Search Results (269)

Under the “dual carbon” goals and rapid clean energy development, power grids face challenges including rapid load growth, uneven power flow distribution, and limited transmission capacity. This paper proposes a novel FACTS device with fault tolerance and switchable topology that maintains power flow control over multiple lines during N-1 faults, enhancing grid safety and economy. The paper establishes a steady-state mathematical model based on additional virtual nodes and provides power flow calculation methods to accurately reflect the device’s control characteristics. An entropy-weighted TOPSIS method was employed to establish a quantitative evaluation system for assessing the grid performance improvement after FACTS device integration. To address interaction issues among multiple flexible devices, an optimization planning model considering th3e coordinated effects of UPFC and VSC-HVDC was constructed. Multi-objective particle swarm optimization obtained Pareto solution sets, combined with the evaluation system, to determine the optimal configuration schemes. Considering wind power uncertainty and fault risks, we propose a system-level coordinated operation strategy. This strategy constructs probabilistic risk indicators and introduces topology switching control constraints. Using particle swarm optimization, it achieves a balance between safety and economic objectives. Simulation results in the Jiangsu power grid scenarios demonstrated significant advantages in enhancing the transmission capacity, optimizing the power flow distribution, and ensuring system security. Full article

This paper introduces sustainable engineering systems built using digital twin technology and circular economy principles. This research presents a framework for monitoring, modeling, and making decisions in real timusing virtual replicas of physical products, processes, and systems in product lifecycles. A digital twin was used to show that through a digital twin, waste was reduced by 27%, energy consumption was reduced by 32%, and the resource recovery rate increased to 45%. The proposed approach under the framework employs various machine learning algorithms, IoT sensor networks, and advanced data analytics to support closed-loop flows of materials. The results show how digital twins can enhance progress toward the goals the circular economy sets to identify inefficiencies, predict maintenance needs, and optimize the use of resources. This integration is a promising industry approach that will introduce more sustainable operations and maintain economic viability. Full article

This paper deals with an important issue, which is the influence of failure caused by the quality of matrix post-curing on the strength of complex and difficult materials of the “new generation” such as fibre composites, particularly with a polymer matrix. In recent years, significant advances in the field of adhesive materials chemistry have led to the constant development of bonding technology. The effectiveness of bonding depends, to a large extent, on the suitable selection of the adhesive and the use of appropriate surface treatment technology. It is difficult to imagine virtually any modern industry without adhesive joints, be it the aircraft, aerospace or automotive industries, which simultaneously highlights the great importance of adhesives and adhesive materials for the present-day economy. In modern technology, it is extremely important to obtain the right combination of modern construction materials. The statistical analysis of the components showed the complexity of the layered composite structure. The proposed model of the weakest micro-volume developed in this study indirectly reflects the experimentally based curing variables that affect the stresses of the components in the composite (laminate) structure. The strength of fibrous composite structures based on the Markov chain theory considers technological aspects during hardening. The model proposed in the paper was validated on the basis of examples from the literature and experimental data obtained in the research project. The numerical results are in good agreement with the literature database and measurement data. The presented model could be a novel method, which allows better insight into the curing process of epoxy resins. Full article

In islanded microgrids with high-proportion renewable energy, the disconnection from the main grid leads to the characteristics of low inertia, weak damping, and high impedance ratio, which exacerbate the safety risks of frequency and voltage. To balance the requirements of system operation economy and frequency–voltage safety, a coordinated optimization scheduling method for frequency and voltage in islanded microgrids considering the active support of battery energy storage (BES) is proposed. First, to prevent the state of charge (SOC) of BES from exceeding the frequency regulation range due to rapid frequency adjustment, a BES frequency regulation strategy with an adaptive virtual droop control coefficient is adopted. The frequency regulation capability of BES is evaluated based on the capacity constraints of grid-connected converters, and a joint frequency and voltage regulation strategy for BES is proposed. Second, an average system frequency model and an alternating current power flow model for islanded microgrids are established. The influence of steady-state voltage fluctuations on active power frequency regulation is analyzed, and dynamic frequency safety constraints and node voltage safety constraints are constructed and incorporated into the optimization scheduling model. An optimization scheduling method for islanded microgrids that balances system operation costs and frequency–voltage safety is proposed. Finally, the IEEE 33-node system in islanded mode is used as a simulation case. Through comparative analysis of different optimization strategies, the effectiveness of the proposed method is verified. Full article

