计及阶梯式碳交易的牵引供电系统混合储能容量配置

郭文凯; 王果; 闵永智

doi:10.3969/j.issn.0258-2724.20230693

计及阶梯式碳交易的牵引供电系统混合储能容量配置

doi: 10.3969/j.issn.0258-2724.20230693

郭文凯^{1, 2,},
王果^{1, 2, ,},
闵永智^{1, 2}

1.
兰州交通大学自动化与电气工程学院，甘肃兰州 730070
2.
兰州交通大学甘肃省轨道交通电气自动化工程实验室，甘肃兰州 730070

基金项目: 国家自然科学基金项目（52467007）；甘肃省科技项目重点研发计划（22YF7GA146）

详细信息

作者简介:
郭文凯（1997—），男，博士研究生，研究方向为轨道交通电气化，E-mail：937781902@qq.com

通讯作者:
王果（1977—），女，教授，博士，研究方向为电气化铁路电能质量及拓扑研究，E-mail：wangguo@lzjtu.edu.cn

中图分类号: TM7
计量
- 文章访问数: 129
- HTML全文浏览量: 33
- PDF下载量: 23
- 被引次数: 0
出版历程
- 收稿日期: 2023-12-22
- 修回日期: 2024-07-14
- 网络出版日期: 2025-04-17
- 刊出日期: 2024-07-21

Hybrid Energy Storage Capacity Configuration for Traction Power Supply Systems Considering Ladder-Type Carbon Trading Mechanism

GUO Wenkai^{1, 2
,},
WANG Guo^{1, 2
, ,},
MIN Yongzhi^{1, 2}

1.
School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
2.
Rail Transit Electrical Automation Engineering Laboratory of Gansu Province, Lanzhou Jiaotong University, Lanzhou 730070, China

摘要

摘要:
在“双碳”背景下，为推动铁路行业的低碳转型，提出一种以牵引供电系统成本最小为优化目标的混合储能容量配置方法. 首先，考虑多源互补、新能源高效消纳等因素，构建含新能源发电系统、电-氢混合储能系统、牵引供电系统的综合能源系统框架，并给出碳交易市场的交易方案；其次，构建规划-运行模型，其中，规划层确定电-氢混合储能配置方案，运行层引入阶梯式碳交易机制，以计算牵引供电系统的日运行成本；最后，利用改进海鸥优化算法对模型进行求解，结合牵引供电系统与新能源实测数据，验证所提模型的有效性. 结果表明：与仅考虑阶梯式碳交易方案和仅考虑电-氢混合储能方案相比，系统总成本分别降低48%与36%，弃风弃光率则下降11%与3%；与仅考虑阶梯式碳交易搭配单一储能介质（蓄电池或氢储能）方案相比，系统总成本分别降低19%与40%，新能源消纳率则提升4%与6%.
- 牵引供电系统 /
- 阶梯式碳交易机制 /
- 新能源消纳 /
- 电-氢混合储能 /
- 改进海鸥算法
Abstract:
To promote the low-carbon transformation of the railway industry in the context of “carbon peaking and carbon neutrality” , a hybrid energy storage capacity configuration method was proposed, with an optimization objective of minimizing the total cost of the traction power supply system. Firstly, by considering factors such as multi-source complementarity and efficient consumption of new energy, a comprehensive energy system framework integrating new energy generation systems, electric–hydrogen hybrid energy storage systems, and traction power supply systems was constructed, and trading schemes for the carbon trading market were provided. Secondly, a planning–operation model was developed. The electric–hydrogen hybrid energy storage configuration scheme was determined at the planning level, and a ladder-type carbon trading mechanism was introduced at the operation level to calculate the daily operating cost of the traction power supply system. Finally, the improved seagull optimization algorithm was utilized to solve the model, and the actual data of the traction power supply system and new energy were combined to verify the effectiveness of the proposed model. The results show that compared with that of scenarios considering only ladder-type carbon trading schemes or electric–hydrogen hybrid energy storage schemes, the total system cost of the proposed method is reduced by 48% and 36%, respectively, while the renewable energy curtailment rate decreases by 11% and 3%, respectively. Compared with that of scenarios considering only ladder-type carbon trading combined with single energy storage media (battery or hydrogen energy storage), the total system cost is reduced by 19% and 40%, respectively, while the new energy consumption rate is improved by 4% and 6%, respectively.
- traction power supply system /
- ladder-type carbon trading mechanism /
- new energy consumption /
- electric–hydrogen hybrid energy storage /
- improved seagull optimization algorithm

