Abstract
Ocean observation and monitoring relies on data gathered from multiple sources including gliders, cabled observatories, underwater vehicles, and moored buoys. Moored buoys, augmented by resident autonomous underwater vehicles, are potentially well-suited to long-term oceanographic data collection with the resolution and scope of data collection increased by utilizing in-situ power generation. While power needs could be provided by a single generation source (e.g., diesel generator, solar photovoltaics, or wave energy converter) with battery backup, a hybrid power system can potentially smooth seasonal variations that would otherwise require large generation and storage capacities, and be tailored to a locations’ resource availability. By reducing system size through hybridization, we have the potential to reduce overall system cost. To this end, we developed an optimization model that defines the generator and storage capacities of the cost-optimal hybrid power system for a given location and load profile. The model considers generation by wave energy converters, wind turbines, solar photovoltaics, diesel generators, and current turbines with a battery for energy storage. The model uses the defined load profile and location specific time series of resource availability for a time-domain simulation of the power generated and battery state of charge. Based on the power system size, mooring, and mission specific maintenance schedule, the model calculates capital and operating costs for each potential combination of generation and storage capacities. The optimization model searches the 6-dimensional design space to find the lowest cost system that can satisfy the load requirements. In this paper, we focus on a case study of a hybrid power system serving an ocean observation buoy with a resident autonomous underwater vehicle located at the Mid-Atlantic Shelf Break. Diagnostic metrics such as the cost-breakdown and generator capacity factors are evaluated to understand cost drivers and draw comparisons to the costs of single generation systems. Additionally, the model sensitivity to key assumptions such as battery life, maintenance vessel cost, survival loads, and required persistence are discussed.