论文标题
不确定性下的复杂工程系统的弹性增强风险优化
Risk-Averse Optimization for Resilience Enhancement of Complex Engineering Systems under Uncertainties
论文作者
论文摘要
随着复杂性和程度的增长，大规模互连网络系统，例如运输网络或基础架构网络变得更容易受到外部破坏的影响。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，因此改善系统的弹性是一项重要但又具有挑战性的任务。为了确保发生故障事件发生后的系统弹性，本研究提出了使用异构调度剂的基于混合的线性编程（MILP）恢复框架。采用基于方案的随机优化（SO）技术来处理自然界恢复过程中施加的固有的不确定性。此外，与常规的不同，因此使用确定性等效的表述不同，本研究实施了其他风险措施，因为在应用程序中的决策暂时性稀疏（例如从极端事件中恢复）。由此产生的恢复框架涉及一个大规模的MILP问题，因此还采用了适当的分解技术，即修改的Lagrangian双重分解，以实现可拖动的计算复杂性。基于IEEE 37总线测试馈线的案例研究结果表明，使用拟议的框架改善了提议的框架以及采用SO配方的优势。
With the growth of complexity and extent, large scale interconnected network systems, e.g. transportation networks or infrastructure networks, become more vulnerable towards external disruptions. Hence, managing potential disruptive events during the 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 heterogeneous dispatchable agents. 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, 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 a large-scale MILP problem and thus an adequate decomposition technique, i.e. modified Lagrangian dual decomposition, is also employed in order 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.
