A dynamic multi-stage design framework for staged deployment optimization of highly stochastic systems

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.