HTML全文

图 1 系统框架

Figure 1. System framework

下载: 全尺寸图片幻灯片

图 2 模型结构

Figure 2. Model structure

下载: 全尺寸图片幻灯片

图 3 系统运行控制策略

Figure 3. Operation and control strategy of the system

下载: 全尺寸图片幻灯片

图 4 模型求解流程

Figure 4. Model solving process

下载: 全尺寸图片幻灯片

图 5 牵引负荷功率

Figure 5. Traction load power

下载: 全尺寸图片幻灯片

图 6 新能源功率

Figure 6. New energy power

下载: 全尺寸图片幻灯片

图 7 混合储能响应结果

Figure 7. Response results of hybrid energy storage

下载: 全尺寸图片幻灯片

图 8 系统功率平衡

Figure 8. System power balance

下载: 全尺寸图片幻灯片

图 9 牵引所功率曲线

Figure 9. Traction substation power curves

下载: 全尺寸图片幻灯片

图 10 碳交易收益与总成本变化情况

Figure 10. Variations in carbon trading benefit and total cost

下载: 全尺寸图片幻灯片

图 11 不同算法模型结果

Figure 11. Results of different algorithm models

下载: 全尺寸图片幻灯片

表 1 牵引所1各部分功率

Table 1. Power of components at traction substation 1 kW

时间	牵引负荷总功率	光伏发电总功率	风力发电总功率	蓄电池总功率	电解槽总功率	燃料电池总功率	弃风弃光总功率	外部电网总功率	再生制动能量回收总功率	控制策略
8 ：10—8 ：30	31203	4137	11862	15204	0	0	0	0	0	E
9 ：30—9 ：35	−8800	5203	4827	−9000	−3600	0	0	0	2570	D
12 ：10—12 ：20	−2016	16804	10951	−13500	−5400	0	8855	0	0	C
12 ：40—12 ：45	16500	11541	7625	−2666	0	0	0	0	0	A
13 ：40—13 ：45	7333	11232	7990	−9000	−2889	0	0	0	0	B
18 ：15—18 ：20	33697	936	7355	9000	0	2400	0	14006	0	G
18 ：25	9460	371	3659	4500	0	930	0	0	0	F

下载: 导出CSV

表 2 不同牵引所运行结果对比

Table 2. Comparison of operation results of different traction substations

牵引供电所	系统总成本/万元	碳交易成本/万元	弃风弃光率/%	外部电网出力占比/%
1	10067	−363	6	6
2	8125	−525	4	2

下载: 导出CSV

表 3 不同牵引所储能配置结果对比

Table 3. Result comparison of different energy storage configurations for traction substations kW

牵引变电所	蓄电池功率	电解槽功率	燃料电池功率
1	4500	1800	1200
2	6300	2400	960

下载: 导出CSV

表 4 不同方案结果对比

Table 4. Result comparison of different schemes

方案	总成本/ 万元	碳交易成本/万元	弃风弃光率/%	外部电网出力占比/%	蓄电池功率/kW	电解槽功率/kW	燃料电池功率/kW
1	19523	1321	17	32	0	0	0
2	12381	−165	10	25	6842	0	0
3	16548	−124	12	23	0	2854	3664
4	15698	0	9	16	3657	886	653
5	10067	−363	6	6	4500	1800	1200

下载: 导出CSV

参考文献(25)