In the context of global climate governance and the low-carbon energy transition, virtual power plant (VPP), a key technology for integrating distributed energy resources, is urgently needed to solve the problem of decentralization and lack of synergy in electricity, carbon, and green certificate trading. Existing studies mostly focus on single energy or carbon trading scenarios and lack a multi-market coupling mechanism supported by blockchain technology, resulting in low transaction transparency and a high risk of information tampering. For this reason, this paper proposes a synergistic optimization strategy for electricity/carbon/green certificate virtual power plants based on blockchain cross-chain transactions. First, Latin Hypercubic Sampling (LHS) is used to generate new energy output and load scenarios, and the K-means clustering method with improved particle swarm optimization are combined to cut down the scenarios and improve the prediction accuracy; second, a relay chain cross-chain trading framework integrating quota system is constructed to realize organic synergy and credible data interaction among electricity, carbon, and green certificate markets; lastly, the multi-energy optimization model of the virtual power plant is designed to integrate carbon capture, Finally, a virtual power plant multi-energy optimization model is designed, integrating carbon capture, power-to-gas (P2G) and other technologies to balance the economy and low-carbon goals. The simulation results show that compared with the traditional model, the proposed strategy reduces the carbon emission intensity by 13.3% (1.43 tons/million CNY), increases the rate of new energy consumption to 98.75%, and partially offsets the cost through the carbon trading revenue, which verifies the Pareto improvement of environmental and economic benefits. This study provides theoretical support for the synergistic optimization of multi-energy markets and helps to build a low-carbon power system with a high proportion of renewable energy. Full article

With the increasing penetration of renewable energy and the large-scale integration of electric vehicles (EVs), the economic optimization dispatch of EV-integrated virtual power plants (VPPs) faces multiple uncertainties and challenges. This paper first proposes an optimized dispatching model for EV clusters to form large-scale coordinated regulation capabilities. Subsequently, considering diversified resources such as energy storage systems and photovoltaic (PV) generation within VPPs, a low-carbon economic optimization dispatching model is established to minimize the total system operation costs and polluted gas emissions. To address the limitations of traditional algorithms in solving high-dimensional, nonlinear dispatching problems, this paper introduces a plant root-inspired growth optimization algorithm. By simulating the nutrient-adaptive uptake mechanism and branching expansion strategy of plant roots, the algorithm achieves a balance between global optimization and local fine-grained search. Compared with the genetic algorithm, particle swarm optimization algorithm and bat algorithm, simulation results demonstrate that the proposed method can effectively enhance the low-carbon operational economy of VPPs with high PV, ESS, and EV penetration. The research findings provide theoretical support and practical references for optimal dispatch of multi-stakeholder VPPs. Full article

This paper outlines a methodology for creating an educational and informative communication system for non-specialised audiences in order to preserve and pass on the heritage of ideas and practices adopted in the medieval political and administrative sphere. Through the combined use of digital technologies (such as GISs, 3D modelling and virtual tours), historical sources can potentially reveal how political and administrative aspects affected different areas within the medieval city, not just the main seats of power. Bologna, a prestigious medieval university metropolis, is chosen as a case study because of the remarkable wealth of documentation in its archives from the city’s political culture in the Middle Ages. Written historical sources, including documentary and narrative texts, are among the primary tools employed in the study of European medieval urban communities in general. Documentary sources help us understand and reconstruct the complexities of civic administration, urban policies and the economy, as well as how citizens experience them daily. The involvement of citizens in the political and administrative life of late medieval cities is explored through the management and digital processing of historical documentation. Digital humanities tools can facilitate this analysis, offering a perspective that sheds light on the formation of the pre-modern state. Although digital databases and repositories have significantly contributed to preserving and digitally archiving historical sources, these are often aimed exclusively at the academic level and remain underutilised as privileged didactic and educational tools for a broad audience. Full article

