基于广义目标级联法的多牵引变电站光伏-储能协同规划配置

doi:10.19595/j.cnki.1000-6753.tces.240164

Abstract
Figure/Table
References
Related Citation (3)

Download: PDF (3686 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract The continuous integration of energy and transportation development is a hot topic. Connecting new energy sources such as photovoltaic (PV) and energy storage equipment to the traction substations in the railroad traction power supply system is one of the means to implement the green integration of energy and transportation and enhance the greening degree of the system. Reasonable deployment and coordinated planning of new energy and energy storage capacity in each traction substation can effectively improve the greening of the longer-distance traction power supply network and the degree of system coordination. Thus, a green energy system for rail transportation can be gradually built up.
Based on the integrated traction power supply architecture and the basic idea of “planning + operation”, a collaborative planning and configuration method for PV and energy storage in multiple traction substations is proposed. The purpose of this method is to complete the one-time and integrated configuration of a multi-traction substation system. The traction load side data are processed. The means of averaging eliminates the drastic volatility of the load and meets fluctuations near the mean value. The time scale can be reduced while ensuring its volatility. A probabilistic model is established to construct the yearly typical day PV output model. According to the hour-by-hour output interval, the probability processing histogram of PV is constructed, and the probability sum of PV output is obtained by stepwise summation. The annual typical daily output curve is finally obtained.
Then, with the economic optimization, a single traction substation optimization planning model is established. The generalized analytical target cascading (G-ATC) method is used to construct a cooperative planning model for multiple traction substations along the same railroad line, by minimum investment and costs operational alongside PV power curtailment expenses of the cooperative planning system. Finally, the operation simulation is carried out. The cooperative planning model of multiple traction substations along the same railway line is constructed, and the cooperative planning configuration is carried out. Then the proposed model and method are verified by taking the freight railroad as an example. By changing the billing method of the interactive power on the contact line, the payback period of the investment cost is calculated.
The proposed model can reasonably and efficiently complete the multi-site new energy-energy storage planning configuration and meet the system operation requirements, providing a feasible solution for the upgrading and greening of the existing traction power supply system.

Key words： Rain transit green energy system traction substation generalized analytical target cascading method photovoltaic and energy storage collaborative planning

Received: 24 January 2024

PACS:	TM715
	U223

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Chen Yanbo
	Liu Yuxiang
	Tian Haoxin
	Zhang Ruixin
	Xu Zitao

Cite this article:

Chen Yanbo,Liu Yuxiang,Tian Haoxin等. Collaborative Planning and Configuration of Photovoltaic and Energy Storage in Multiple Traction Substations Based on Generalized Analytical Target Cascading Method[J]. Transactions of China Electrotechnical Society, 2024, 39(15): 4599-4612.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.240164 OR https://dgjsxb.ces-transaction.com/EN/Y2024/V39/I15/4599