[1]	高攀,方志伟,赵旭. “碳减排”视域下内河流域梯级枢纽联合通航调度优化[J]. 西南交通大学学报,2025,60(2): 308-316 GAO Pan, FANG Zhiwei, ZHAO Xu. Optimization of joint navigation scheduling of cascade hubs in inland river basin from the perspective of carbon emission reduction[J]. Journal of Southwest Jiaotong University, 2025, 60(2): 308-316
[2]	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): 7028-7051. 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): 7028-7051.
[3]	杨凯,胡海涛,陈俊宇,等. 电气化铁路再生制动能量利用系统保护方案研究[J]. 中国电机工程学报,2023,43(24): 9523-9534. YANG Kai, HU Haitao, CHEN Junyu, et al. Study on protection scheme of regenerative braking energy utilization system for electrified railway[J]. Proceedings of the CSEE, 2023, 43(24): 9523-9534.
[4]	HAYASHIYA H, ITAGAKI H, MORITA Y, et al. Potentials, peculiarities and prospects of solar power generation on the railway premises[C]//2012 International Conference on Renewable Energy Research and Applications (ICRERA). Nagasaki: IEEE, 2012: 1-6.
[5]	HAYASHIYA H, IINO Y, TAKAHASHI H, et al. Review of regenerative energy utilization in traction power supply system in Japan: applications of energy storage systems in d.c. traction power supply system[C]//The 43rd Annual Conference of the IEEE Industrial Electronics Society. Beijing: IEEE, 2017: 3918-3923.
[6]	OKUI A, HASE S, SHIGEEDA H, et al. Application of energy storage system for railway transportation in Japan[C]//The 2010 International Power Electronics Conference—ECCE ASIA. Sapporo: IEEE, 2010: 3117-3123.
[7]	朱恒恺. 薄弱电源条件下的牵引供电系统供电能力评估方法研究[D]. 成都:西南交通大学,2022.
[8]	AGUADO J A, SÁNCHEZ RACERO A J, DE LA TORRE S. Optimal operation of electric railways with renewable energy and electric storage systems[J]. IEEE Transactions on Smart Grid, 2018, 9(2): 993-1001. doi: 10.1109/TSG.2016.2574200
[9]	闵永智,成柯,袁佳歆,等. 储能接入电气化铁路的拓扑及控制策略研究综述[J]. 高电压技术,2023,49(10): 4069-4083. MIN Yongzhi, CHENG Ke, YUAN Jiaxin, et al. Research review on topology and control strategy of energy storage connected to electrified railway[J]. High Voltage Engineering, 2023, 49(10): 4069-4083.
[10]	杨健维,冯素华,郭惠斌,等. 多变电所互联牵引供电系统光储容量优化配置[J/OL]. 西南交通大学学报,2024:1-11(2024-05-10)[2024-06-10]. http://kns.cnki.net/kcms/detail/51.1277.u.20240509.1711.012.html.
[11]	张丽艳,贾瑛,韩笃硕,等. 电气化铁路同相储能供电系统能量管理及容量配置策略[J]. 西南交通大学学报,2023,58(1): 22-29. doi: 10.3969/j.issn.0258-2724.20210247 ZHANG Liyan, JIA Ying, HAN Dushuo, et al. Energy management and capacity allocation scheme for co-phase traction power supply and energy storage system in electrified railways[J]. Journal of Southwest Jiaotong University, 2023, 58(1): 22-29. doi: 10.3969/j.issn.0258-2724.20210247
[12]	郝婷,樊小朝,王维庆,等. 阶梯式碳交易下考虑源荷不确定性的储能优化配置[J]. 电力系统保护与控制,2023,51(1): 101-112. HAO Ting, FAN Xiaochao, WANG Weiqing, et al. Optimal configuration of energy storage considering the source-load uncertainty under ladder-type carbon trading[J]. Power System Protection and Control, 2023, 51(1): 101-112.
[13]	陈曦,袁梦玲,王松,等. 考虑碳交易影响风电消纳的综合能源系统优化运行[J]. 重庆理工大学学报(自然科学),2022,36(1): 268-276. CHEN Xi, YUAN Mengling, WANG Song, et al. Optimal scheduling of integrated energy system considering impact of carbon trading on wind power consumption[J]. Journal of Chongqing University of Technology (Natural Science), 2022, 36(1): 268-276.
