Abstract: This work presents the development and validation of a 1D, co-flow, finite-volume model for the simulation of planar SOFCs, developed for integration in more complex systems and process simulations. The model is calibrated and validated using experimental SOFC polarization curves in a wide range of operating conditions in terms of H2 and H2O molar fraction in the fuel, temperature, and fuel utilization factor, demonstrating good accuracy and the possibility to simulate the most relevant physical processes occurring within an SOFC and to investigate its internal operating conditions in terms of temperature, current density, and gas composition profiles.

Abstract: Agrivoltaic systems represent an integrated approach to simultaneous agricultural production and photovoltaic energy generation, aiming to optimize land use under increasing climate and energy pressures. This study conducts a comprehensive bibliometric analysis of the agrivoltaics literature to identify research trends, thematic structures, and knowledge gaps. A structured search was performed in the Web of Science, Scopus, and PubMed databases, covering all publications up to December 2025. After data cleaning, 1,271 documents were analyzed using Python-based bibliometric and topic modeling tools. The literature was organized into four main thematic axes: system design and integration, photovoltaic performance and radiative modeling, agro-environmental interactions, and livestock-oriented agrivoltaic systems. Results indicate a rapid expansion of research activity, strong emphasis on technological optimization, and limited integration of long-term agronomic, economic, and regulatory dimensions, particularly in Global South contexts. The study highlights the need for standardized metrics, integrated energy–water–crop modeling, and normative frameworks to support scalable and agriculturally prioritized agrivoltaic systems.

Abstract: We analyzed the behavior of a “Solar Wall” and validated the apparatus with three nanofluids (silver, titanium dioxide and a hybrid compound) in view of their photothermal conversion performance. The factors considered were the temperature gain in relation to the base fluid, the stored energy and the specific absorption rate. A cost survey was carried out to find out the profit of each nanofluid. Five concentrations were studied for each nanofluid. A hybrid nanofluid formed by the previous ones was also tested. The results presented that the Solar Wall has achieved repeatability, and we can state that it is suitable for the tests on other nanofluids. The silver and hybrid nanofluids performed better, the first obtained a temperature gain of 10.2 °C compared to the base fluid and the hybrid reached 9.9 °C. Regarding the energy gain, the silver-based obtained a gain of 31.93%, and the hybrid obtained 34.52%. The SAR values for the silver nanofluid were higher than the titanium-based, nevertheless the cost to generate an energy unit using the former was higher than in the titanium case. The silver-based and the hybrid nanofluids obtained improved photothermal conversion, being the most promising options.

Abstract: In recent years, numerous initiatives have aimed to implement renewable energy sources in diverse contexts. This article presents the design and evaluation of a photovoltaic charging station prototype for low-power devices in educational settings. Its foremost innovation is achieved through the integration of IoT technologies for real-time monitoring and optimization, enabling data collection on energy generation, consumption, and environmental conditions, with potential for AI-based processing. The system adopts a modular and scalable design, allowing adaptation to different needs and conditions. The project demonstrates how renewable energy use can be optimized in non-commercial contexts according to environmental factors and energy demand. The system comprises four subsystems: solar energy capture via a photovoltaic panel, current regulation and control, environmental parameter monitoring, and real-time data transmission through advanced communication protocols. Results indicate that the prototype efficiently supports device charging and enables intelligent energy management through IoT integration. Remote access to operational data facilitates real-time decision-making and management optimization. The charging efficiency allows laptops to operate for a one-hour class in off-grid outdoor environments, with up to four hours of battery life under average radiation. Beyond technical outcomes, the project positively impacted student motivation and user engagement, fostering critical thinking, problem-solving, and environmental awareness. In conclusion, this proposal contributes to advancing the intersection of education, sustainability, and technological innovation. Its modular structure, real-time analysis capacity, and educational value make it an adaptable and replicable solution that contributes to a more efficient and sustainable energy model.

