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储能系统技术
★ 5.0
一种新颖的移动式储能预部署鲁棒优化方法
A novel robust optimization method for mobile energy storage pre-positioning
| 作者 | Hening Yuan · Yueqing Shen · Xuehua Xie |
| 期刊 | Applied Energy |
| 出版日期 | 2025年1月 |
| 卷/期 | 第 379 卷 |
| 技术分类 | 储能系统技术 |
| 相关度评分 | ★★★★★ 5.0 / 5.0 |
| 关键词 | Establishing a pre-positioning method for mobile [energy storage systems](https://www.sciencedirect.com/topics/engineering/energy-storage-system "Learn more about energy storage systems from ScienceDirect's AI-generated Topic Pages"). |
语言:
中文摘要
摘要 由于分布式能源的接入,传统的配电网络正在向主动式配电网转型。与此同时,极端天气事件的频发要求电力网络具备更强的韧性。分布式能源,特别是移动式储能系统(MESS),在提升配电网韧性方面发挥着关键作用。然而,目前针对MESS在灾后运行中用于增强韧性、效率及电能资源利用率的预部署研究仍显不足。为解决上述问题,本文提出了一种考虑分布式发电出力不确定性的主动配电网中MESS预部署的主动式方法。首先,对主动配电网中的柔性资源进行建模,包括分布式电源、移动式储能系统和电动汽车。然后,建立了考虑光伏出力不确定性的MESS预部署鲁棒优化模型,并采用大M法与列约束生成算法求解MESS的最优容量与位置配置。最后,分别基于IEEE 33节点系统和IEEE 141节点系统验证了所提MESS预部署模型的有效性。仿真结果表明,与固定MESS接入位置的情形相比,大多数节点的负荷损失显著降低,系统总成本降低了17.65%。此外,结果还显示,在MESS数量较少的情况下,每增加一个MESS单元,总的负荷削减成本约降低20%。同时,所提出的MESS预部署鲁棒优化模型在大规模系统中同样具有良好的有效性。
English Abstract
Abstract The traditional power distribution network is transitioning to an active electrical distribution network due to the integration of distributed energy resources . Simultaneously, the increasing occurrence of extreme weather requires power networks to be more resilient. Distributed energy resources, especially mobile energy storage systems (MESS), play a crucial role in enhancing the resilience of electrical distribution networks. However, research is lacking on pre-positioning of MESS to enhance resilience, efficiency and electrical resource utilization in post-disaster operations. To address these issues, this paper introduces a proactive MESS pre-positioning method in active electrical distribution networks considering the uncertainties of distributed generation output. Firstly, the flexible resources in active distribution networks are modeled, including distributed generation, MESS and Electric Vehicles. Then, a robust optimization model is established for the pre-positioning of MESS considering the PV output uncertainty, where the big-M method and the column constraint generation algorithm are used to calculate the optimal capacity and location of the MESS. Finally, the effectiveness of the MESS pre-positioning model is verified using the IEEE 33-node system and the IEEE 141-node system, respectively. The simulation results show that the load loss at most of the nodes is significantly reduced and the total system cost is reduced by 17.65% compared with the case of fixed MESS access location. The results also show that when the number of MESS is low, each additional MESS unit reduces the total load shedding cost by about 20%. Moreover, the proposed robust optimization model for MESS pre-positioning is also effective in large-scale systems.
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SunView 深度解读
该移动储能预定位鲁棒优化技术对阳光电源PowerTitan移动储能系统及ST系列PCS具有重要应用价值。研究中的分布式资源协同优化模型可直接应用于iSolarCloud平台,实现灾前储能资源智能调度。其鲁棒优化算法可集成到阳光储能EMS系统,结合光伏出力不确定性预测,优化移动储能容量配置与选址决策。研究显示单台移动储能可降低20%负荷削减成本,验证了PowerTitan在应急场景的经济性。该方法可增强阳光充电桩与储能系统协同能力,提升配电网韧性,为综合能源解决方案提供算法支撑。