Browsing by Author "Coronado-Hernandez, Oscar E."

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Contributions Toward Net-Zero Carbon in the Water Sector: Application to a Case Study
(IWA Publishing, 2025) Ramos, Helena M.; Perez-Sanchez, Modesto; Correia, Tiago; Bekci, E.; Besharat, M.; Kuriqi, Alban; Coronado-Hernandez, Oscar E.
This study presents an integrated smart water-energy nexus framework combining IoT-based water monitoring, hybrid renewables (hydropower/solar/wind), and AI-driven optimization. Real-time sensor data enables automated grid management, while AI analytics optimize operations and predict maintenance needs through a closed-loop system. The solution achieves bidirectional energy exchange, with the full hybrid system (G + H + PV + W) reducing costs by 41.5% (831K) and LCOE by 57.2% (0.0475/kWh). Financial analysis confirms viability with 26.4% IRR and 3.8-year payback, while achieving negative CO2 emissions (-160,476 kg/year). Progressive renewable integration enhances all key performance indicators (KPIs), cutting OPEX by 89.9% (7,156/year) through optimized operations. Dual water-energy performance metrics (leakage, pressure, % renewable share) ensure balanced and sustainable grid management. Key innovations include IoT-energy synergy, AI-driven predictive maintenance, and circular resource efficiency. The framework demonstrates how smart water grids can achieve both economic and environmental benefits through renewable energy integration and advanced digital solutions.
Citation - WoS: 3
Citation - Scopus: 4
Optimization and Machine Learning in Modeling Approaches to Hybrid Energy Balance to Improve Ports' Efficiency
(MDPI, 2025) Ramos, Helena M.; Coelho, Joao S. T.; Bekci, Eyup; Adrover, Toni X.; Coronado-Hernandez, Oscar E.; Perez-Sanchez, Modesto; Espina-Valdes, R.
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 Avil & eacute;s 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.