遗传算法的火-多储系统调频功率双层优化[J]. 电工技术学报, 2024, 39(7): 2017-2032.
Li Cuiping, Si Wenbo, Li Junhui, et al.Two-layer optimization of frequency modulated power of thermal generation and multi-storage system based on ensemble empirical mode decomposition and multi-objective genetic algorithm[J]. Transactions of China Electrotechnical Society, 2024, 39(7): 2017-2032.
[22] Xue Xizhen, Ai Xiaomeng, Fang Jiakun, et al.Real-time schedule of microgrid for maximizing battery energy storage utilization[J]. IEEE Transactions on Sustainable Energy, 2022, 13(3): 1356-1369.
[23] Dratsas P A, Psarros G N, Papathanassiou S A.A real-time redispatch method to evaluate the contribution of storage to capacity adequacy[J]. IEEE Transactions on Power Systems, 2024, 39(1): 1274-1286.
[24] Zhou Mike, Yan Jianfeng.A new solution architecture for online power system analysis[J]. CSEE Journal of Power and Energy Systems, 2018, 4(2): 250-256.
[25] 童宇轩, 胡俊杰, 杜昊明, 等. 基于虚拟电池模型的外逼近闵可夫斯基热泵负荷调节可行域聚合方法[J]. 电网技术, 2024, 48(8): 3340-3349.
Tong Yuxuan, Hu Junjie, Du Haoming, et al.Feasible region aggregation method for load regulation of minkowski heat pump based on external approximation of VB model[J]. Power System Technology, 2024, 48(8): 3340-3349.
[26] Tan Zhenfei, Yu Ao, Zhong Haiwang, et al.Optimal virtual battery model for aggregating storage-like resources with network constraints[J]. CSEE Journal of Power and Energy Systems, 2024, 10(4): 1843-1847.
[27] Wang Chong, Ju Ping, Lei Shunbo, et al.Markov decision process-based resilience enhancement for distribution systems: an approximate dynamic programming approach[J]. IEEE Transactions on Smart Grid, 2020, 11(3): 2498-2510.
[28] Nascimento J, Powell W B.An optimal approximate dynamic programming algorithm for concave, scalar storage problems with vector-valued controls[J]. IEEE Transactions on Automatic Control, 2013, 58(12): 2995-3010.
[29] Chen Peiyuan, Pedersen T, Bak-Jensen B, et al.ARIMA-based time series model of stochastic wind power generation[J]. IEEE Transactions on Power Systems, 2010, 25(2): 667-676.
[30] Singh A K, Bikash C P.IEEE PES task force on benchmark systems for stability controls: report on the 68-bus, 16-machine, 5-area system[J]. IEEE Power Energy Society, 2013, 3(12): 1-41.
[31] Elia Open Data Portal[EB/OL]. (2025-04-17). https:// opendata.elia.be/pages/home/.
[32] Liu Hui, Qi Junjian, Wang Jianhui, et al.EV dispatch control for supplementary frequency regulation considering the expectation of EV owners[J]. IEEE Transactions on Smart Grid, 2018, 9(4): 3763-3772.
[33] Deng Xiaosong, Zhang Qian, Li Yan, et al.Hierarchical distributed frequency regulation strategy of electric vehicle cluster considering demand charging load optimization[C]//2020 IEEE 3rd Student Conference on Electrical Machines and Systems (SCEMS), Jinan, China, 2020: 959-969.
[34] 胡俊杰, 陆家悦, 马文帅, 等. 面向电网调峰的电动汽车聚合商多层级实时控制策略[J]. 电力系统自动化, 2024, 48(22): 84-95.
Hu Junjie, Lu Jiayue, Ma Wenshuai, et al.Multi-layer real-time control strategy of electric vehicle aggregators for peak regulation of power grid[J]. Automation of Electric Power Systems, 2024, 48(22): 84-95.
[35] Li Yang, Han Meng, Yang Zhen, et al.Coordinating flexible demand response and renewable uncertainties for scheduling of community integrated energy systems with an electric vehicle charging station: a bi-level approach[J]. IEEE Transactions on Sustainable Energy, 2021, 12(4): 2321-2331.
[36] Kovačević M, Vašak M.Aggregated representation of electric vehicles population on charging points for demand response scheduling[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(10): 10869-10880.