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| Coordinated Source-Storage Capacity Optimization of a Wind-Photovoltaic-Storage Traction Power Supply System Based on Improved Typical Scenarios |
| Su Zhaoxu1, Tian Mingxing1, Li Qian2, Qiang Fabin3, Huang Qiang4 |
1. School of Automation & Electrical Engineering Lanzhou Jiaotong University Lanzhou 730070 China;
2. School of Traffic and Transportation Engineering Central South University Changsha 410075 China;
3. Gansu Institute of Urban Planning and Design Co. Ltd Lanzhou 730030 China;
4. Gansu Water Resources and Hydroelectric Investigation & Design Institute Co. Ltd Lanzhou 730030 China |
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Abstract The integration of distributed renewable energy sources into traction power supply systems represents a key step toward realizing low-carbon and resilient electrified railways. To enhance both the economic efficiency and supply reliability of such systems, this study investigates the coordinated planning and operation of a combined wind-photovoltaic-storage traction power supply system (CWPS-TPSS). The main objective is to determine the optimal configuration of renewable and storage capacities under uncertain operating conditions and to quantitatively assess the bidirectional reliability between the CWPS-TPSS and the external grid.
To capture the stochastic characteristics of renewable energy generation, a data-driven scenario generation and reduction framework is developed. Specifically, a Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is employed to learn the complex temporal correlations and non-Gaussian features of wind and photovoltaic (PV) power outputs, generating diverse and physically consistent scenarios that reflect the variability of renewable resources. Subsequently, an improved hierarchical clustering algorithm based on probabilistic distance metrics is designed to perform scenario reduction, ensuring that the reduced set maintains statistical representativeness while significantly decreasing computational complexity during the optimization process. This hybrid approach overcomes the limitations of conventional sampling methods that often rely on limited historical data and linear assumptions.
On this basis, a bi-level multi-objective coordinated capacity optimization model is constructed. The upper-level model addresses long-term capacity planning by minimizing the daily equivalent annualized investment cost, optimizing the capacities of wind turbines, PV arrays, energy storage systems (ESS), and traction substations subject to technical, reliability, and power balance constraints. The lower-level model describes short-term operational dispatch under varying renewable and traction load conditions. It minimizes the system’s operational cost while accounting for reliability indices that characterize the dynamic interaction between the CWPS-TPSS and the main grid. Two reliability indicators are defined: (1) The source-load fluctuation rate, quantifying the temporal mismatch between renewable generation and traction demand. (2) The average locomotive load loss probability during islanded operation, measuring the risk of supply interruption under grid faults. The coordination between the two levels is achieved through iterative information exchange until convergence in both investment and operational decisions is attained.
The proposed framework is applied to a representative electrified railway corridor equipped with wind, PV, and ESS units. Simulation results show that the integrated optimization method yields a rational allocation of source and storage capacities that effectively balances cost, reliability, and renewable utilization. Compared with traditional single-objective or independent subsystem designs, the coordinated bi-level strategy achieves smoother power interactions with the external grid and a lower reliance on grid-side supply support. Under the obtained optimal configuration, the total system cost is significantly reduced while the reliability indicators satisfy operational constraints. Sensitivity analyses are further conducted to evaluate the effects of renewable resource availability, storage cost, and reliability weighting factors on the capacity allocation results. The findings indicate that increasing storage capacity enhances the system’s ability to accommodate renewable fluctuations and reduce locomotive load loss, although with diminishing marginal benefits beyond a certain threshold.
The study demonstrates that combining advanced scenario modeling techniques with hierarchical multi-objective optimization provides an effective pathway for the planning and operation of renewable-integrated traction power systems. The modeling approach and quantitative indicators developed herein can be generalized to other railway electrification projects and distributed energy systems that require simultaneous consideration of investment economy and power-supply reliability. Future work will focus on extending the framework to include carbon trading mechanisms, real-time dispatch under uncertainty, and resilience evaluation under extreme operating conditions.
