RAMPING FLEXIBILITY COLLABORATIVE PLANNING OF HIGH PROPORTION OF RENEWABLE ENERGY POWER SYSTEM CONSIDERING LIFESPAN DYNAMIC INVESTMENT OF MULTI-TYPES ENERGY STORAGE

Cao Fang; Wang Chunyi; Zheng Jinzhao

doi:10.19912/j.0254-0096.tynxb.2024-0983

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (10) : 45-55. DOI: 10.19912/j.0254-0096.tynxb.2024-0983

RAMPING FLEXIBILITY COLLABORATIVE PLANNING OF HIGH PROPORTION OF RENEWABLE ENERGY POWER SYSTEM CONSIDERING LIFESPAN DYNAMIC INVESTMENT OF MULTI-TYPES ENERGY STORAGE

Cao Fang¹, Wang Chunyi¹, Zheng Jinzhao²

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Abstract

In order to further improve the power system ramping flexibility, this paper puts forward the method of ramping flexibility collaborative planning of high proportion of renewable energy power system considering lifespan dynamic investment of multi-types energy storage. Firstly, considering that the maximum charging and discharging power and state of charge （SOC） characteristics of energy storage shows obvious deterioration over their service lifespan, additional capacity needs to be installed each year during subsequent operation, therefore, this paper established a lifespan deterioration model for energy storage based on experimental data and proposed a dynamic investment allocation method that takes into account the additional investment during the lifespan. Secondly, based on the classification of energy storage by charging and discharging speed and unit capacity investment, a multi-type energy storage and coal fired power plant transformation integrated ramping flexibility coordinated planning and configuration model was established for high proportion of new energy systems. Thirdly, based on the ramping requirements of different types of energy storage and coal fired power plant transformation, the large-scale multi-variable model was divided into multiple stages of small-scale solution processes, in which the unknown variables were solved in each stage. Finally, the IEEE 30-node system was used as an example to verify the correctness and effectiveness of the proposed model.

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new energy / energy storage / investment / planning / deterioration / ramping flexibility / phased solution

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Cao Fang, Wang Chunyi, Zheng Jinzhao. RAMPING FLEXIBILITY COLLABORATIVE PLANNING OF HIGH PROPORTION OF RENEWABLE ENERGY POWER SYSTEM CONSIDERING LIFESPAN DYNAMIC INVESTMENT OF MULTI-TYPES ENERGY STORAGE[J]. Acta Energiae Solaris Sinica. 2025, 46(10): 45-55 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0983