Abstract: Small hydro power plants (HPPs) play an important role in managing fluctuating energy requirements. This article presents a real-world case study where model predictive control (MPC) utilizing lightGBM-based machine learning (ML) forecasts of energy demand and water availability is employed to optimize the scheduling of a small HPP for peak shaving. A comparative analysis is conducted between the current non-predictive control strategy, which relies on operator decisions for peak shaving, and a fully automatic controller that optimally schedules the utilization of available water resources based on ML predictions. Preliminary results show that the MPC can outperform the operator’s decisions and that this has the potential of improving peak shaving capabilities of small HPPs, emphasizing the role of predictive control methodologies for exploiting energy storage resource in the management of the distribution grid. This approach offers a pragmatic solution that small utilities can adopt with minimal effort using their own data.

Abstract: This study presents a prospective attributional Life Cycle Assessment of the Italian na-tional electricity grid for 2024–2040, aligned with the integrated national climate and en-ergy plan and the country's decarbonization pathway. The main goal was to calculate, analyze, and apply hourly emission factors for electricity and compare them with annual average factors for the same consumption profile to assess temporal effects on environmental outcomes. Hourly factors were derived from a cost-optimization energy model simulating Italy's evolving generation mix. The model projects a sharp decline in fossil-based generation and a significant expansion of solar photovoltaics and wind, which together exceed half of national production by 2040. Natural gas remains essential for system balancing, while electricity imports stabilize the grid when renewable output is low. 16 impact categories were evaluated, revealing decreasing trends in climate change (255 to 141 g CO2-eq/kWhe), acidification, and others, and rising temporal variability in mineral/ metal resource depletion and land use due to renewable intermittency. Applying the method to a positive energy district in Bologna shows that time-resolved factors offer clearer insights than annual averages, especially for season-dependent impacts, and demonstrate substantial reductions in impact by 2040, alongside notable differences between consuming and exporting electricity.

Abstract: The rising uncertainties in electric load behaviour owing to human, technological and so-cio-economic events present a need to improve the accuracy and efficiency of current short-term load prediction (STLP) models. This paper compares the performance of four hybrid models for short-term Amp load prediction: Adaptive Neuro-Fuzzy Inference Sys-tem (ANFIS) integrated with Genetic Algorithm (GA) and Particle Swarm Optimisation (PSO), and Convolutional Neural Network (CNN) integrated with Long Short-Term Memory (LSTM) network and Extreme Gradient Boosting (XGB) machine. The models were trained and tested using historical data comprising hourly electrical Amp load ob-tained from a power utility substation in Kenya, and the corresponding weather data (temperature, wind speed, humidity) from January 2023 to June 2024. From the model testing results, both ANFIS-PSO and ANFIS-GA hybrid models show superior predictive accuracies with MAPE values of 4.519 and 4.636; RMSE of 0.3901 and 0.4024, and R2 scores of 0.9425 and 0.9391 respectively compared to CNN-LSTM and CNN-XGB models. The prediction across all models improved when the load data was pre-processed using Variational Mode Decomposition (VMD) technique. Nonetheless the hybrid ANFIS mod-els exhibited superior prediction accuracy, which is owed to their inherent adaptability to irregular data that enables them to capture the complex temporal patterns and non-linearities of Amp load well, thus making them more suitable for short-term load prediction problems.

Abstract: Interdigitated back contact solar cells were fabricated entirely with inkjet printing. PEDOT: PSS films with co-solvents and TiO2 suspension were printed on a textured silicon substrate with only one inkjet printer as was the metal ink. Adding co-solvent to the PEDOT:PSS and passivation of the Si surface significantly reduced the losses and enhanced the short circuit current, Jsc, and, as a result, improved the fill factor and efficiency of the devices. Although the thickness of PEDOT: PSS layer is approximately one micrometer, which is thicker than the optimal range typically reported, there is still adequate short-circuit current, Jsc, suggesting that there is a relatively large processing window for the PEDOT: PSS film fabrication. To further improve the performance of the devices, an anti-reflective coating on the front side is required. Also, an improved metal contact ink is needed to improve the contact resistance between the PEDOT:PSS layer and the metal contact.

