高比例风电系统中储能集群辅助火电机组调峰分层优化控制策略

doi:10.19595/j.cnki.1000-6753.tces.240545

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Abstract To address the challenges associated with integrating large-scale wind power into the power grid, we propose a layered optimization control strategy. This strategy employs energy storage clusters to support thermal power unit peaking. In the fire-storage control layer, an optimization control strategy is introduced based on the peak-regulating surplus coefficient of the energy storage cluster. The goal of this strategy is to reserve space for energy storage peak-regulating output by introducing the storage peak-regulating surplus coefficient, thereby enhancing the overall peak-regulating effect. In the internal control layer of the storage cluster, a multi-objective constraint optimization control strategy is proposed to mitigate the aging of the storage power station. This strategy considers various technical and economic constraints such as network tidal current constraints, regulation consistency constraints, output constraints, and fairness constraints. To determine the optimal power output of each energy storage power station, a multi-objective model is employed that takes into account factors such as slow decay of storage life, low node load rate balance, and low operating costs. The initial value of power output for each storage power station is obtained using genetic algorithms and iterated with the corrected power output of storage energy in the upper layer of the peak regulation task assignment. Through simulation, the proposed strategy can achieve a peak demand reduction of 87.40 MW, while also reducing the total operating cost by 3.62%. Additionally, the number of cycle lives for each energy storage power station increases to varying degrees, including a 4.38% increase for lithium battery storage plants. The effectiveness of the proposed strategy is verified from both an economic and technical standpoint.
In order to verify the effectiveness of the above control strategies, this paper takes an actual wind power and load data from a certain location in China for simulation analysis. The specific conclusions are as follows:
The peak optimization control strategy predicated on the surplus coefficient diminishes the peak shifting demand by 87.40MW in comparison to traditional methods, achieving a 100% wind power consumption rate. Utilizing the residual adjustable capacity of thermal power units to charge the energy storage stations leads to a 0.22% increment in their operating costs (from 365.78 ¥ to 427.68 ¥). However, this results in an 80% extension in the operation time of the energy storage, with no wind abandonment or load loss, culminating in a total operational cost reduction of 1.70×10⁵ ¥ or 3.62% relative to traditional strategies.
By elevating the life loss cost of the energy storage stations within the lower model, while ensuring equitable load rate equalization, pumped storage stations with lower cycle aging costs are empowered, with the converse true for various electrochemical stations. Computational outcomes indicate that the depth of discharge for Lithium Battery Station 1 is reduced by 5.38% under the proposed strategy, increasing its cycle life by 188 times, and for Lithium Battery Station 2 by 4.62%.
Varying the surplus coefficient of energy storage peaking reveals its relationship with operational costs and peaking demands. Optimal economy and peaking effects are achieved when selecting a State of Charge (SOC_tj) of 0.88 and a surplus coefficient of 0.3.

Key words： Energy storage cluster power system peaking remaining useful life two-layer optimal control strategy

Received: 07 April 2024

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	Li Junhui
	Pan Yahui
	Mu Gang
	Li Cuiping
	Jia Chen

Cite this article:

Li Junhui,Pan Yahui,Mu Gang等. Hierarchical Optimal Control Strategy for Storage Cluster-Assisted Thermal Unit Peaking in High-Ratio Wind Power System[J]. Transactions of China Electrotechnical Society, 2025, 40(7): 2127-2145.

