不确定条件下弹性增强的风险规避优化

Jiaxin Wu, Pingfeng Wang
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引用次数: 1

摘要

随着复杂性和广度的增长,大型互联网络系统,如交通网络或基础设施网络,更容易受到外部干扰。因此,在工程系统的设计、操作和恢复阶段管理潜在的破坏性事件,从而提高系统的弹性是一项重要但具有挑战性的任务。为了保证系统在故障事件发生后的恢复能力,本文提出了一种基于混合整数线性规划(MILP)的异构可调度代理恢复框架。采用基于情景的随机优化(SO)技术来处理自然灾害恢复过程中的固有不确定性。此外,与使用确定性等效公式的传统SO不同,由于在极端事件恢复等应用中决策的时间稀疏性,本研究中实施了额外的风险度量。所得到的恢复框架涉及一个大规模的MILP问题,因此还提出了一种适当的分解技术,即改进的朗格松弛,以实现可处理的时间复杂度。基于IEEE 37总线测试馈线的案例研究结果表明,使用所提出的框架改善弹性的好处以及采用SO配方的优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Risk-Averse Optimization for Resilience Enhancement Under Uncertainty
With the growth of complexity and extent, large scale interconnected network systems, e.g., transportation networks or infrastructure networks, become more vulnerable towards external disturbances. Hence, managing potential disruptive events during design, operating, and recovery phase of an engineered system therefore improving the system’s resilience is an important yet challenging task. In order to ensure system resilience after the occurrence of failure events, this study proposes a mixed integer linear programming (MILP) based restoration framework using heterogenous dispatchable agents. Scenario based stochastic optimization (SO) technique is adopted to deal with the inherent uncertainties imposed on the recovery process from the nature. Moreover, different from conventional SO using deterministic equivalent formulations, additional risk measure is implemented for this study because of the temporal sparsity of the decision making in applications such as the recovery from extreme events. The resulting restoration framework involves with a large-scale MILP problem and thus an adequate decompaction technique, i.e., modified Langragian Relaxation, is also proposed in order to achieve tractable time complexity. Case study results based on the IEEE 37-buses test feeder demonstrate the benefits of using the proposed framework for resilience improvement as well as the advantages of adopting SO formulations.
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