Abstract: Smart Energy Power systems – encompassing oil, gas, nuclear, mining, and electricity – are undergoing rapid transformation driven by digitalization, decarbonization, and geopolitical uncertainty. Ensuring stability in energy communities within this complex, multi-sector landscape requires analytical frameworks that integrate both “hard” and “soft” dimensions of energy systems. This study proposes an integrated approach combining hard analyses, such as capacity and technical capability of energy infrastructures, as well as the security of supply of energy raw materials., with soft analyses, including international relations and energy diplomacy, in the context of stability in energy communities. By bridging technical and social perspectives, the framework captures interdependencies that are often overlooked when sectors or methodologies are treated in isolation. The paper conceptualizes energy communities as adaptive socio-technical systems in which technological performance and social acceptance co-evolve. Through comparative analysis across fossil fuel, nuclear, mining, and electricity domains, the study demonstrates how misalignment between hard and soft factors can amplify instability, while strategic integration enhances resilience and long-term sustainability. The findings highlight the necessity of interdisciplinary planning tools, data-driven decision support, and inclusive governance mechanisms to manage transition risks and operational uncertainties. This integrated model contributes to energy policy and systems engineering by offering a holistic lens for designing stable, smart energy power systems capable of supporting secure, equitable, and resilient energy communities in a rapidly changing global context.

Abstract: This review examines the combustion characteristics of hydrogenenriched natural gas with a specific focus on residential appliances, where safety, efficiency, and emission performance are critical. Drawing on experimental studies, numerical simulations, and regulatory considerations, the paper synthesizes current knowledge on how hydrogen addition influences flame stability, flashback phenomenon, thermal efficiency, pollutant formation, and flame geometry. Results across cooktop burners, boilers, and other domestic systems show that moderate hydrogen blending can reduce CO and CO₂ emissions and enhance combustion efficiency, but also increases burning velocity, diffusivity, and flame temperature, thereby elevating flashback and NOx risks. The review highlights the blending limits, design adaptations, and operational strategies required to ensure safe and effective integration of hydrogen into residential gas infrastructures, supporting its role as a transitional lowcarbon fuel.

Abstract: The smart grid concept is based on the full integration of renewable energy sources. Due to the short- and long-term volatility of these sources, new flexibility measures are necessary to ensure the smart grid operates stably and reliably. One option is to convert renewable energy into hydrogen, especially during periods of generation overcapacity. So hydrogen that is produced can be stored effectively and used “just in time” to stabilize the power system by undergoing a reverse conversion process in gas turbines or fuel cells which then supply power to the network. On the other hand and in order to achieve a sustainable general energy system (GES) it is necessary to replace other forms of fossil energy use, such as that used for heating and other industrial processes. Research indicates that a comprehensive hydrogen supply infrastructure is required. This infrastructure would include electrolysers, conversion stations, pipelines, storage facilities, and hydrogen gas turbines and/or fuel cell power stations. Some studies in Germany suggest that the existing gas infrastructure could be used for this purpose. Further, nuclear and coal power plants are not considered reserve power plants (also German case), an additional 20–30 GW of generation capacity in H2 operated gas turbines and strong H₂ transportation infrastructure will be required over the next ten years. This paper describes the systematic transformation from today's power system to one that includes a hydrogen economy. It discusses the components of this new system in depth, focusing on current challenges and applications. Some scaled current applications demonstrate the state of the art in this area, including not only technical requirements (reliability, risks) and possibilities, but also economic aspects (cost, business models, impact factors).

Abstract:

District heating systems are central to Europe’s decarbonisation efforts and its 2050 climate-neutrality target. However, given the deep embedding of district heating in the socio-economic system and built environment, meeting policy targets at the local level gives rise to a range of technical, infrastructural and socio-economic challenges. This is due to the high complexity and multidimensionality of the process, as well as the scarcity of local resources (e.g. land, surface waters, waste heat, etc.). In Bucharest, Romania, the largest district heating system in the European Union, the process of decarbonisation represents a particularly complex challenge. The system is characterised by high technical wear, heavy dependence on natural gas, significant heat losses and complex governance structures. This paper presents a strategic planning exercise for aligning the Bucharest system with the Energy Efficiency Directive 2023/1791. Drawing on system data, investment modelling and local resource mapping from the LIFE22-CET-SET_HEAT project, it evaluates scenarios for 2028 and 2035 that shift generation from natural gas to renewable, waste heat and high-efficiency sources. Options include large-scale heat pumps, waste-to-energy, geothermal and solar heat. Heat demand profiles and electricity price dynamics are used to evaluate economic feasibility and operational flexibility. The findings show that technical decarbonisation is possible, but financial viability hinges on phased investments, regulatory reforms and access to EU funding. The study concludes with recommendations for staged implementation, coordinated governance and socio-economic measures to safeguard affordability and reliability.

