Hybrid renewable energy systems (HRESs) are being incorporated and evaluated within seaports to realize efficiencies, reduce dependence on grid electricity, and reduce operating costs. The paper adopts a genetic algorithm (GA)-based optimization framework to assess four energy management scenarios that embed wind turbines (WTs), photovoltaic energy (PV), an energy storage system (ESS), and an energy management system (EMS). The scenarios were developed based on different levels of renewable energy integration, energy storage utilization, and grid dependency to optimize cost and sustainability while reflecting the actual port energy scenario as the base case. Integrating HRES, ESS, and EMS reduced the port’s levelized cost of energy (LCOE) by up to 54%, with the most optimized system (Scenario 3) achieving a 53% reduction while enhancing energy stability, minimizing grid reliance, and maximizing renewable energy utilization. The findings show that the HRES configuration provides better cost, sustainability, and resiliency than the conventional grid-tied system. The unique proposed EMS takes it a step further, optimizing not just the energy flow but also the cost, making the overall system more efficient—and less costly—for the user. ESS complements energy storage and keeps it functional and reliable while EMS makes it completely functional by devising ways to reduce costs and enhance efficiency. The study presents the technical and economic viability of HRES as an economic and operational smart port infrastructure through its cost-effective integration of renewable energy sources. The results reinforce the move from conventional to sustainable autonomous port energy systems and lay the groundwork for forthcoming studies of DR-enhanced port energy management schemes. While prior studies have explored renewable energy integration within ports, many lack a unified, empirically validated framework that considers HRES, ESS, and EMS within real-world port operations. This research addresses this gap by developing an optimization-driven approach that assesses the techno-economic feasibility of port energy systems while incorporating real-time data and advanced control strategies. This study was conducted to enhance port infrastructure and evaluate the impact of HRES, ESS, and EMS on port sustainability and autonomy. By bridging the gap between theoretical modeling and practical implementation, it offers a scalable and adaptable solution for improving cost efficiency and energy resilience in port operations.

Ports are vital nodes in global trade networks but are also significant contributors to greenhouse gas emissions. Their transition toward sustainable, nearly zero-energy operations require comprehensive and structured strategies. This study proposes a practical and scalable framework to support the energy decarbonization of ports through the phased integration of hybrid renewable energy systems (HRES) and energy storage systems (ESS). Emphasizing a systems-level approach, the framework addresses key aspects such as energy demand assessment, resource potential evaluation, HRES configuration, and ESS sizing, while incorporating load characterization protocols and decision-making thresholds for technology deployment. Special consideration is given to economic performance, particularly the minimization of the Levelized Cost of Energy (LCOE), alongside efforts to meet energy autonomy and operational resilience targets. In parallel, the framework integrates digital tools, including smart grid infrastructure and digital shadow technologies, to enable real-time system monitoring, simulation, and long-term optimization. It also embeds mechanisms for regulatory compliance and continuous adaptation to evolving standards. To validate its applicability, the framework is demonstrated using a representative case study based on a generic port profile. The example illustrates the transition process from conventional energy models to a sustainable port ecosystem, confirming the framework’s potential as a decision-making tool for port authorities, engineers, and policymakers aiming to achieve effective, compliant, and future-proof energy transitions in maritime infrastructure.

This study investigates the integration of soft sensors and deep learning in the oil-refinery industry to improve monitoring efficiency and predictive accuracy in complex industrial processes, particularly de-ethanization and debutanization. Soft sensor models were developed to estimate critical variables such as the C2 and C5 contents in liquefied petroleum gas (LPG) after distillation and the energy consumption of distillation columns. The refinery’s LPG purification process relies on periodic sampling and laboratory analysis to maintain product specifications. The models were tested using data from actual refinery operations, addressing challenges such as scalability and handling dirty data. Two deep learning models, an artificial neural network (ANN) soft sensor model and an ensemble random forest regressor (RFR) model, were developed. This study emphasizes model interpretability and the potential for real-time updating or online learning. The study also proposes a comprehensive, iterative solution for predicting and optimizing component concentrations within a dual-column distillation system, highlighting its high applicability and potential for replication in similar industrial scenarios.

