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一种考虑扩散性不确定性的铁路移动储能 resilient 机组组合两阶段鲁棒方法
A Two-Stage Robust Approach for Resilient Unit Commitment With Rail-Based Mobile Energy Storage Under Diffusional Uncertainties
| 作者 | Xiang Yang · Xinghua Liu · Tianyang Zhao · Zhonggang Yin · Gaoxi Xiao · Bangji Fan |
| 期刊 | IEEE Transactions on Sustainable Energy |
| 出版日期 | 2024年12月 |
| 技术分类 | 储能系统技术 |
| 技术标签 | 储能系统 SiC器件 可靠性分析 |
| 相关度评分 | ★★★★★ 5.0 / 5.0 |
| 关键词 | 铁路移动储能 时空网络模型 两阶段鲁棒管理方案 嵌套列与约束生成算法 电力系统韧性 |
语言:
中文摘要
基于铁路的移动储能(RMES)为提升电力系统韧性提供了有效手段。本文提出扩展的时间-空间网络(TSN)模型,刻画飓风对铁路运输网络的影响。针对输电线路与铁路在灾害中随机故障的扩散性不确定性,构建两阶段鲁棒优化模型,协调电力系统与铁路网联合运行,最小化最恶劣场景下的运行成本。第一阶段决策机组启停与RMES预置位置,第二阶段基于实际不确定性调整RMES充放电行为以恢复负荷。设计并验证了一种改进的嵌套列与约束生成(N-C&CG)算法,在IEEE RTS系统与6节点铁路网耦合案例中表明,所提RMES策略能有效利用移动性增强系统韧性,且该算法具备良好的求解效率与适应性。
English Abstract
Rail-based mobile energy storage (RMES) provides promising solutions to enhance power system resilience. This paper proposes an extended time-space network (TSN) model to characterize the impact of hurricanes on the rail-based transportation network (RTN). To coordinate the joint operation of the power transmission network and the RTN under a hurricane while considering the diffusional uncertainties about the random failures of the transmission lines and the transportation railway in both systems, a two-stage robust management scheme is presented to minimize the worst-case operating costs of the systems. Specifically, the first stage determines the pre-disaster on/off state of the generator and the pre-planned location of the RMES. In the second stage, the actual charging and discharging actions of RMES based on the proposed extended TSN model will be adjusted by the uncertainty realities to restore the load supply. To solve the developed scheduling model, a customized nested column-and-constraint generation (N-C&CG) algorithm is designed and validated on the IEEE reliability test system (RTS) with a 6-node railway network under hurricane. Case studies illustrate that the proposed RMES strategy can effectively improve power system resilience by exploiting mobility. Compared to classical approaches, the customized nested C&CG algorithm possesses strong sensing capability and computational efficiency.
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SunView 深度解读
该铁路移动储能鲁棒调度技术对阳光电源PowerTitan大型储能系统和ST系列储能变流器具有重要应用价值。文章提出的两阶段鲁棒优化框架可直接应用于阳光电源移动储能解决方案,通过时间-空间网络模型优化储能系统在极端天气下的预部署策略与实时调度,提升电网韧性。扩散性不确定性建模方法可集成到iSolarCloud云平台的智能调度模块,增强预测性维护能力。N-C&CG算法为多场景下储能系统充放电策略优化提供高效求解工具,特别适用于阳光电源在海外飓风多发地区(如美国、东南亚)的大型储能项目,可显著降低极端灾害下的负荷损失,提升ESS集成方案的市场竞争力。