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Received: 17 September 2025
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[1] 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.Decarbon-ization-oriented rail transportation and renewable energy integration development: configurations, solutions, and enabling/empowering technologies[J]. Transactions of China Electrotechnical Society, 2023, 38(12): 3321-3337.
[2] 陈艳波, 刘宇翔, 田昊欣, 等. 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[J]. 电工技术学报, 2024, 39(15): 4599-4612.
Chen Yanbo, Liu Yuxiang, Tian Haoxin, et al.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.
[3] Li Junhao, Guo Qi, Wang Xin, et al.Parallel-execute-based real-time energy management strategy for FTPSS integrated PV and ESS[J]. Applied Energy, 2025, 385: 125538.
[4] 李欣, 朱成琨. 碳视角下基于网-车-线耦合的高速列车节能运行优化[J]. 电工技术学报, 2025, 40(22): 7431-7448.
Li Xin, Zhu Chengkun.Energy-efficient operation optimization of high-speed trains based on network-train-line coupling under carbon perspective[J]. Transactions of China Electrotechnical Society, 2025, 40(22): 7431-7448 .
[5] 陈冲, 贾利民, 赵天宇, 等. 光伏和储能植入铁路牵引供电系统的拓扑架构与控制策略研究综述[J]. 电工技术学报, 2024, 39(24): 7874-7901.
Chen Chong, Jia Limin, Zhao Tianyu, et al.Research review on topology and control strategy of PV and energy storage connected to railway traction power supply systems[J]. Transactions of China Electro-technical Society, 2024, 39(24): 7874-7901.
[6] 李欣, 马学东, 赵天阳. 考虑再生制动能量不确定性的光储两相接入牵引供电系统能量优化调度[J]. 高电压技术, 2025, 51(7): 3519-3528.
Li Xin, Ma Xuedong, Zhao Tianyang.Energy optimization scheduling of photovoltaic-storage two-phase integrated traction power supply system considering the uncertainty of regenerative braking energy[J]. High Voltage Engineering, 2025, 51(7): 3519-3528.
[7] Kano N, Tian Zhongbei, Chinomi N, et al.Renewable sources and energy storage optimization to minimize the global costs of railways[J]. IEEE Transactions on Vehicular Technology, 2023, 74(5): 7049-7060.
[8] Ying Yichen, Tian Zhongbei, Wu Mingli, et al.Capacity configuration method of flexible smart traction power supply system based on double-layer optimization[J]. IEEE Transactions on Transportation Electrification, 2023, 9(3): 4571-4582.
[9] Yang Ruizhang, Li Yujia, Xiao Zhuang, et al.Coordinative operating strategy of grid-interactive FTPSS-HST for cost-efficient operation[J]. IEEE Transactions on Smart Grid, 2024, 16(1): 146-163.
[10] Chen Minwu, Gong Xin, Liang Zongyou, et al.Bi-hierarchy capacity programming of co-phase TPSS with PV and HESS for minimum life cycle cost[J]. International Journal of Electrical Power & Energy Systems, 2023, 147: 108904.
[11] Chen Minwu, Liang Zongyou, Cheng Zhe, et al.Optimal scheduling of FTPSS with PV and HESS considering the online degradation of battery capacity[J]. IEEE Transactions on Transportation Electrification, 2021, 8(1): 936-947.
[12] 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]. 西南交通大学学报, 2025, 60(6): 1537-1549.
Yang Jianwei, Feng Suhua, Guo Huibin, et al.Optimal configuration of photovoltaic energy storage capacity in multi-substation interconnected traction power supply system[J]. Journal of Southwest Jiaotong University, 2025, 60(6): 1537-1549.
[13] Liu Guodong, Ferrari M, Ollis B, et al.Resilient microgrid scheduling considering stochastic chance-constrained islanding capability[C]//2023 IEEE Power & Energy Society General Meeting (PESGM), Orlando, FL, USA, 2023: 1-5.
