通过移动储能系统调度的三阶段框架增强配电网韧性
Resilience Enhancement of Distribution Grids Through a Three-Stage Framework for Scheduling Mobile Energy Storage Systems
- University of Colorado Boulder（科罗拉多大学博尔德分校）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对极端事件频发，提出三阶段框架调度移动储能系统，通过经济套利、主动定位和动态重定位增强配电网韧性，并采用图神经网络提升计算效率，仿真验证减少失电并加速恢复。
AI中文摘要：
随着极端天气事件等高影响、低概率扰动发生频率的增加，增强电网韧性已成为一项关键优先事项。移动储能系统（MESSs）因其高运行灵活性和快速部署能力，在实现这一目标方面具有巨大潜力。它们可以快速重新定位以供应关键负荷，支持孤岛运行，并适应电网不断变化的条件，使其成为传统韧性提供方法的有前景的补充或替代方案。本文提出了一种用于调度MESS单元的三阶段框架。在第一阶段（正常运行），优化公交站MESS的充放电调度以实现经济套利。在收到早期预警信号后，第二阶段（主动定位）将重点转向韧性。在此阶段，模型确定交通网络中的最优集结点，以最小化到达关键负荷的预期时间，同时考虑线路停运的概率分布。第三阶段（动态重新定位）处理事件后的恢复阶段，在此阶段，MESS根据电网更新（如后续线路故障）进行重新定位。此外，提出了一种基于图神经网络的求解方法，以进一步提高在预分配和重新定位阶段确定MESS单元最优放置位置的计算效率。仿真结果表明，所提出的框架减少了预期未供电能量，并提高了恢复速度。
英文摘要：
As high-impact, low-probability disturbances such as extreme weather events increase in frequency, enhancing power grid resilience has become a critical priority. Mobile Energy Storage Systems (MESSs) have strong potential for helping with this purpose due to their high operational flexibility and fast deployability. They can be rapidly relocated to supply critical loads, support islanded operation, and adapt to changing conditions of the grid, making them a promising addition or alternative to conventional resilience-providing methods. This paper proposes a three-stage framework for scheduling MESS units. In the first stage (Normal Operation), charging/discharging scheduling of MESSs at buses is optimized for economic arbitrage. Upon receiving an early warning signal, the second stage (Proactive Positioning) shifts the focus to resilience. Here, the model determines the optimal staging points within the transportation network to minimize the expected time of arrival at critical loads, considering the probability distribution of line outages. The third stage (Dynamic Relocation) addresses the post-event restoration phase, in which MESSs are relocated in response to grid updates, such as subsequent line failures. Furthermore, a graph neural network-based solution approach is proposed to further enhance computational efficiency in determining the optimal placing of MESS units during the pre-allocation and relocation stages. The simulation results demonstrate that the proposed framework reduces expected energy not served and improves the restoration speed.