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[1] 国家能源局. 2023年全国电力工业统计数据[EB/OL]. [2024-01-26]. https://www.nea.gov.cn/2024-01/26/c1310762246.htm.
[2] 鲁宗相, 林弋莎, 乔颖, 等. 极高比例可再生能源电力系统的灵活性供需平衡[J]. 电力系统自动化, 2022, 46(16): 3-16.
Lu Zongxiang, Lin Yisha, Qiao Ying, et al.Flexibility supply-demand balance in power system with ultra-high proportion of renewable energy[J]. Automation of Electric Power Systems, 2022, 46(16): 3-16.
[3] 中国储能网. 浅析国内电源侧储能利用率问题[EB/OL].[2023-11-11]. https://www.escn.com.cn/20231111/47f4149732c5421e8c34bf25dad6f5b7/c.html.
[4] 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.
[5] 李军徽, 陈国航, 马腾, 等. 高风电渗透率下液流电池储能系统调峰优化控制策略[J]. 发电技术, 2024, 45(3): 434-447.
Li Junhui, Chen Guohang, Ma Teng, et al.Optimal control strategy of peak shaving of flow battery energy storage system under high wind power permeability[J]. Power Generation Technology, 2024, 45(3): 434-447.
[6] 李建林, 牛萌, 周喜超, 等. 能源互联网中微能源系统储能容量规划及投资效益分析[J]. 电工技术学报, 2020, 35(4): 874-884.
Li Jianlin, Niu Meng, Zhou Xichao, et al.Energy storage capacity planning and investment benefit analysis of micro-energy system in energy intercom-nection[J]. Transactions of China Electrotechnical Society, 2020, 35(4): 874-884.
[7] 孔昱凯, 温步瀛, 唐雨晨. 考虑辅助服务含储能区域电网运行优化[J]. 电气技术, 2021, 22(4): 26-32, 77.
Kong Yukai, Wen Buying, Tang Yuchen.Operation optimization for regional grid containing energy storage considering auxiliary service[J]. Electrical Engineering, 2021, 22(4): 26-32, 77.
[8] 段潇涵, 孙丹, 赵琛, 等. 计及调频能力和经济效益的储能集群多状态区间优化策略[J]. 电力系统自动化, 2024, 48(5): 58-67.
Duan Xiaohan, Sun Dan, Zhao Chen, et al.Multi-state interval optimization strategy for energy storage clusters considering frequency regulation ability and economic benefits[J]. Automation of Electric Power Systems, 2024, 48(5): 58-67.
[9] 陈明昊, 朱月瑶, 孙毅, 等. 计及高渗透率光伏消纳与深度强化学习的综合能源系统预测调控方法[J].电工技术学报, 2024, 39(19): 6054-6071.
Chen Minghao, Zhu Yueyao, Sun Yi, et al.The predictive-control optimization method for park integrated energy system considering the high penetration of photovoltaics and deep reinforcement learning[J]. Transactions of China Electrotechnical Society, 2024, 39(19): 6054-6071.
[10] 麻秀范, 王戈, 朱思嘉, 等. 计及风电消纳与发电集团利益的日前协调优化调度[J]. 电工技术学报, 2021, 36(3): 579-587.
Ma Xiufan, Wang Ge, Zhu Sijia, et al.Coordinated day-ahead optimal dispatch considering wind power consumption and the benefits of power generation group[J]. Transactions of China Electrotechnical Society, 2021, 36(3): 579-587.
[11] 李军徽, 张嘉辉, 李翠萍, 等. 参与调峰的储能系统配置方案及经济性分析[J]. 电工技术学报, 2021, 36(19): 4148-4160.
Li Junhui, Zhang Jiahui, Li Cuiping, et al.Configuration scheme and economic analysis of energy storage system participating in grid peak shaving[J]. Transactions of China Electrotechnical Society, 2021, 36(19): 4148-4160.
[12] 李军徽, 张嘉辉, 穆钢, 等. 储能辅助火电机组深度调峰的分层优化调度[J]. 电网技术, 2019, 43(11): 3961-3970.
Li Junhui, Zhang Jiahui, Mu Gang, et al.Hierarchical optimization scheduling of deep peak shaving for energy-storage auxiliary thermal power generating units[J]. Power System Technology, 2019, 43(11): 3961-3970.
[13] 郝文波, 景菲, 颜庆宇, 等. 数据驱动下基于风电场景的多时间尺度调峰调度研究[J]. 电力系统保护与控制, 2023, 51(16): 115-126.
Hao Wenbo, Jing Fei, Yan Qingyu, et al.A multi-time scale peak shaving scheduling strategy based on wind power scenario using a data-driven method[J]. Power System Protection and Control, 2023, 51(16): 115-126.
[14] 时瑞廷, 杨贺钧, 马英浩, 等. 计及峰谷平滑效益的需求响应和电池储能系统调度联合优化策略[J]. 电力自动化设备, 2023, 43(8): 49-55.