References

[1] 龚银, 刘煜. 高比例可再生能源渗透电力系统关键技术研究[J]. 电气技术与经济,2024 (2): 104-106, 109.
GONG Y, LIU Y.Research on key technologies of high proportion renewable energy penetrating power system[J]. Electrical equipment and economy, 2024(2): 104-106, 109.
[2] 自然资源保护协会、华北电力大学.电力系统灵活性提升:技术路径、经济性与政策建议[R]. 2022.
Natural Resources Defense Council、North China Electric Power University.Improving Power System Flexibility: Technical Pathways, Economic Analysis, and Policy Recommendations[R]. 2022.
[3] 李杨, 孙斌, 吴峰, 等.考虑动态频率约束的多能源电力系统日前-日内优化调度[J]. 太阳能学报, 2024, 45(2): 406-415.
LI Y,SUN B,WU F, et al.Day-ahead and intra-day optimal scheduling of multi-energy power systems considering dynamic frequency constraints[J]. Acta energiae solaris sinica, 2024, 45(2): 406-415.
[4] 梁璐, 洪烽, 季卫鸣, 等. 大规模飞轮储能阵列协同火电机组调频的能量管理系统[J]. 中国电机工程学报, 2025, 45(7): 2658-2669.
LIANG L,HONG F,JI W M, et al.Energy management system of large-scale flywheel energy storage array coordinated with thermal power unit frequency regulation[J]. Proceedings of the CSEE, 2025, 45(7): 2658-2669.
[5] 虞启辉, 高胜昱, 孙国鑫, 等. 基于风光互补发电系统的压缩空气混合储能系统容量优化[J]. 新能源进展, 2024, 12(1): 74-81.
YU Q H,GAO S Y,SUN G X, et al.Capacity optimization of compressed air hybrid energy storage system based on wind-solar complementary power generation system[J].Advances in new and renewable energy, 2024, 12(1): 74-81.
[6] 张浚坤, 雷二涛, 韩蓉, 等. 计及转化效率的电化学储能电站日运行策略研究[J]. 太阳能学报, 2024, 45(5): 198-205.
ZHANG J K, LEI E T, HAN R, et al.Study on operation strategy of electrochemical energy storage station with calculation and efficiency conversion[J]. Acta energiae solaris sinica, 2024, 45(5): 198-205.
[7] 刘道兵, 李珏岑, 齐越, 等.考虑碳效益和运行策略的风电场储能优化配置[J].太阳能学报, 2025, 46(2): 664-675.
LIU D B,LI J C, QI Y, et al.Wind farm energy storage optimization considering carbon benefit and operation strategy[J].Acta energiae solaris sinica, 2025, 46(2): 664-675.
[8] 王威, 谢丽蓉, 张琦, 等.基于优化赋权模型预测控制的风储协调策略研究[J]. 太阳能学报, 2024, 45(5): 260-266.
WANG W, XIE L R, ZHANG Q, et al.Research on wind storage coordination strategy based on predictive control of optimization empowerment model[J]. Acta energiae solaris sinica, 2024, 45(5): 260-266.
[9] 桑丙玉, 杨波, 李克成, 等.模型差异性混合储能系统建模及功率分配[J]. 太阳能学报, 2024, 45(1): 433-441.
SANG B Y, YANG B, LI K C, et al.Model difference hybrid energy storage system modeling and power distribution strategy[J]. Acta energiae solaris sinica, 2024, 45(1): 433-441.
[10] HU X S, JIANG H F, FENG F, et al.An enhanced multi-state estimation hierarchy for advanced lithium-ion battery management[J]. Applied energy, 2020, 257: 114019.
[11] 谢翌, 江迪生, 张扬军, 等. 新能源汽车锂离子电池组SOC-SOP联合估计算法[J]. 汽车安全与节能学报, 2022, 13(3): 580-589.
XIE Y, JIANG D S, ZHANG Y J, et al.Joint estimation algorithm of SOC-SOP for lithium-ion battery pack in new energy vehicles[J]. Journal of automotive safety and energy, 2022, 13(3): 580-589.
[12] 吴雄, 贺明康, 何雯雯, 等. 考虑储能寿命的风-光-火-储打捆外送系统容量优化配置[J]. 电力系统保护与控制, 2023, 51(15): 66-75.
WU X, HE M K, HE W W, et al.Optimal capacity of a wind-solar-thermo-storage-bundled power transmission system considering battery life[J]. Power system protection and control, 2023, 51(15): 66-75.
[13] 邵泽广, 李永丽, 李怡, 等. 计及混合储能寿命损耗的光伏功率平抑策略[J]. 电力系统及其自动化学报, 2024, 36(2): 97-104.
SHAO Z G, LI Y L, LI Y, et al.Photovoltaic power smoothing strategy considering life loss of hybrid energy storage[J]. Proceedings of the CSU-EPSA, 2024, 36(2): 97-104.
[14] 刘凡, 李凤婷, 张高航, 等.计及循环寿命和运营策略的风电汇集区域储能电站优化配置[J]. 电力系统保护与控制, 2023, 51(8): 127-139.
LIU F, LI F T, ZHANG G H, et al.Optimal configuration of storage power stations in wind power gathering area considering cycle life and operation strategy[J]. Power system protection and control, 2023, 51(8): 127-139.
[15] 孙一飞. 满足新型电力系统特性的爬坡辅助服务市场机制研究[D]. 北京:华北电力大学,2023.
SUN Y F.Research on the market mechanism of climbing auxiliary service to meet the characteristics of new power system[D]. Beijing: North China Electric Power University, 2023.
[16] 李霞, 陈景文, 周光荣, 等. 基于半经验法的锂离子电池衰减模型[J]. 计算机与数字工程, 2024, 52(2): 578-583.
LI X, CHEN J W, ZHOU G R, et al.Decay model of lithium-ion battery based on semi-empirical method[J].Computer & digital engineering, 2024, 52(2): 578-583.
[17] Advanced Life Cycle Engineering(CALCE),University of Maryland,2010.https://calce.umd.edu/battery-data.
[18] 程苒, 段瑶, 吴亚雄, 等. 考虑等效循环放电深度约束的配电网储能规划[J]. 南方电网技术, 2024(10): 54-63.
CHENG R,DUAN Y, WU Y X, et al.Energy storage planning of distribution network considering the constraint of equivalent cyclic discharge depth constraints[J]. Southern power system technology, 2024(10): 54-63.
[19] ESTER M,KRIEGEL H P,SANDER J,et al.A density-based algorithm for discovering clusters in large spatial databases with noise[C]//Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining. Portland: AAAI Press, 1996: 226-231.
[20] 闫强强, 张敏, 荀亚玲. 采用有效邻近点和适应密度的密度聚类算法[J]. 计算机技术与发展, 2022, 32(9): 14-22.
YAN Q Q,ZHANG M,XUN Y L.A density clustering algorithm based on effective neighboring points and adaptive density distribution[J]. Computer technology and development, 2022, 32(9): 14-22.
[21] 汪鹏, 刘泽玲, 王利琴, 等. 基于PB-DBSCAN的GPS数据去噪[J]. 计算机工程与设计, 2021, 42(3): 678-683.
WANG P,LIU Z L,WANG L Q, et al.GPS data denoising based on PB-DBSCAN[J]. Computer engineering and design, 2021, 42(3): 678-683.
[22] 赵小磊, 姚良忠, 刘运鑫, 等.面向储能多应用价值协同的风光储系统日前优化调度方法[J]. 高电压技术, 2024, 50(6): 2511-2522.
ZHAO X L, YAO L Z, LIU Y X, et al.Day-ahead optimal dispatch for wind-photovoltaic-storage system considering synergistic multi-application value of energy storage[J].High voltage engineering, 2024, 50(6): 2511-2522.
[23] 魏乐, 李承霖, 房方, 等. 小样本下基于改进麻雀算法优化卷积神经网络的飞轮储能系统损耗[J]. 电网技术, 2025, 49(1): 366-372.
WEI Y, LI C L, FANG F, et al.Optimization of convolutional neural network based on improved sparrow algorithm for flywheel energy storage system loss in small sample[J]. Power system technology, 2025, 49(1): 366-372.
[24] 田博文, 张志禹, 杨梦飞. 基于多次滑动均值滤波的混合储能功率分配与定容研究[J]. 电工技术学报, 2024, 39(5): 1548-1564.
TIAN B W, ZHANG Z Y, YANG M F.Research on hybrid energy storage power allocation and capacity determination based on multiple moving average filtering[J]. Transactions of China Electrotechnical Society,2024, 39(5): 1548-1564.