Abstract: Indonesia’s long-term climate strategy targets net-zero emissions by 2060; in this context, this paper develops a simulation for the Java–Madura–Bali (Jamali) grid to quantify the joint impact of electric-vehicle (EV) uptake and rooftop photovoltaic (PV) “negative load” on system performance over 2025–2060. Historical statistics and national planning projections are used to calibrate annual capacity, peak load, and energy trajectories, which are then downscaled to a 168-hour representative week and a 365-day year. EV charging demand is generated from an hourly initial-charging-time distribution with state-of-charge–dependent AC/DC profiles, while rooftop PV is modeled using hourly irradiance and performance parameters. A 5×5 policy matrix (EV: BAU, Subsidy, Regulation, Charge-Time Management, Combination; PV: BAU, Subsidy, Regulation, Smart-Home, Combination) is evaluated using a min–max composite index (40% weekly supply-demand balance, 40% annual production–consumption balance, 20% policy cost), where higher values are preferable. Results show that EV3_PV1 (regulated EV growth with BAU PV) achieves the highest average composite score, EV4_PV1 provides the best adequacy, and EV3_PV3 yields the poorest adequacy, while EV3_PV1 and EV1_PV3 define the best and worst long-term production–consumption balance, respectively. EV1_PV1 is the least costly pathway and EV5_PV5 the most expensive, indicating that moderate, regulation-driven and charge-time–oriented strategies outperform highly interventionist EV–PV packages when adequacy, balance, and cost are jointly considered.

Abstract: This systematic review on intelligent HVAC systems for residential buildings focuses on advanced control techniques and AI applications. Model Predictive Control (MPC) is the most common method (≈40% of studies), achieving 15–20% energy savings and 10–30% peak demand reduction. Deep reinforcement learning (DRL) offers a model-free alter-native, reducing energy costs by 15% and comfort violations by up to 98%. Neural networks (LSTM, CNN-BiLSTM, attention mechanisms) significantly improve load pre-diction and thermal comfort modelling, with fusion models boosting accuracy by 66–85%. Comprehensive AI-based systems deliver 22–44% energy savings and 22–86% comfort improvements. Performance varies by climate, building type, and baseline; field trials show lower but more reliable savings than simulations. Hybrid MPC–ML approaches are emerging as best practice. Barriers include model complexity, computational demands, limited training data, and integration with legacy systems. Occupancy-aware strategies save 19–45% energy, while intelligent thermal storage can raise solar fractions from 11% to 61%. Overall, intelligent HVAC control is technically feasible and economically advantageous, but success depends on accurate modelling, tailored control strategies, and robust han-dling of occupancy uncertainty.

Abstract: Wastewater is an abundant yet underutilized source of thermal energy. Integrating wastewater flow with heat exchangers and heat pumps is a promising method for addressing buildings' heating and cooling requirements. This approach not only enhances energy efficiency but also promotes sustainability in buildings. This study explores the techno-economic and environmental potential of such a system, known as a Wastewater Energy Transfer (WET) system. An energy model was developed to simulate and compare the performance of a WET system with an existing conventional Heating, Ventilation and Air Conditioning (HVAC) system. Using local greenhouse gas (GHG) emissions factors, utility rates, and weather data, the model calculated both systems' comparative energy consumption, operating costs, and GHG emissions. The models were created to determine the project's economic and environmental viability. Toronto Metropolitan University (formerly Ryerson University) campus buildings were utilized for a case study and implementation of a WET system. The analysis included six Canadian cities of Halifax, Montreal, Toronto, Winnipeg, Calgary, and Vancouver, with varying climates and energy infrastructures. Montreal had the highest operating cost savings at $2,057,855, while Calgary had the lowest at $128,544. Winnipeg led in GHG reductions, offsetting 5,464 tonnes annually, whereas Montreal had the smallest reduction at 21 tonnes.

