Search Results (1,107)

Amid escalating climate challenges, China’s carbon neutrality objectives necessitate energy electrification as a pivotal strategy. As a critical load hub, East China demonstrates significant trends toward cleaner energy—marked by growing renewable energy penetration and accelerated cross-regional direct current (DC) transmission deployment. Ensuring stable and efficient grid operation requires rigorous assessment of the impacts of ultra-high voltage DC (UHVDC) integration on grid stability. This study introduces the transient multi-feed-in short circuit ratio (TMSCR), a novel metric for evaluating new DC transmission systems’ influence on grid performance. We systematically investigate UHVDC integration within the East China power grid, emphasizing strategic DC landing point placement. Using TMSCR, the effects of diverse DC incorporation methods are analyzed. Furthermore, this research examines impacts of new DC connections on local and main grids, proposing targeted mitigation measures to enhance grid resilience. This comprehensive UHVDC impact analysis addresses a critical literature gap, providing actionable insights for East China power grid planning and establishing a foundation for subsequent grid planning and DC project feasibility studies during the ‘15th Five-Year Plan’ period. Full article

Digital twin technology is reshaping modern agriculture. Digital twins are the virtual replicas of real-world farming systems, which are continuously updated with real-time data, and are revolutionizing the monitoring, simulation, and optimization of agricultural processes. The literature on agricultural digital twins is multidisciplinary, growing rapidly, and often fragmented across disciplines, which lacks well-curated documentation. A bibliometric analysis includes thematic content analysis and science mapping, which provides research trends, gaps, thematic landscape, and key contributors in this continuously evolving and emerging field. Therefore, in this study, we conducted a bibliometric review that included collecting bibliometric data via keyword search strategies on popular scientific databases. The data was further screened, processed, analyzed, and visualized using bibliometric tools to map research trends, landscapes, collaborations, and themes. Key findings show that publications have grown exponentially since 2018, with an annual growth rate of 27.2%. The major contributing countries were China, the USA, the Netherlands, Germany, and India. We observed a collaboration network with distinct geographic clusters, with strong intra-European ties and more localized efforts in China and the USA. The analysis identified seven major research theme clusters revolving around precision farming, Internet of Things integration, artificial intelligence, cyber–physical systems, controlled-environment agriculture, sustainability, and food system applications. We observed that core technologies, such as sensors, artificial intelligence, and data analytics, have been extensively explored, while identifying gaps in research areas. The emerging interests include climate resilience, renewable-energy integration, and supply-chain optimization. The observed transition from task-specific tools to integrated, system-level approaches underline the growing need for adaptive, data-driven decision support. By outlining research trends and identifying strategic research gaps, this review offers insights into leveraging digital twins to improve productivity, sustainability, and resilience in global agriculture. Full article

This study presents a high-performance external strengthening strategy for reinforced concrete (RC) beam–column joints, integrating near-surface mounted (NSM) Carbon Fiber Reinforced Polymer (C-FRP) ropes with externally bonded C-FRP sheets. The X-shaped ropes, anchored diagonally on both principal joint faces and complemented by vertical ropes at column corners, provide enhanced core confinement and shear reinforcement. C-FRP sheets applied to the beam’s plastic hinge region further increase flexural strength and delay localized failure. Three full-scale, shear-deficient RC joints were subjected to cyclic lateral loading. The unstrengthened specimen (JB0V) exhibited rapid stiffness deterioration, premature joint shear cracking, and unstable hysteretic behavior. In contrast, the specimen strengthened solely with X-shaped C-FRP ropes (JB0VF2X2c) displayed a markedly slower rate of stiffness degradation, delayed crack development, and improved energy dissipation stability. The fully retrofitted specimen (JB0VF2X2c + C-FRP) demonstrated the most pronounced gains, with peak load capacity increased by 65%, equivalent viscous damping enhanced by 55%, and joint shear deformations reduced by more than 40%. Even at 4% drift, it retained over 90% of its peak strength, while localizing damage away from the joint core—a performance unattainable by the unstrengthened configuration. These results clearly establish that the combined C-FRP rope–sheet system transforms the seismic response of deficient RC joints, offering a lightweight, non-invasive, and rapidly deployable retrofit solution. By simultaneously boosting shear resistance, ductility, and energy dissipation while controlling damage localization, the technique provides a robust pathway to extend service life and significantly enhance post-earthquake functionality in critical structural connections. Full article