Shi Ruiting, Yang Hejun, Ma Yinghao, et al.Joint optimization strategy of demand response and battery energy storage system dispatch considering peak-valley smoothing benefit[J]. Electric Power Automation Equipment, 2023, 43(8): 49-55.
[15] 赵东元, 胡楠, 傅靖, 等. 提升新能源电力系统灵活性的中国实践及发展路径研究[J]. 电力系统保护与控制, 2020, 48(24): 1-8.
Zhao Dongyuan, Hu Nan, Fu Jing, et al.Research on the practice and road map of enhancing the flexibility of a new generation power system in China[J]. Power System Protection and Control, 2020, 48(24): 1-8.
[16] 姜海洋, 杜尔顺, 马佳豪, 等. 考虑长周期供需不平衡风险的新型电力系统规划方法[J]. 中国电机工程学报, 2024, 44(15): 5845-5858.
Jiang Haiyang, Du Ershun, Ma Jiahao, et al.Power system optimal planning method considering long-term lmbalance risk[J]. Proceedings of the CSEE, 2024, 44(15): 5845-5858.
[17] 郑林烽, 缪源诚, 滕晓毕, 等. 考虑配储的火电机组灵活性改造模型与方法[J/OL]. 中国电机工程学报, 2024: 1-14. https://doi.org/10.13334/j.0258-8013.pcsee.231566.
Zheng Linfeng, Liao Yuanchng, Teng Xiaobi, et al. Model and method for flexible retrofit of thermal power units considering energy storage configuration [J]. Proceedings of the CSEE, 2024: 1-14. https://doi.org/10.13334/j.0258-8013.pcsee.231566.
[18] 潘郑楠, 邓长虹, 徐慧慧, 等. 考虑灵活性补偿的高比例风电与多元灵活性资源博弈优化调度[J]. 电工技术学报, 2023, 38(增刊1): 56-69.
Pan Zhengnan, Deng Changhong, Xu Huihui, et al.Game optimization scheduling of high proportion wind power and multiple flexible resources considering flexibility compensation[J]. Transactions of China Electrotechnical Society, 2023, 38(S1): 56-69.
[19] 朱晓荣, 山雨琦. 考虑灵活性的储能容量多阶段分布鲁棒规划[J]. 电力自动化设备, 2023, 43(6): 152-159, 167.
Zhu Xiaorong, Shan Yuqi.Multi-stage distributionally robust planning of energy storage capacity consi-dering flexibility[J]. Electric Power Automation Equipment, 2023, 43(6): 152-159, 167.
[20] 姜云鹏, 任洲洋, 李秋燕, 等. 考虑多灵活性资源协调调度的配电网新能源消纳策略[J]. 电工技术学报, 2022, 37(7): 1820-1835.
Jiang Yunpeng, Ren Zhouyang, Li Qiuyan, et al.An accommodation strategy for renewable energy in distribution network considering coordinated dispatching of multi-flexible resources[J]. Transactions of China Electrotechnical Society, 2022, 37(7): 1820-1835.
[21] 林墨涵, 刘佳, 唐早, 等. 考虑多能耦合共享储能的微网多智能体混合博弈协调优化[J]. 电力系统自动化, 2024, 48(4): 132-141.
Lin Mohan, Liu Jia, Tang Zao, et al.Coordinated optimization of mixed microgrid multi-agent game considering multi-energy coupled shared energy storage[J]. Automation of Electric Power Systems, 2024, 48(4): 132-141.
[22] 王力, 胡佳成, 曾祥君, 等. 基于混合储能的交直流混联微电网功率分级协调控制策略[J]. 电工技术学报, 2024, 39(8): 2311-2324.
Wang Li, Hu Jiacheng, Zeng Xiangjun, et al.Hierarchical coordinated power control strategy for AC-DC hybrid microgrid with hybrid energy storage[J]. Transactions of China Electrotechnical Society, 2024, 2024, 39(8): 2311-2324.
[23] 吴永飞, 包宇庆. 电能型-功率型混合储能日前-日内协同滚动调度策略[J]. 电力系统自动化, 2024, 48(1): 77-87.
Wu Yongfei, Bao Yuqing.Coordinated day-ahead and intra-day rolling scheduling strategy for electricity-power hybrid energy storage[J]. Automation of Electric Power Systems, 2024, 48(1): 77-87.
[24] Hou Jun, Song Ziyou.A hierarchical energy management strategy for hybrid energy storage via vehicle-to-cloud connectivity[J]. Applied Energy, 2020, 257: 113900.
[25] Alharbi H, Bhattacharya K.Stochastic optimal planning of battery energy storage systems for isolated microgrids[J]. IEEE Transactions on Sustainable Energy, 2018, 9(1): 211-227.
[26] 韩晓娟, 程成, 籍天明, 等. 计及电池使用寿命的混合储能系统容量优化模型[J]. 中国电机工程学报, 2013, 33(34): 91-97, 16.
Han Xiaojuan, Cheng Cheng, Ji Tianming, et al.Capacity optimal modeling of hybrid energy storage systems considering battery life[J]. Proceedings of the CSEE, 2013, 33(34): 91-97, 16.