A dynamic multi-stage design framework for staged deployment optimization of highly stochastic systems
A dynamic multi-stage design framework for staged deployment optimization of highly stochastic systems
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DOI：
10.1007/s00158-023-03609-6
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发表时间：
2023-06
影响因子：
3.9
通讯作者：
Bayan Hamdan;Zheng Liu;K. Ho;˙I. Esra B¨uy¨uktahtakın;Pingfeng Wang
中科院分区：
文献类型：
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作者：
Bayan Hamdan;Zheng Liu;K. Ho;˙I. Esra B¨uy¨uktahtakın;Pingfeng Wang
对于具有强大的跨系统联系和复杂系统的多学科系统，阶段设计优化的必要性已在各种情况下得到承认。这在子系统之间的决策是相互依赖的领域，以及需要在不确定的环境中做出战术决策的情况下是突出的。通过将时变不确定性离散到不同的场景中，并分别考虑每个场景中的柔性决策变量，分析了采用进化设计方案所获得的灵活性。然而，这些问题在决策时间步使用现有信息。本文提出了一种动态多阶段设计框架，用于解决动态纳入更新的系统信息并重新制定问题以考虑更新的参数的问题。研究了考虑阶段决策的重要性，并在参数随机性随时间减小的情况下评估了模型的效益。通过一个数值案例研究和一个IEEE 30总线系统的案例研究，研究了考虑阶段部署对高度随机、大规模系统的影响。本文的案例研究探讨了大型复杂系统的多学科设计问题以及高度随机系统的运行规划问题。通过数值和工程案例研究的结果，强调了考虑多学科系统阶段部署的重要性，这些系统的参数随时间变化的变异性越来越小。
The need for staged design optimization for multidisciplinary systems with strong, cross-system links and complex systems has been acknowledged in various contexts. This is prominent in fields where decisions between subsystems are dependant, as well as in cases where tactical decisions need to be made in uncertain environments. The flexibility gained by incorporating evolutionary design options has been analyzed by discretizing the time-variant uncertainties into scenarios and considering the flexible decision variables in each scenario separately. However, these problems use existing information at the decision time step. This paper presents a dynamic multi-staged design framework to solve problems that dynamically incorporate updated system information and reformulate the problem to account for the updated parameters. The importance of considering staged decisions is studied, and the benefit of the model is evaluated in cases where the stochasticity of the parameters decreases with time. The impact of considering staged deployment for highly stochastic, large-scale systems is investigated through a numerical case study as well as a case study for the IEEE 30 bus system. The case studies presented in this paper investigate multi-disciplinary design problems for large-scale complex systems as well as operational planning for highly stochastic systems. The importance of considering staged deployment for multi-disciplinary systems that have decreasing variability of their parameters with time is highlighted and demonstrated through the results of numerical and engineering case studies.