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Global electricity demand is rising daily due to economic development and population growth. To reduce greenhouse gas emissions, renewable energy is essential for meeting this increasing power demand. One promising source is the use of open ocean currents, such as the Gulf Stream, Kuroshio, and Agulhas currents, for electricity production. These currents can provide predictable and nearly continuous electricity generation along the western boundaries of ocean basins. While global evaluations using numerical models and regional assessments with direct measurements have been conducted, a comprehensive global assessment based on actual measurements is lacking. This paper fills that gap by presenting a global measurement-based assessment of ocean current energy, utilizing more than thirty years of drifter data. Results show that power densities above 1500 W/m² were typical in the waters off Florida’s east coast and South Africa, where total water depths are around 300 m, but not in other regions. The results for North America and Japan showed higher estimation accuracy, while poor or indeterminable estimation accuracy was commonly found off South Africa and South America, especially in the relatively shallow water area off northern Brazil and French Guiana. In addition, over 490,000 km² of the sea surface in these four regions exhibits power densities higher than 500 W/m², representing nearly 59% of the total identified high power density areas globally. 

Existing computer analytic methods for the microgrid system, such as reinforcement learning (RL) methods, suffer from a long-term problem with the empirical assumption of the reward function. To alleviate this limitation, we propose a multi-virtual-agent imitation learning (MAIL) approach to learn the dispatch policy under different power supply interrupted periods. Specifically, we utilize the idea of generative adversarial imitation learning method to do direct policy mapping, instead of learning from manually designed reward functions. Multi-virtual agents are used for exploring the relationship of uncertainties and corresponding actions in different microgrid environments in parallel. With the help of a deep neural network, the proposed MAIL approach can enhance robust ability by minimizing the maximum crossover discriminators to cover more interrupted cases. Case studies show that the proposed MAIL approach can learn the dispatch policies as well as the expert method and outperform other existing RL methods. 

Blue economy industries like aquaculture are expanding further offshore to leverage the ocean’s vast scale. However, this shift demands reliable, regular power independent of land-based grids. This study introduces a bi-level optimization framework for the design and operation of a hybrid OTEC-diesel system equipped with battery energy storage to power offshore fish farms. The upper-level optimization aims to minimize the levelized cost of energy (LCOE) and ensure continuous operation by optimizing the battery size within the constraints of energy storage. The objective of the lower-level optimization is to minimize energy waste while also addressing an environmental goal, which is to decrease the unnecessary mixing of cold and warm water in the Rankine cycle of the ocean thermal energy conversion (OTEC) system. In our study, we investigated two system configurations: first, a traditional setup using only a diesel generator; second, an OTEC/Diesel/BESS hybrid configuration was evaluated under two scenarios for different fish farm sizes. The results show that scaling up the fish farm and hybrid OTEC-diesel system with energy storage significantly lowers the LCOE. Furthermore, regulating the working fluid’s mass flow rate cuts energy waste by nearly 20% compared to single-level optimization, which reduces environmental impact and positions this hybrid system as a sustainable and cost-effective alternative to diesel-powered fish farms.