基于改进典型场景生成的CWPS-TPSS多目标源储协同容量优化及经济性分析

doi:10.19595/j.cnki.1000-6753.tces.251626

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摘要

电气化铁路引入分布式风光储系统是提升经济性与供电可靠性的有效途径。为实现分布式风光储在牵引供电系统中的合理配置与高效利用,并考虑风光储联合牵引供电系统从传统需求侧负荷向主动供电侧参与者的转变,提出一种计及电网与风光储联合牵引供电系统双方交互供电可靠性的双层多目标源储协同容量优化策略。首先,针对风光出力不确定性及规划阶段牵引供电系统区域风光资源实测数据有限的问题,基于带梯度惩罚的Wasserstein生成对抗网络和改进层次聚类算法的场景生成与削减方法,构建多样且可信的风光出力时序场景。其次,建立双层多目标源储容量协同优化配置模型：规划层以最小化日均等年值投资成本为目标,确定风电、光伏、储能及牵引变电所的最优配置规模;运行层则从经济性与可靠性双重视角出发,引入源荷波动率和离网机车平均负荷损失概率作为评价风光储联合牵引供电系统与电网交互的可靠性指标,实现系统运行成本最小化与可靠性指标最优的运行策略优化。算例结果表明,所提方法能够有效支持风光储联合牵引供电系统与电网的协同运行,在合理配置风光储容量的同时兼顾经济性与可靠性。最后,不同方案的经济性分析结果可为工程实践中的经济评估与项目决策提供参考。

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	苏照旭
	田铭兴
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	强发斌
	黄强

关键词 ：风光联合出力场景生成, 风光储联合牵引供电系统, 容量配置, 供电可靠性

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; and (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.

Key words： Joint wind-solar power output scenario generation wind-solar-storage integrated traction power supply system capacity planning power supply reliability

收稿日期: 2025-09-17

PACS:

TM7

基金资助:

国家自然科学基金（52167013）、甘肃省自然科学基金重点项目（24JRRA225）和甘肃省联合基金一般项目（25JRRA1167）资助

通讯作者: 田铭兴男,1968年生,教授,博士生导师,研究方向为电力系统电能质量分析与控制、电机与电器设计及其控制、电力电子技术及其应用等。E-mail：tianmingxing@mail.lzjtu.cn

作者简介: 苏照旭男,1997年生,博士研究生,研究方向多源融合牵引供电系统容量优化配置及调度。E-mail：suzhaoxu@aliyun.com

引用本文:

苏照旭, 田铭兴, 李倩, 强发斌, 黄强. 基于改进典型场景生成的CWPS-TPSS多目标源储协同容量优化及经济性分析[J]. 电工技术学报, 0, (): 20251626-. Su Zhaoxu, Tian Mingxing, Li Qian, Qiang Fabin, Huang Qiang. Multi-Objective Coordinated Source-Storage Capacity Optimization and Economic Analysis of CWPS-TPSS Based on an Improved Scenario Generation Method. Transactions of China Electrotechnical Society, 0, (): 20251626-.

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