[14]	陈锦鹏,胡志坚,陈嘉滨,等. 考虑阶梯式碳交易与供需灵活双响应的综合能源系统优化调度[J]. 高电压技术,2021,47(9): 3094-3106. CHEN Jinpeng, HU Zhijian, CHEN Jiabin, et al. Optimal dispatch of integrated energy system considering ladder-type carbon trading and flexible double response of supply and demand[J]. High Voltage Engineering, 2021, 47(9): 3094-3106.
[15]	王俐英,林嘉琳,董厚琦,等. 计及阶梯式碳交易的综合能源系统优化调度[J]. 系统仿真学报,2022,34(7): 1393-1404. WANG Liying, LIN Jialin, DONG Houqi, et al. Optimal dispatch of integrated energy system considering ladder-type carbon trading[J]. Journal of System Simulation, 2022, 34(7): 1393-1404.
[16]	林森,文书礼,朱淼,等. 考虑碳交易机制的海港综合能源系统电-热混合储能优化配置[J]. 上海交通大学学报,2024,58(9): 1344-1356. LIN Sen, WEN Shuli, ZHU Miao, et al. Optimal allocation of electric-thermal hybrid energy storage for seaport integrated energy system considering carbon trading mechanism[J]. Journal of Shanghai Jiao Tong University, 2024, 58(9): 1344-1356.
[17]	李奇,邹雪俐,蒲雨辰,等. 基于氢储能的热电联供型微电网优化调度方法[J]. 西南交通大学学报,2023,58(1): 9-21. doi: 10.3969/j.issn.0258-2724.20210348 LI Qi, ZOU Xueli, PU Yuchen, et al. Optimal schedule of combined heat-power microgrid based on hydrogen energy storage[J]. Journal of Southwest Jiaotong University, 2023, 58(1): 9-21. doi: 10.3969/j.issn.0258-2724.20210348
[18]	崔焕影. 碳排放权配额分配与交易定价研究[D]. 成都:西南交通大学,2018.
[19]	刘佳玲,秦博宇,孙颖,等. 面向清洁低碳转型的隧道智慧能源系统框架设计及储能容量优化配置[J]. 高电压技术,2022,48(7): 2563-2572. LIU Jialing, QIN Boyu, SUN Ying, et al. Framework design of tunnel intelligent power system and optimal planning method of energy storage capacity for low carbon transition[J]. High Voltage Engineering, 2022, 48(7): 2563-2572.
[20]	袁铁江,郭建华,杨紫娟,等. 平抑风电波动的电-氢混合储能容量优化配置[J]. 中国电机工程学报,2024,44(4): 1397-1406. YUAN Tiejiang, GUO Jianhua, YANG Zijuan, et al. Optimal allocation of power electric-hydrogen hybrid energy storage of stabilizing wind power fluctuation[J]. Proceedings of the CSEE, 2024, 44(4): 1397-1406.
[21]	李群湛,王喜军,黄小红,等. 电气化铁路飞轮储能技术研究[J]. 中国电机工程学报,2019,39(7): 2025-2033. LI Qunzhan, WANG Xijun, HUANG Xiaohong, et al. Research on flywheel energy storage technology for electrified railway[J]. Proceedings of the CSEE, 2019, 39(7): 2025-2033.
[22]	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 Technology, 2023, 12(9): 2862-2870.
[23]	陈超泉,王宇涵,谢晓兰,等. 融合麻雀搜索机制的改进混沌海鸥优化算法[J]. 科学技术与工程,2023,23(10): 4259-4271. doi: 10.12404/j.issn.1671-1815.2023.23.10.04259 CHEN Chaoquan, WANG Yuhan, XIE Xiaolan, et al. An improved chaotic seagull optimization algorithm incorporating sparrow search mechanism[J]. Science Technology and Engineering, 2023, 23(10): 4259-4271. doi: 10.12404/j.issn.1671-1815.2023.23.10.04259
[24]	冯飞波,闫兴德,郑宝强,等. 蓄电-氢储混合储能系统的配电网双层优化[J]. 系统仿真学报,2022,34(7): 1405-1416. FENG Feibo, YAN Xingde, ZHENG Baoqiang, et al. Bi-level optimization of distribution network for hybrid energy storage system of storage battery and hydrogen storage[J]. Journal of System Simulation, 2022, 34(7): 1405-1416.
[25]	谢鹏,蔡泽祥,刘平,等. 考虑多时间尺度不确定性耦合影响的风光储微电网系统储能容量协同优化[J]. 中国电机工程学报,2019,39(24): 7126-7136,7486. XIE Peng, CAI Zexiang, LIU Ping, et al. Cooperative optimization of energy storage capacity for renewable and storage involved microgrids considering multi time scale uncertainty coupling influence[J]. Proceedings of the CSEE, 2019, 39(24): 7126-7136,7486.