Abstract: This study investigated the degradation and contamination behavior of 41 real-world operational Marine Diesel Fuel samples, conforming to ELOT ISO 8217:2024 (DFA category). Samples were sourced directly from land-based supply tanks. To assess fuel degradation, a comprehensive suite of parameters was evaluated, including fuel characteristics such as viscosity and density. Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) was employed for elemental analysis to determine the content of wear and other metallic contaminants. Elevated concentrations of various metals were detected, suggesting potential leaching from system components within the storage infrastructure. Notable elemental concentrations included Iron (Fe up to 1.38 mg/kg), Copper (Cu up to 0.401 mg/kg), Lead (Pb up to 0.358 mg/kg), Aluminum (Al up to 0.218 mg/kg), Zinc (Zn up to 1.331 mg/kg), Nickel (Ni up to 0.172 mg/kg), Calcium (Ca up to 8.054 mg/kg), Sodium (Na up to 0.332 mg/kg), Phosphorous (P up to 0.602 mg/kg), and Silicon (Si up to 8.249 mg/kg). The presence of these contaminants in marine fuels, if bunkered, poses a significant risk of impaired engine performance, including injector fouling and ash formation. Critically, this study suggests that FAME content is not the primary driver of the observed oxidation and subsequent metallic degradation.

Abstract: Hot-fluid injection in thermal enhanced oil recovery (TEOR) imposes temperature-driven volumetric strains that can substantially alter in-situ stresses, fracture geometry, and wellbore/reservoir integrity, yet existing TEOR modeling has not fully captured coupled thermo-poroelastic effects on fracture aperture, fracture-tip behavior, and stress rotation within a displacement discontinuity method (DDM) framework. This study develops a fully coupled thermo-poroelastic DDM formulation in which fracture-surface normal and shear displacement discontinuities, together with fluid and heat influx, act as boundary sources to compute time-dependent stresses, pore pressure, and temperature, while internal fracture fluid flow (Poiseuille-based volume balance), heat transport (conduction–advection with rock exchange), and mixed-mode propagation criteria are included. A representative scenario considers an initially isothermal hydraulic fracture grown to 32 m, followed by 12 months of hot-fluid injection with temperature contrasts of ΔT = 0-100 °C and reduced pumping rate. Results show that hydraulic fracture aperture increases under isothermal and modest heating (ΔT = 25 °C) and remains nearly stable near ΔT = 50 °C, but progressively narrows for ΔT = 75-100 °C despite continued injection, indicating potential injectivity decline driven by thermally induced compressive stresses. Hot injection also tightens fracture tips, restricting unintended propagation, and produces pronounced near-fracture stress amplification and re-orientation: minimum principal stress increases by 6 MPa for ΔT = 50 °C and 10 MPa for ΔT = 100 °C, with principal-stress rotation reaching 70–90° in regions above and below the fracture and markedly elevated shear stresses that may promote natural-fracture activation. These findings demonstrate that thermo-poroelastic coupling can govern fracture stability, containment, and injectivity during thermal EOR, motivating temperature-aware geomechanical risk assessment and design for long-term hot-fluid injection operations.

Abstract: In this work, hydrogenated amorphous silicon carbide (a-SiCx​:H) and hydrogenated amorphous silicon oxide (a-SiOx​:H) films with similar optical bandgaps (Eg​), refractive indices (n), and extinction coefficients (k) were fabricated using pulse-wave modulation plasma technology by controlling the plasma turn-on and turn-off time ratio (ton​​/toff​). These films were placed at the 1/4 position of the p/i and i/n interfaces of hydrogenated amorphous silicon (a-Si:H) p-i-n solar cells to investigate their influence on solar cell performance. Experimental results showed that when deviations in Eg​, n, and k were less than 0.2%, 1.4%, and 4.1%, respectively, placing a-SiCx​:H and a-SiOx​:H films at the p/i and i/n interfaces increased the open-circuit voltage (Voc​​) and decreased the short-circuit current due to valence band (ΔEv​) or conduction band (ΔEc​​) offsets. The reduction in cell fill factor (FF) and efficiency (η) caused by placing a-SiCx​:H and a-SiOx​:H films at the p/i interface was greater than that caused by placing them at the i/n interface. Placing the a-SiCx​:H film at the p/i interface significantly improved the Voc​​. Due to the n-type doping effect of oxygen atoms, the a-SiOx​:H film exhibited the lowest FF and η at the p/i interface; however, when placed at the i/n interface, it yielded FF and η values second only to the a-Si:H standard reference cell. Appropriately placing the a-SiCx​:H film at the p/i interface and the slightly n-type a-SiOx​:H film at the i/n interface can effectively improve the Voc​​, FF, and η of p-i-n solar cells.