[1] 陈艳波, 田昊欣, 刘宇翔, 等. 计及电动汽车需求响应的高速公路服务区光储充鲁棒优化配置[J/OL]. 中国电机工程学报, 2023, https://doi.org/10.13334/j. 0258-8013.pcsee.231850.
Chen Yanbo, Tian Haoxin, Liu Yuxiang, et al. Robust optimization configuration of photovoltaic-energy storage-charging integrated system in expressway service area considering demand response of electric vehicles[J/OL]. Proceedings of the CSEE, 2023, https://doi.org/10.13334/j.0258-8013.pcsee.231850.
[2] 高仕斌, 罗嘉明, 陈维荣, 等. 轨道交通“网-源-储-车”协同供能技术体系[J/OL]. 西南交通大学学报, 2022, https://kns.cnki.net/kcms/detail/51.1277.U.20221130. 1759.003.html.
Gao Shibin, Luo Jiaming, Chen Weirong, et al. Rail transit ‘network-source-storage-vehicle’ collaborative energy supply technology system[J/OL]. Journal of Southwest Jiaotong University, 2022, https://kns.cnki. net/kcms/detail/51.1277.U.20221130.1759.003.html.
[3] 赵宏程, 李再华, 赖俊宏, 等. 基于RPC的混合储能接入双流制牵引供电系统协调运行方法[J]. 储能科学与技术, 2023, 12(9): 2862-2870.
Zhao Hongcheng, Li Zaihua, Lai Junhong, et al.Coordinated operation method of RPC-based hybrid energy storage access dual-mode traction power supply system[J]. Energy Storage Science and Tech- nology, 2023, 12(9): 2862-2870.
[4] 陈冲, 贾利民, 赵天宇, 等. 去碳化导向的轨道交通与新能源融合发展——形态模式、解决方案和使/赋能技术[J]. 电工技术学报, 2023, 38(12): 3321-3337.
Chen Chong, Jia Limin, Zhao Tianyu, et al.Decarbonization-oriented rail transportation and renewable energy integration development- configu- rations, solutions, and enabling/empowering tech- nologies[J]. Transactions of China Electrotechnical Society, 2023, 38(12): 3321-3337.
[5] 李全生, 卓卉. 基于协同供能的轨道交通能源转型发展路径研究[J]. 北京交通大学学报(社会科学版), 2022, 21(3): 53-60.
Li Quansheng, Zhuo Hui.Research on the development path of rail transit energy based on synergistic energy[J]. Journal of Beijing Jiaotong University (Social Sciences Edition), 2022, 21(3): 53-60.
[6] 成柯. 含光伏与混合储能的高速铁路供电系统能量优化调度方法研究[D]. 兰州: 兰州交通大学, 2023.
Cheng Ke.Study on energy optimization dispatching method of high-speed railway power supply system with photovoltaic and hybrid energy storage system[D]. Lanzhou: Lanzhou Jiaotong University, 2023.
[7] 武沛池. 城市轨道交通中风光储系统的短期能量预测与容量优化配置研究[D]. 南昌: 华东交通大学, 2023.
Wu Peichi.Study on short-term energy prediction and capacity optimal allocation of wind and solar energy storage system in urban rail transit[D]. Nanchang: East China Jiaotong University, 2023.
[8] 胡海涛, 葛银波, 黄毅, 等. 电气化铁路“源-网-车-储”一体化供电技术[J]. 中国电机工程学报, 2022, 42(12): 4374-4391.
Hu Haitao, Ge Yinbo, Huang Yi, et al.“Source- network-train-storage” integrated power supply system for electric railways[J]. Proceedings of the CSEE, 2022, 42(12): 4374-4391.
[9] 罗嘉明, 韦晓广, 高仕斌, 等. 高速铁路储能系统容量配置与能量管理技术综述与展望[J]. 中国电机工程学报, 2022, 42(19): 7028-7050.
Luo Jiaming, Wei Xiaoguang, Gao Shibin, et al.Summary and outlook of capacity configuration and energy management technology of high-speed railway energy storage system[J]. Proceedings of the CSEE, 2022, 42(19): 7028-7050.
[10] 莫少雄. 中压柔性直流铁路牵引供电系统运行域模型[J]. 电气技术, 2022, 23(10): 41-50.
Mo Shaoxiong.Dispatchable region model for medium- voltage flexible direct current railway traction power system[J]. Electrical Engineering, 2022, 23(10): 41-50.
[11] 陈池瑶, 苗世洪, 姚福星, 等. 基于多智能体算法的多微电网-配电网分层协同调度策略[J]. 电力系统自动化, 2023, 47(10): 57-65.
Chen Chiyao, Miao Shihong, Yao Fuxing, et al.Hierarchical cooperative dispatching strategy of multi-microgrid and distribution networks based on multi-agent algorithm[J]. Automation of Electric Power Systems, 2023, 47(10): 57-65.
[12] 董雷, 杨子民, 乔骥, 等. 基于分层约束强化学习的综合能源多微网系统优化调度[J]. 电工技术学报, 2024, 39(5): 1436-1453.
Dong Lei, Yang Zimin, Qiao Ji, et al.Optimal scheduling of integrated energy multi-microgrid system based on hierarchical constraint reinforcement learning[J]. Transactions of China Electrotechnical Society, 2024, 39(5): 1436-1453.
