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Risk-averse optimization for resilience enhancement of complex engineering systems under uncertainties

机译：在不确定性下复杂工程系统复合力增强的风险厌恶优化

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摘要

With the growth of complexity and extent, large scale interconnected network systems, e.g., transportation networks or infrastructure networks, become more vulnerable to external disturbances. Hence, managing potential disruptive events during the design, operating, and recovery phase of an engineered system and therefore improving the system's resilience is an important yet challenging task. To ensure system resilience after the occurrence of failure events, this study proposes a mixed-integer linear programming (MILP) based restoration framework using heterogeneous dispatchable agents. The scenario-based stochastic optimization (SO) technique is adopted to deal with the inherent uncertainties imposed on the recovery process from nature. Moreover, different from conventional SO using deterministic equivalent formulations, the CVaR 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 a large-scale MILP problem and thus an adequate decomposition technique i.e. modified Lagrangian dual decomposition, is also employed to achieve tractable computational complexity. Case study results based on the IEEE 37-bus test feeder demonstrate the benefits of using the proposed framework for resilience improvement as well as the advantages of adopting SO formulations.
机译：随着复杂性和范围的增长，大规模互连的网络系统，例如运输网络或基础设施网络，变得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事件，从而提高系统的恢复力是一个重要而挑战性的任务。为了确保在发生故障事件发生后的系统恢复性，本研究提出了使用异构调度代理的混合整数基于线性编程（MILP）的恢复框架。采用了基于方案的随机优化（SO）技术来处理从自然恢复过程中施加的固有的不确定性。此外，与使用确定性等效式制定的传统方式不同，由于在诸如从极端事件中恢复的恢复之类的应用程序的时间稀疏性来实现CVAR风险措施。所得到的恢复框架涉及大规模的MILP问题，从而涉及一种充分的分解技术，即改进的拉格朗日双分解，也用于实现易易化的计算复杂性。基于IEEE 37-Bus测试馈线的案例研究结果证明了利用所提出的塑造框架的益处，以及采用制剂的优点。

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