This research provides a comprehensive review of hybrid energy solutions and optimization models for ports and marine environments. It details new methodologies, including strategic energy management and a machine learning (ML) tool for predicting energy surplus and deficits. The hybrid energy module solution for the Port of Avilés was further developed to evaluate the performance of new tools such as the Energy Management Tool (EMTv1), HYbrid for Renewable Energy Solutions (HY4RES), and a commercial model (Hybrid Optimization of Multiple Energy Resources—HOMER) in optimizing renewable energy and storage management. Seven scenarios were analyzed, integrating different energy sources and storage solutions. Using EMTv1, Scenario 1 showed high surplus energy, while Scenario 2 demonstrated grid independence with Pump-as-Turbine (PAT) storage. The HY4RES model was used to analyze Scenario 3, which achieved a positive grid balance, exporting more than imported, and Scenario 4 revealed limitations of the PAT system due to the low power installed. Scenario 5 introduced a 15 kWh battery, efficiently storing and discharging energy, reducing grid reliance, and fully covering energy needs. Using HOMER modeling, Scenario 6 required 546 kWh of grid energy but sold 2385 kWh back. Scenario 7 produced 3450 kWh/year, covering demand, resulting in 1834 kWh of surplus energy and a small capacity shortage (1.41 kWh/year). AI-based ML analysis was applied to five scenarios (the ones with access to numerical results), accurately predicting energy balances and optimizing grid interactions. A neural network time series (NNTS) model trained on average year data achieved high accuracy (R²: 0.9253–0.9695). The ANN model proved effective in making rapid energy balance predictions, reducing the need for complex simulations. A second case analyzed an increase of 80% in demand, confirming the model’s reliability, with Scenario 3 having the highest MSE (0.0166 kWh), Scenario 2 the lowest R² (0.9289), and Scenario 5 the highest R² (0.9693) during the validation process. This study highlights AI-driven forecasting as a valuable tool for ports to optimize energy management, minimize grid dependency, and enhance their efficiency.

Tides can be a vast and predictable source of renewable energy. Due to the solar and lunar influx on our planet, they move large amounts of water periodically, and this energy can be harnessed using devices designed and positioned adequately, such as current turbines. However, the relation between the energy obtained with actual devices and the economic and environmental cost of their installation limits the practical application of these solutions. In order to optimize the design of this technology and achieve its successful installation and use, a detailed knowledge about the energy potential of tides at the specific location is necessary. This calculation is not easy and requires the use of specialized software tools. Currently, there is no specific software to evaluate the tidal currents energy potential, but there are more than a few codes able to calculate the hydraulic flow in rivers, estuaries and coastal regions. These programs are usually used for the calculation of pollutant dispersion and floods, but they can be adapted with more or less success. This paper reviews the available 1D, 2D, and 3D software tools with the aim of analyzing their functionality and their validity to evaluate the energy potential of tidal currents.