[13] 张后谊. 多微电网系统体系架构建模及能量协同优化管理研究[D]. 重庆: 重庆大学, 2020.
Zhang Houyi.Study on system of systems architecture model and energy cooperative opti- mization management of multi-microgrid systems[D]. Chongqing: Chongqing University, 2020.
[14] 范培潇, 杨军, 温裕鑫, 等. 基于可进化模型预测控制的含电动汽车多微电网智能发电控制策略[J]. 电工技术学报, 2024, 39(3): 699-713.
Fan Peixiao, Yang Jun, Wen Yuxin, et al.A multi microgrid intelligent generation control strategy with electric vehicles based on evolutionary model predictive control[J]. Transactions of China Elec- trotechnical Society. 2024, 39(3): 699-713.
[15] 邢毓华, 侯静茹, 支娜. 功率互济的多独立微电网运行优化及分析[J]. 电工电能新技术, 2022, 41(3): 33-42.
Xing Yuhua, Hou Jingru, Zhi Na.Operation optimization and analysis of independent double microgrid group with power mutual assistance[J]. Advanced Technology of Electrical Engineering and Energy, 2022, 41(3): 33-42.
[16] 唐忠, 田晨, 资容涛. 多微电网互联系统的储能容量配置[J]. 电测与仪表, 2019, 56(4): 95-100.
Tang Zhong, Tian Chen, Zi Rongtao.Energy storage capacity configuration of inter-connected multi- microgrids[J]. Electrical Measurement & Instru- mentation, 2019, 56(4): 95-100.
[17] 李蕊睿, 李奇, 蒲雨辰, 等. 计及功率交互约束的含电-氢混合储能的多微电网系统容量优化配置[J]. 电力系统保护与控制, 2022, 50(14): 53-64.
Li Ruirui, Li Qi, Pu Yuchen, et al.Optimal configuration of an electric-hydrogen hybrid energy storage multi-microgrid system considering power interaction constraints[J]. Power System Protection and Control, 2022, 50(14): 53-64.
[18] 王鲁浩, 李歧强, 丁然, 等. 可再生能源微网鲁棒多目标优化调度[J]. 电工技术学报, 2017, 32(5): 184-192.
Wang Luhao, Li Qiqiang, Ding Ran, et al.Robust multi-objective optimization scheduling of micro- grids with renewable energy[J]. Transactions of China Electrotechnical Society, 2017, 32(5): 184-192.
[19] 欧阳聪, 刘明波, 林舜江, 等. 采用同步型交替方向乘子法的微电网分散式动态经济调度算法[J]. 电工技术学报, 2017, 32(5): 134-142.
Ouyang Cong, Liu Mingbo, Lin Shunjiang, et al.Decentralized dynamic economic dispatch algorithm of microgrids using synchronous alternating direction method of multipliers[J]. Transactions of China Electrotechnical Society, 2017, 32(5): 134-142.
[20] 牵引供电所BJTU. “网-源-储-车”—国内首个光伏供能铁路牵引项目成功实现一期并网[EB/OL]. (2023-12-29). https://mp.weixin.qq.com/s/HyOb9kxZfscw6alKJc6G6w.
Institute of traction power supply BJTU. “Net- Source-Storage-Car”-China’s first photovoltaic energy supply railroad traction project successfully realizes a phase of grid connection [EB/OL](2023-12-29). https://mp.weixin.qq.com/s/HyOb9kxZfscw6alKJc6G6w.
[21] 胡田飞, 刘济华, 李天峰, 等. 铁路与新能源融合发展现状及展望[J]. 中国工程科学, 2023, 25(2): 122-132.
Hu Tianfei, Liu Jihua, Li Tianfeng, et al.Current status and prospect of the integration of railway and new energy[J]. Strategic Study of CAE, 2023, 25(2): 122-132.
[22] Kim H M, Michelena N F, Papalambros P Y, et al.Target cascading in optimal system design[J]. Journal of Mechanical Design, 2003, 125(3): 474-480.
[23] Tosserams S, Etman L F P, Papalambros P Y, et al. An augmented Lagrangian relaxation for analytical target cascading using the alternating direction method of multipliers[J]. Structural and Multi- disciplinary Optimization, 2006, 31(3): 176-189.
[24] 张旋, 张玉敏, 吉兴全, 等. 基于目标级联分析法的输电网结构优化[J]. 电力自动化设备, 2023, 43(2): 218-224.
Zhang Xuan, Zhang Yumin, Ji Xingquan, et al.Optimal transmission switching based on analytical target cascading method[J]. Electric Power Auto- mation Equipment, 2023, 43(2): 218-224.
[25] 罗首权, 丁孝华, 韩韬, 等. 基于目标级联分析法的区域源网荷储系统日前运行优化[J]. 电工电能新技术, 2021, 40(7): 11-19.
Luo Shouquan, Ding Xiaohua, Han Tao, et al.Day- ahead operation optimization of regional scale source network load storage system based on analytical target cascading theory[J]. Advanced Technology of Electrical Engineering and Energy, 2021, 40(7): 11-19.
[26] 戴朝华, 陈维荣, 邓文丽. 电气化铁路牵引供电系统: 光伏发电理论与技术[M]. 北京: 科学出版社, 2020.