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Nowadays, electric warships are involved in complex missions involving multiple simultaneous operations. In order to ensure mission success, future ships must be designed in a way that optimizes their performance in presence of complex mission loads. The metric operability has played a major role in assessing mission performance. This paper discusses the evolution of this metric from fixed load-based early-stage shipboard electrical system design to the current approaches of using dynamic mission profiles. Results for a notional two-function problem involving multiple missions are shown to demonstrate the current state of operability-based approaches for the evaluation of power system performance.

With the advent of high energy pulse mission loads and distributed power and energy on all-electric ship platforms, a new total ship control system capability is emerging that could significantly impact overall mission success. No longer are loads limited to an all-or-nothing power per use approach. Rather, energy applied to advanced mission loads can be scaled—even regulated—to achieve a desired outcome. Within this new framework, the effectiveness of unique missions can be quantified as a function of the amount and allocation of power to service each mission load. Power elasticity enables a new frontier of how decisions are made during intense operational scenarios where limited power and energy resources are applied to competing load demands. Herein, a power allocation strategy that addresses this challenge is proposed. The general approach is to use state-space models to represent the evolution of both the mission and the engineering system that is supporting the mission. An optimal control problem is performed to maximize some functional over the mission state and actions. By doing so, it indicates the capability of the engineering system to support such missions. The uniqueness of this approach is that solutions become far more mission specific. Results based on a simple two-function platform—each competing for the same limited power resources—show that this approach can be used to assess the probability of success given a certain initial allocation of power.