In this paper is provided a systematic, in-depth, behavioral analysis of renewable energy sources cooperatives’ members. The analysis proved that in, on hand, there was a noticeable difference in the portion of affection of each proposed intervention on the actual energy consumption, which may be to even ten times more in some cases, and on the other hand, the difference in energy consumption between the analyzed groups was noticeable as well. So, implementing energy efficiency interventions of various types, such as technical support, special tariffs, energy generation schemes, and smart meters, seems to lead to substantial energy reductions to even more than 10%, cumulatively, and reduces the environmental footprint. Additionally, the majority of energy efficiency interventions applied by the renewable energy sources cooperatives are proved to be effective in achieving their primary goal, sensitizing members, and leading them to a more efficient energy consumption behavior (“greener”). The results of the analysis showed that each proposed intervention had played a different but nonetheless significant role in the diminishing of the energy consumption of the members and that there was a noticeable difference in energy consumption between the analyzed groups. The results of the analysis demonstrated more than 22 GWh totally in green consumption, and almost 4500 tons of CO₂ saved.

Satellite data of moderate spatial resolution (MODIS and MERIS) were used to retrieve the aerosol optical depth (AOD) over the urban area of Athens. MODIS products were obtained at a horizontal resolution of 10 by 10 km²centered over Athens, while the differential textural analysis (DTA) code was applied to MERIS images to retrieve relative‐to‐reference AOD with a resolution of 260 m by 290 m. The possibility of exploiting the full resolution of MERIS data in retrieving AOD over a grid of a few hundreds metres was thereby investigated for the first time. MERIS‐based AOD, centred at 560 nm, showed strong positive correlation to ground‐based PM₁₀data (R²= 0.85), while MODIS AOD products were in agreement with both MERIS and PM₁₀. Back trajectories were used to study the impact of atmospheric conditions prevailing during the examined days. Days associated with Saharan air masses corresponded to enhanced AOD and predominance of coarse‐mode particles. The results suggest that, at least for the case of Athens, AOD retrieved by MERIS images using the DTA code over cloud‐free areas can be related to PM₁₀. The accuracy of retrieval mainly depends on the successful selection of the reference satellite data, namely, an image being least contaminated by tropospheric aerosols.

The pandemic has created opportunities which researchers, developers and planners should analyze, capitalize and adapt. How much COVID-19 restrictions affected air quality in Mediterranean tourism destinations? Was there a rebound effect during post-lockdown period? Which was the behavioral change concerning the urban mobility? Noteworthy changes in the air pollutants’ mean concentration on the ground-level have been experienced. The air quality was measured on the ground-level and reviewed concerning CO₂, CO, NO_x, and CH4 under various climate conditions. 5-min air quality and traffic load data were processed from different city spots in a Mediterranean tourism destination. The study examined the impact of State strategies on tackling the COVID-19 spread on both traffic load and air quality employing statistical methods. The obtained results show the lockdown period’s impact on improving air quality. Although there was a minor decrease, the mean CO₂ levels were not significantly affected during the lockdown period. The mean NOx levels during the lockdown period were decreased by 32% compared to the same period in 2019; there is also a 10% decrease in the mean levels of NOx compared to the period before the lockdown. This study related four primary air pollutants (CO₂, CO, NOx, and CH₄), the total number of vehicles, and the total number of tourists. A rebound effect proved during the post lockdown period. The results and the methodology of this research work are replicable as confirmed by other studies for different cities. Abbreviations: Analysis of Variance, ANOVA, Coronavirus Disease 2019, COVID-19; Green House Gases, GHGs; European Commission, EC; European Union, EU; National Aeronautics and Space Administration, NASA.