Community-scale biomass power plants (CSBPPs) offer a decentralized approach for electricity generation by utilizing locally available biomass while delivering socioeconomic benefits. Site selection plays a critical role in the success of CSBPPs and requires the consideration of diverse spatial and non-spatial factors. This study presents a spatial decision-support tool for identifying suitable CSBPP sites in Thailand’s Eastern Economic Corridor (EEC), which comprises the Chachoengsao, Chonburi, and Rayong provinces. A geoprocessing workflow integrating Geographic Information Systems (GISs), Multi-Criteria Decision-Making (MCDM), and the Analytic Hierarchy Process (AHP) was developed using ModelBuilder tools in ArcGIS Pro (version 3.0.2). Thirteen sub-criteria related to geographical, infrastructural, and socioeconomic–cultural dimensions, along with exclusion zones, were evaluated by 15 experts from diverse stakeholder groups. Biomass availability from five major economic crops was combined with other spatial data layers, incorporating expert-assigned weights and suitability scores. The findings indicated a remaining biomass energy potential was 34,156 TJ, with sugarcane residues contributing over 80%. Approximately 20% of the EEC area (about 0.262 million hectares) was classified as highly suitable for CSBPP development, revealing several viable site options. The proposed model offers a flexible and replicable framework for regional biomass planning and can be adapted to other locations by adjusting the criteria and integrating optimization techniques. Full article

This study evaluates the viability of a specific hybrid renewable energy system (HRES) installation designed for a remote community as a case study in Cuba. The system integrates solar, wind, and biomass resources to address localised challenges of energy insecurity and environmental degradation. Rather than offering a generalised evaluation of HRES technologies, this work focuses on the performance, impacts, and viability of this particular configuration within its unique geographical, social, and technical context. Using life cycle assessment (LCA) and input–output modelling, the research assesses environmental and socioeconomic impacts. The proposed HRES reduces greenhouse gas emissions by 60% (from 1.14 to 0.47 kg CO₂eq/kWh) and fossil energy consumption by 50% compared to diesel-based systems. Socioeconomic analysis reveals that the system generates 40.3 full-time equivalent (FTE) jobs, with significant employment opportunities in operation and maintenance. However, initial investments primarily benefit foreign suppliers due to Cuba’s reliance on imported components. The study highlights the potential for local economic gains through workforce training and domestic manufacturing of renewable energy technologies. These findings underscore the importance of integrating multiple renewable sources to enhance energy resilience and sustainability in Cuba. Policymakers should prioritise strategies to incentivise local production and capacity building to maximise long-term benefits. Future research should explore scalability across diverse regions and investigate policy frameworks to support widespread adoption of HRES. This study provides valuable insights for advancing sustainable energy solutions in Cuba and similar contexts globally. Full article

In rural Ghana, limited access to affordable, clean cooking fuels drives the need for decentralised waste-to-energy solutions. Anaerobic co-digestion (AcoD) offers a viable route for transforming organic residues into renewable energy, with the added benefit of improved process stability resulting from substrate synergy. This study aims to evaluate the technical feasibility and stabilisation challenges of AcoD, using locally available fruit waste and beet molasses at a secondary school in Bedabour (Ghana). Biological methane potential (BMP) assays of different co-digestion mixtures were conducted at two inoculum-to-substrate (I/S) ratios (2 and 4), identifying the highest yield (441.54 ± 45.98 NmL CH₄/g VS) for a mixture of 75% fruit waste and 25% molasses at an I/S ratio of 4. Later, this mixture was tested in a 6 L semi-continuous AcoD reactor. Due to the high biodegradability of the substrates, volatile fatty acid (VFA) accumulation led to acidification and process instability. Three low-cost mitigation strategies were evaluated: (i) carbonate addition using eggshell-derived sources, (ii) biochar supplementation to enhance buffering capacity, and (iii) the integration of a bioelectrochemical system (BES) into the AcoD recirculation loop. The BES was intended to support VFA removal and enhance methane recovery. Although they temporarily improved the biogas production, none of the strategies ensured long-term pH stability of the AcoD process. The results underscore the synergistic potential of AcoD to enhance methane yields but also reveal critical stability limitations under high-organic-loading conditions in low-buffering rural contexts. Future implementation studies should integrate substrates with higher alkalinity or adjusted organic loading rates to ensure sustained performance. These findings provide field-adapted insights for scaling-up AcoD as a viable renewable energy solution in resource-constrained settings. Full article

