A computational approach for real-time stochastic recovery of electric power networks during a disaster
A computational approach for real-time stochastic recovery of electric power networks during a disaster
复制标题
DOI：
10.1016/j.tre.2022.102752
复制
发表时间：
2022-07
期刊：
影响因子：
--
通讯作者：
Alireza Inanlouganji;Giulia Pedrielli;T. Reddy;Fernando Tormos Aponte
中科院分区：
文献类型：
--
作者：
Alireza Inanlouganji;Giulia Pedrielli;T. Reddy;Fernando Tormos Aponte
灾害在全世界日益频繁地发生，造成重大社会困难和经济损失。电力网络等关键基础设施在此类事件下容易发生故障，这严重影响了受影响地区人民的日常生活。因此，至关重要的是，这些电力网络的恢复规划要积极主动地进行。电力网络中的灾难响应是一个很好的研究问题，特别是对于事件前和事件后的恢复。类似于事件前，我们考虑与故障路径相关的不确定性，并且我们研究故障发生时的实时响应。在这方面，在每一个时间步，我们移动维修团队向分布负载的基础上，他们的当前状态，他们的可能性失败，并在节点故障的情况下损坏的影响。我们考虑大规模网络（> 50个节点和> 20个修复团队），并提出了一个有效的算法来支持实时恢复。特别是，为了解决维数灾难，我们设计了一种新的近似动态程序，（i）评估未来的影响，当前的行动使用推出，（ii）减少行动空间依赖于聚合动态规划。所提出的方法被应用到配电网在阿瓜达市，波多黎各。我们的研究结果表明，所提出的推出方法显着提高了网络服务水平相比，基地启发式通过预置的维修人员。此外，我们发现，性能差距越来越大的凹恢复功能（即，随着恢复的进展，在负载服务水平的增加率下降）相比，线性恢复（在整个恢复操作的恒定恢复率）。最后，在更强的故障场景下，性能差距也会变得更大。
Disasters are occurring with increasing frequency worldwide, causing significant social hardship and economic losses. Critical infrastructures such as electric power networks are prone to failure under such events, and this significantly impacts the daily lives of people in affected areas. It is hence critical that the restoration planning of these power networks be done proactively. Disaster response in power networks is a well-studied problem, especially for pre-and post-event restoration Similar to pre-event, we consider uncertainty associated with the failure paths, and we look into real-time response while failures are happening. In this regard, at each time step, we move repair teams towards distribution loads based on their current state, their likelihood to fail, and the impact of the damage in case of node failure. We consider large-scale networks (> 50 nodes and> 20 repair teams) and propose an efficient algorithm to support real-time recovery. In particular, to address the curse of dimensionality, we design a novel approximate dynamic program that (i) evaluates the future impact of current actions using rollout,(ii) reduces the action space relying on aggregate dynamic programming. The proposed approach is applied to the power distribution network in Aguada municipality, Puerto Rico. Our results show that the proposed rollout approach significantly improves the network service level compared to the base heuristic through prepositioning of the repair crew. Moreover, we find that the performance gap grows larger with the concave restoration function (ie, a decreasing Rate of Increase in the Load Service Level as the recovery progresses) compared to the linear restoration (a constant recovery rate throughout the recovery operation). Finally, the performance gap also grows larger under stronger failure scenarios.