[14] Yang Mao, Sun Li, Wang Jinxin.Multi-objective optimization scheduling considering the operation performance of islanded microgrid[J]. IEEE Access, 2020, 8: 83405-83413.
[15] Liu Guodong, Ben Ollis T, Zhang Yichen, et al.Robust microgrid scheduling with resiliency considerations[J]. IEEE Access, 2020, 8: 153169-153182.
[16] Chu Zhongda, Zhang Ning, Teng Fei.Frequency-constrained resilient scheduling of microgrid: a distributionally robust approach[J]. IEEE Transactions on Smart Grid, 2021, 12(6): 4914-4925.
[17] Keshavarzi M D, Ali M H.Disturbance resilience enhancement of islanded hybrid microgrid under high penetration of renewable energy resources by BESS[C]//2020 IEEE/PES Transmission and Distribution Conference and Exposition (T&D), Chicago, IL, USA, 2020: 1-5.
[18] Younesi A, Wang Zongjie, Dorado-Rojas S A, et al. Quantification of DERs penetration level in microgrids: a quest for enhancing short-term power grid resilience[C]//2023 IEEE Power & Energy Society General Meeting (PESGM), Orlando, FL, USA, 2023: 1-5.
[19] Chen Yize, Wang Yishen, Kirschen D, et al.Model-free renewable scenario generation using generative adversarial networks[C]//2019 IEEE Power & Energy Society General Meeting (PESGM), Atlanta, GA, USA, 2019: 1.
[20] 马燕峰, 傅钰, 赵书强, 等. 基于WGAN风光资源场景模拟和时序生产模拟的新能源电源容量配置[J]. 电力自动化设备, 2020, 40(11): 77-86.
Ma Yanfeng, Fu Yu, Zhao Shuqiang, et al.Capacity allocation of new energy source based on wind and solar resource scenario simulation using WGAN and sequential production simulation[J]. Electric Power Automation Equipment, 2020, 40(11): 77-86.
[21] 王彦哲, 周胜, 王宇, 等. 中国核电和其他电力技术环境影响综合评价[J]. 清华大学学报(自然科学版), 2021, 61(4): 377-384.
Wang Yanzhe, Zhou Sheng, Wang Yu, et al.Comprehensive assessment of the environmental impact of China’s nuclear and other power generation technologies[J]. Journal of Tsinghua University (Science and Technology), 2021, 61(4): 377-384.
[22] 陈聿, 田博今, 彭云竹, 等. 联合手肘法和期望最大化的高斯混合聚类电力系统客户分群算法[J]. 计算机应用, 2020, 40(11): 3217-3223.
Chen Yu, Tian Bojin, Peng Yunzhu, et al.Gaussian mixture clustering algorithm combining elbow method and expectation-maximization for power system customer segmentation[J]. Journal of Computer Applications, 2020, 40(11): 3217-3223.
[23] 孙文浩, 张乔, 刘志刚, 等. 考虑高海拔山区铁路沿线电网灵活性的分布鲁棒优化方法研究[J]. 电网技术, 2023, 47(6): 2485-2497.
Sun Wenhao, Zhang Qiao, Liu Zhigang, et al.A distributionally robust optimization method considering the flexibility of power grid along high altitude mountain railway[J]. Power System Technology, 2023, 47(6): 2485-2497.
[24] Chen Xing, Li Kang, Zhang Li, et al.Robust optimization of energy-saving train trajectories under passenger load uncertainty based on p-NSGA-II[J]. IEEE Transactions on Transportation Electrification, 2023, 9(1): 1826-1844.
[25] Hamisu Umar N, Bora B, Banerjee C, et al.Performance and economic viability of the PV system in different climatic zones of Nigeria[J]. Sustainable Energy Technologies and Assessments, 2021, 43: 100987. |
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2026, Vol. 41 
