The authors have recently proposed a ‘decision-based’ framework to design and maintain repairable systems. In their approach, a multiobjective optimization problem is solved to identify the best design using multiple short and long-term statistical performance metrics. The design solution considers the initial design, the system maintenance throughout the planning horizon, and the protocol to operate the system. Analysis and optimization of complex systems such as a microgrid is however, computationally intensive. The problem is exacerbated if we must incorporate flexibility in terms of allowing the microgrid architecture and its running protocol to change with time. To reduce the computational effort, this paper proposes an approach that “learns” the working characteristics of the microgrid and quantifies the stochastic processes of the total load and total supply using autoregressive time-series. This allows us to extrapolate the microgrid operation in time and eliminate therefore, the need to perform a full system simulation for the entire long-term planning horizon. The approach can be applied to any repairable system. We show that building in flexibility in the design of repairable systems is computationally feasible and leads to better designs.

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