Erosive wear from suspended sediments significantly threatens the structural integrity and efficiency of composite tidal turbine blades. This study develops a novel framework for predicting erosion in FR4 glass fibre-reinforced polymers (GFRPs)—materials increasingly adopted for marine renewable energy components. While erosion models exist for metals, their applicability to heterogeneous composites with unique failure mechanisms remains unvalidated. We calibrated the Oka erosion model specifically for FR4 using a complementary experimental–computational approach. High-velocity slurry jet tests (12.5 m/s) were conducted at a 90° impact angle, and erosion was quantified using both gravimetric mass loss and surface profilometry. It revealed a distinctive W-shaped erosion profile with 3–6 mm of peak material removal from the impingement centre. Concurrently, CFD simulations employing Lagrangian particle tracking were used to extract local impact velocities and angles. These datasets were combined in a constrained nonlinear optimisation scheme (SLSQP) to determine material-specific Oka model coefficients. The calibrated coefficients were further validated on an independent 45° impingement case (same particle size and flow conditions), yielding 0.0143 g/h predicted versus 0.0124 g/h measured (15.5% error). This additional case confirms the accuracy and feasibility of the predictive model under input conditions different from those used for calibration. The calibrated model achieved strong agreement with measured erosion rates (R² = 0.844), successfully capturing the progressive matrix fragmentation and fibre debonding, the W-shaped erosion morphology, and highlighting key composite-specific damage mechanisms, such as fibre detachment and matrix fragmentation. By enabling the quantitative prediction of erosion severity and location, the calibrated model supports the optimisation of blade profiles, protective coatings, and maintenance intervals, ultimately contributing to the extended durability and performance of tidal turbine systems. This study presents a procedure and the output of calibration for the Oka erosion model, specifically for a composite material, providing a transferable methodology for erosion prediction in GFRPs subjected to abrasive marine flows. Full article

Digital livestock farming faces a critical deployment challenge: bridging the gap between cutting-edge AI algorithms and practical implementation in resource-constrained agricultural environments. While deep learning models demonstrate exceptional accuracy in laboratory settings, their translation to operational farm systems remains limited by computational constraints, connectivity issues, and user accessibility barriers. Dairy DigiD addresses these challenges through a novel edge-cloud AI framework integrating YOLOv11 object detection with DenseNet121 physiological classification for cattle monitoring. The system employs YOLOv11-nano architecture optimized through INT8 quantization (achieving 73% model compression with <1% accuracy degradation) and TensorRT acceleration, enabling 24 FPS real-time inference on NVIDIA Jetson edge devices while maintaining 94.2% classification accuracy. Our key innovation lies in intelligent confidence-based offloading: routine detections execute locally at the edge, while ambiguous cases trigger cloud processing for enhanced accuracy. An entropy-based active learning pipeline using Roboflow reduces the annotation overhead by 65% while preserving 97% of the model performance. The Gradio interface democratizes system access, reducing technician training requirements by 84%. Comprehensive validation across ten commercial dairy farms in Atlantic Canada demonstrates robust performance under diverse environmental conditions (seasonal, lighting, weather variations). The framework achieves mAP@50 of 0.947 with balanced precision-recall across four physiological classes, while consuming 18% less energy than baseline implementations through attention-based optimization. Rather than proposing novel algorithms, this work contributes a systems-level integration methodology that transforms research-grade AI into deployable agricultural solutions. Our open-source framework provides a replicable blueprint for precision livestock farming adoption, addressing practical barriers that have historically limited AI deployment in agricultural settings. Full article