Abstract: As new energy technology systems grow increasingly complex, corporate innovation activities in photovoltaics, energy storage, wind power, and energy management exhibit distinct distributed characteristics. Cross-enterprise collaboration and cross-regional innovation have become pivotal drivers for industrial upgrading. However, the lengthy new energy industrial chain, numerous participants, and high technical coupling lead to significant knowledge stickiness in knowledge transfer processes, thereby impairing collaborative innovation efficiency.To address this issue, this paper constructs a probabilistic resource allocation optimization model for distributed innovation networks in new energy enterprises. First, Least Squares Support Vector Machine (LSSVM) is employed to predict the success rate of collaborative innovation tasks. Model parameters are jointly optimized using grid search and Particle Swarm Optimization (PSO) under RBF, Polynomial, and Sigmoid kernel functions to adapt to different types of technological coupling and knowledge stickiness structures.Subsequently, a Gaussian Copula is introduced to characterize the risk dependency structure among technological maturity, external market volatility, and R&D collaboration complexity between innovation nodes. This enables the calculation of conditional value at risk (CoVaR) to quantify collaborative innovation risks under high uncertainty in new energy technologies. During resource allocation, a cost-loss model based on Bayesian decision theory is established, and the simulated annealing algorithm is employed to solve for the optimal combination of R&D and collaborative resources.Simulation results demonstrate that under conditions of rapid new energy technology iteration and significant knowledge stickiness, this method reduces resource waste by 17%–23% compared to traditional empirical allocation approaches while enhancing cross-enterprise innovation collaboration efficiency. This study provides a quantifiable and interpretable decision-making method for distributed innovation management in the new energy industry, holding significant implications for building an efficient industrial collaborative innovation ecosystem.

Abstract: Against the backdrop of continuously increasing renewable energy penetration, enterprises within the power system have formed highly distributed collaborative innovation networks in wind power, photovoltaics, and energy storage. However, knowledge stickiness resulting from complex knowledge structures and dispersed organizational boundaries significantly impedes technology diffusion efficiency.To quantify this impact, this study constructs a distributed innovation network comprising 96 nodes and 1,243 collaborative links based on joint patent, cooperative R&D, and technology licensing data from 96 power equipment and new energy enterprises between 2010 and 2023. Knowledge distance between enterprises is calculated using technology classification codes and patent citation information, thereby constructing a knowledge stickiness index.By measuring diffusion trajectories across 14 specialized new energy technology domains, diffusion efficiency is characterized using weighted shortest paths, average diffusion strides, and diffusion coverage rates. A panel regression model and spatial lag model are established, incorporating knowledge stickiness indices, network centrality, structural hole constraints, and firm absorption capacity.Empirical results indicate that a 0.1-unit increase in the knowledge stickiness index raises the average diffusion stringency of new energy technologies by approximately 7.4% and reduces the three-year diffusion coverage rate by about 5.8%, with the model's overall explanatory power ranging between 0.42 and 0.57.Leading power enterprises with high network intermediary centrality partially offset the adverse effects of knowledge stickiness, achieving diffusion efficiency approximately 12–15% higher than peripheral firms within their technological sub-networks. This study quantifies the impact of knowledge stickiness within distributed innovation networks in power systems, providing empirical foundations for designing collaborative promotion strategies for new energy technologies and cultivating critical nodes.