This paper introduces a new general methodology for the modeling and reliability evaluation of small isolated power systems, which include wind turbines, photovoltaics, and diesel generators, based on fluid stochastic Petri nets (FSPNs). The proposed methodology presents two major novelties in FSPN modeling, namely: 1) the introduction of a new kind of Petri net arc, called the database arc, which makes possible the direct import of real data in the simulation process; and 2) the selection of constant time intervals in FSPN modeling, instead of assuming continuous dynamics defined by the change of fluid level over time. Moreover, in order to construct the overall system model, this paper proposes a general framework for modular representation of the system under study following a number of well-defined steps. The obtained model is fully parameterized and compared to classical simulation methods, it provides to its user the additional advantage of graphical representation of system's components and attributes. Four scenarios, which describe power system's performance under different conditions, were implemented. For each one of the developed scenarios, nine reliability and performance indexes have been calculated and compared.

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.

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.

A generic well-defined methodology for the construction and operation of dynamic process models of discrete industrial systems following a number of well-defined steps is introduced. The sequence of steps for the application of the method as well as the necessary inputs, conditions, constraints and the results obtained are defined. The proposed methodology covers the classical offline modelling and simulation applications as well as their online counterpart, which use the physical system in the context of digital twins, with extensive data exchange and interaction with sensors, actuators and tools from other scientific fields as analytics and optimisation. The implemented process models can be used for what-if analysis, comparative evaluation of alternative scenarios and for the calculation of key performance indicators describing the behaviour of the physical systems under given conditions as well as for online monitoring, management and adjustment of the physical industrial system operations with respect to given rules and targets. An application of the proposed methodology in a discrete industrial system is presented, and interesting conclusions arise and are discussed. Finally, the open issues, limitations and future extensions of the research are considered.

STEM education is of paramount importance, especially in the lower levels of education, and it has been proven beneficial for students in many ways. Although there are various tools available, there are significant drawbacks mainly related to the cost and the ease of use. In this study, we introduce a new low-cost educational framework oriented toward elementary and secondary educational needs. The proposed system exploits open tools and low-cost devices. The system’s core is based on the popular Arduino microcontroller, a low-cost device supported by a large community. The overall system was designed and developed, providing an expandable, modular system of low complexity suitable for students with no or low prior knowledge in related subjects, among others, to programming, embedded devices, sensors and actuators, as well as robotics. Our scope was to provide a system with a small learning curve. Practically, this makes it possible in a short amount of time for the students to perform appealing yet straightforward tasks which will boost their self-confidence and creativity, improve their technical skills and simultaneously provide a system with several capabilities usable in different kinds of projects. The introduced system was tested through a preliminary study using flow theory in a team of 68 students of the three last grades in an elementary school in Greece.

Renewable energy technologies like photovoltaics may be considered an indispensable component of a low-carbon electricity mix, but social acceptance should not be taken for granted. For instance, in Greece there are still claims, especially in rural areas, regarding the land use and the competition against more traditional economic activities such as grazing. An argument in favor of confining to roof-mounted photovoltaic installations is the additional infrastructure requirements for ground-mounted larger-scale photovoltaics. These requirements reduce and could potentially negate their environmental benefits. The aim of this study is to investigate the life cycle environmental impacts of commercial ground-mounted photovoltaic farms and compare them against residential roof-mounted photovoltaic installations. Data were gathered for a 500 kW ground-mounted photovoltaic installation and for five roof-mounted installations of 10 kW capacity, each from the same area at the prefecture of Pella in Northern Greece. An LCA (Life Cycle Assessment) was performed and results show that panel production is the main contributor for both types and that ground-mounted photovoltaics—when no transmission/distribution infrastructure is considered—have lower impacts than the roof-mounted residential photovoltaic installations for all impact categories except terrestrial ecotoxicity. However, when located further than 10.22 km from grid connection, ground-mounted photovoltaics have higher impacts for almost all environmental impact categories.