The growing importance of microgrids—linking buildings with distributed energy resources and storage—is driving the evolution of Smart Local Energy Systems (SLESs). These systems require advanced modeling and simulations to address growing complexity, decentralization, and interoperability. This review presents an analysis of commonly used environments and methods applied in the design and operation of SLESs. Particular emphasis is placed on their capabilities for multi-domain integration, predictive control, and smart automation. A novel contribution is the identification of Closed Ecological Systems (CES) and Life Support Systems (LSSs)—fully or semi-isolated environments designed to sustain human life through autonomous recycling of air, water, and other resources—as promising new application domains for SLES technologies. This review explores how concepts developed for building and energy systems, such as demand-side management, IoT-based monitoring, and edge computing, can be adapted to CES/LSS contexts, which demand isolation, autonomy, and high reliability. Challenges related to model integration, simulation scalability, and the bidirectional transfer of technologies and modeling between Earth-based and space systems are discussed. This paper concludes with a SWOT analysis and a roadmap for future research. This work lays the foundation for developing sustainable, intelligent, and autonomous energy infrastructures—both terrestrial and extraterrestrial. Full article

The urgent need for sustainable food systems in Indonesia is hindered by urban planning policies that are disconnected from food security priorities. Smart city planning policies in Indonesia have been subject to numerous misconceptions compared to successful implementations in developed countries. This study examines the relationship between urban planning policies and architectural design in fostering sustainable food systems, employing a mixed-methods approach that combines multiple linear regression analysis with a sample of 75 smart cities, correlation analysis, and case studies from six representative cities that demonstrate best practices. Key findings reveal that food security is significantly undermined by the Gross Regional Domestic Product (GRDP), indicating distributional inequalities, high food expenditure, and a lack of clean water, while access to electricity improves resilience. Case study analysis showed that Semarang is the city with the highest readiness level (97%), followed by Makassar (91%), which employs a Holistic Benchmark approach, Jakarta (91%), which follows a Technological—fragmented approach, Samarinda (86%) and Medan (79%), which are in a Developing Transition phase, and Surabaya (66%), which utilizes a Community and Local Initiatives approach. Each city adopted a different approach, which means the national strategy for developing Smart Cities will also differ; however, they must prioritize equitable infrastructure and architectural innovation, such as urban farming integration and a water–energy–food nexus system. Smart cities extend beyond technological innovations, encompassing integrated urban planning policies and architectural practices that foster sustainable food systems through infrastructure management and environmental sustainability. Full article

Fuel cells have become a fundamental technology in the development of clean energy systems, playing a vital role in the global shift toward a low-carbon future. With the growing need for sustainable hydrogen production, perfluoro sulfonic acid (PFSA) ionomer membranes play a critical role in optimizing green hydrogen technologies and fuel cells. This study aims to investigate the effects of different environmental and solvent treatments on the chemical and physical properties of Nafion N−115 membranes to evaluate their suitability for both hydrogen production in proton exchange membrane (PEM) electrolyzers and hydrogen utilization in fuel cells, supporting integrated applications in the local and global green hydrogen economy. To achieve this, Nafion N−115 membranes were partially dissolved in various solvent mixtures, including ethanol/isopropanol (EI), isopropanol/water (IW), dimethylformamide/N-methyl-2-pyrrolidone (DN), and ethanol/methanol/isopropanol (EMI), evaluated under water immersion and thermal stress, and characterized for chemical stability, mechanical strength, water uptake, and proton conductivity using advanced electrochemical and spectroscopic techniques. The results demonstrated that the EMI-treated membrane showed the highest proton conductivity and maintained its structural integrity, making it the most promising for hydrogen electrolysis applications. Conversely, the DN-treated membrane exhibited reduced stability and lower conductivity due to solvent-induced degradation. This study highlights the potential of EMI as an optimal solvent mixture for enhancing PFSA membranes performance in green hydrogen production, contributing to the advancement of sustainable energy solutions. Full article

