Historically, energy constraints have limited the spatial range, endurance and capabilities of ocean observation systems. Recently developed wave energy conversion technologies have the potential to help overcome these limitations by providing co-located and persistent power generation for ocean observations, enabling new opportunities for ocean research. In this paper, we develop the first techno-economic model for wave-powered ocean observation systems and use the model to study system characteristics and cost drivers. Our model utilizes time-domain simulation and optimization to identify cost-optimal system characteristics and to estimate capital and operational costs. Using our model, we evaluate the use of wave energy to power a 200 W ocean observation system deployed for five years at five unique geographic locations. We found that, depending on the geographic location, cost-optimal wave energy powered systems require an ≈ 0.5–3 kW wave energy converter and an ≈ 15-50 kWh battery. The corresponding range of power system costs over the deployment duration is between $110,800 and $673,200. We build on these results by performing a sensitivity analysis of key model parameters and identifying the potential economic impact of future technology advancements. Overall, our results indicate that characteristics of the geographic location, power system durability, and electrical power demand are key drivers of power system economics for ocean observing.

从历史上看，能源限制限制了海洋观测系统的空间范围、续航能力和能力。最近开发的波浪能转换技术有可能通过为海洋观测提供协同定位和持续发电来帮助克服这些限制，为海洋研究提供新的机会。在本文中，我们开发了第一个波浪动力海洋观测系统的技术经济模型，并使用该模型来研究系统特性和成本驱动因素。我们的模型利用时域模拟和优化来识别成本最优的系统特性并估算资本和运营成本。使用我们的模型，我们评估了使用波浪能为在五个独特地理位置部署了五年的 200 W 海洋观测系统供电的情况。我们发现，根据地理位置，成本最优的波浪能供电系统需要一个 ≈ 0.5-3 kW 的波浪能转换器和一个 ≈ 15-50 kWh 的电池。部署期间相应的电力系统成本范围在 110,800 美元到 673,200 美元之间。我们通过对关键模型参数进行敏感性分析并确定未来技术进步的潜在经济影响，以这些结果为基础。总体而言，我们的结果表明，地理位置、电力系统耐用性和电力需求的特征是海洋观测电力系统经济性的关键驱动因素。800 美元和 673,200 美元。我们通过对关键模型参数进行敏感性分析并确定未来技术进步的潜在经济影响，以这些结果为基础。总体而言，我们的结果表明，地理位置、电力系统耐用性和电力需求的特征是海洋观测电力系统经济性的关键驱动因素。800 美元和 673,200 美元。我们通过对关键模型参数进行敏感性分析并确定未来技术进步的潜在经济影响，以这些结果为基础。总体而言，我们的结果表明，地理位置、电力系统耐用性和电力需求的特征是海洋观测电力系统经济性的关键驱动因素。