Rapid urban growth poses distinct energy and environmental challenges in various regions of the world. This study evaluated the technical and economic feasibility of a grid-connected photovoltaic system in Santo Domingo Tehuantepec, Oaxaca, Mexico, using Homer Pro software, version 3.14.2, to simulate realistic scenarios. The analysis incorporated local climate data, residential load profiles, and updated economic parameters for 2024. System optimization resulted in an installed capacity of 173 kW of solar panels and 113 kW of inverters, yielding a levelized cost of energy (LCOE) of MXN 1.43/kWh, a return on investment (ROI) of 5.3%, an internal rate of return (IRR) of 8%, and a simple payback period of 10 years. The projected annual energy output was 281,175 kWh, covering 36% of the local energy demand. These results highlight the potential for integrating renewable energy into urban contexts, offering significant economic and environmental benefits. The integration of public policy with urban planning can enhance energy resilience and sustainability in intermediate cities. This study also supports the application of tools such as Homer Pro in designing energy solutions tailored to local conditions and contributes to a fair and decentralized energy transition. Full article

Renewable Energy Communities (RECs) are central to Europe’s strategy for reducing greenhouse gas emissions and advancing a sustainable, decentralized energy system. RECs aim to transform consumers into prosumers—individuals who both produce and consume energy—thereby enhancing energy efficiency, local autonomy, and citizen engagement. This study introduces a novel Geographic Information System (GIS)-based methodology that integrates socio-economic and spatial data to support the design of optimal REC configurations. QGIS 3.40.9 “Batislava” tool is used to simulate site-specific energy distribution scenarios, enabling data-driven planning. By combining a Composite Energy Vulnerability Index (CEVI), Rooftop Solar Potential (RSP), and the distribution of urban gardens (UGs), the approach identifies priority urban zones for intervention. Urban gardens offer multifunctional public spaces that can support renewable infrastructures while fostering local resilience and energy equity. Applied to the city of Rome, the methodology provides a replicable framework to guide REC deployment in vulnerable urban contexts. The results demonstrate that 11 of the 18 highest-priority areas already host urban gardens, highlighting their potential as catalysts for collective PV systems and social engagement. The proposed model advances sustainability objectives by integrating environmental, social, and spatial dimensions—positioning RECs and urban agriculture as synergistic tools for inclusive energy transition and climate change mitigation. Full article

This study presents a simulation-based feasibility analysis of a beam steering metasurface, theoretically driven by mechanical energy harvested from human motion via a triboelectric nanogenerator (TENG). In the proposed model, the TENG converts biomechanical motion into alternating current (AC), which is rectified into direct current (DC) to bias varactor diodes integrated into each metasurface unit cell. These bias voltages are numerically applied to dynamically modulate the local reflection phase, enabling beam steering without external power. Full-wave electromagnetic simulations were conducted to confirm the feasibility of beam manipulation under TENG-generated voltage levels. The proposed simulation-driven design offers a promising framework for battery-free, adaptive electromagnetic control with potential applications in wearable electronics, intelligent sensing, and energy-autonomous radar systems. Full article

To address the increasing need for clean energy and efficient resource utilization, this paper aims to provide a cooperative framework and a fair profit allocation mechanism for integrated photovoltaic (PV) and energy storage systems that are shared among different types of users within a regional alliance, including industrial, commercial, and residential users. A cooperative game model is proposed and formulated by a two-level optimization problem: the upper level determines the optimal PV and storage capacities to maximize the alliance’s net profit, while the lower level allocates profits using an improved Nash bargaining approach based on Shapley value. The model simultaneously incorporates different real-world factors such as time-of-use electricity pricing, system life cycle cost, and load diversity. The results demonstrate that coordination between energy storage systems and PV systems can avoid 18% of solar curtailment losses. Compared to independent deployment by individual users, the cooperative sharing model increases the net present value by 8.41%, highlighting improvements in cost-effectiveness, renewable resource utilization, and operational flexibility. Users with higher demand or better load–generation matching gain greater economic returns, which can provide decision-making guidance for the government in formulating differentiated subsidy policies. Full article