The rapid evolution of the Fourth Industrial Revolution has significantly transformed educational practices, necessitating the integration of advanced technologies into higher education to address contemporary sustainability challenges. This study explores the integration of interactive metaverse environments and generative artificial intelligence (GAI) in promoting the green digital economy and developing e-entrepreneurship skills among graduate students. Grounded in a quasi-experimental design, the research was conducted with a sample of 25 postgraduate students enrolled in the “Computers in Education” course at King Khalid University. A 3D immersive learning environment (FrameVR) was combined with GAI platforms (ChatGPT version 4.0, Elai.io version 2.5, Tome version 1.3) to create an innovative educational experience. Data were collected using validated instruments, including the Green Digital Economy Scale, the e-Entrepreneurship Scale, and a digital product evaluation rubric. The findings revealed statistically significant improvements in students’ awareness of green digital concepts, entrepreneurial competencies, and their ability to produce sustainable digital products. The study highlights the potential of immersive virtual learning environments and AI-driven content creation tools in enhancing digital literacy and sustainability-oriented innovation. It also underscores the urgent need to update educational strategies and curricula to prepare future professionals capable of navigating and shaping green digital economies. This research provides a practical and replicable model for universities seeking to embed sustainability through emerging technologies, supporting broader goals such as SDG 4 (Quality Education) and SDG 9 (Industry, Innovation, and Infrastructure). Full article

As awareness of the negative environmental impact of fashion grows, most companies are choosing to innovate in areas such as recycling and digital transformation. In the context of the rising digital economy and the ongoing development of 3D simulation software, there has been a notable increase in the demand for realistic 3D virtual-fitting effects. However, no standardized evaluation method exists for the realism of virtual fabric drape. This study proposes a systematic approach to enhance the objective evaluation and rapid optimization of virtual fabric drape realism. The research is structured in four stages. First, virtual drape testing conditions are established by referencing real-world fabric drape tests. Second, fuzzy classification is employed to categorize the realism of virtual drape effects into six levels. Third, subjective evaluations of representative fabrics are conducted to define the grading thresholds and reveal differences among the fabric types. Finally, a backpropagation (BP) neural network is used to construct three rapid evaluation models and one optimization model, which are validated through practical application. The proposed method supports accurate assessment and optimization of virtual simulations, contributes to a refined virtual fabric database, and offers insights for improving other 3D fitting software. Full article

Addressing the issue of efficient, economical, and reliable operation of a single lead-acid battery (LAB) black start system in complex scenarios, a hybrid energy storage system (HESS) black start scheme based on super capacitors (SCs) is proposed. The proposed solution mainly includes two aspects: an integrated structure and a control strategy. A topology structure with a direct parallel output on the AC side is adopted, and the SC is directly connected to the AC side of the LAB in the current source mode. Compared with traditional DC side access schemes, it can cope with large surge currents by a small capacity, and the economy of the HESS black start system has been effectively improved. In order to improve the dynamic characteristics of the black start control system, a self-adaptive control strategy based on the virtual synchronous generator (VSG) and model predictive control (MPC) is proposed. Based on the small signal disturbance model, the influence of the system parameters on stability was analyzed, and the control parameters are adjusted according to the angular velocity and frequency deviation. A generator recognition model at the ms level was constructed, and the set reference current according to the power level is brought into the MPC to track the reference current. Compared with existing methods, it can effectively suppress the disturbance of the black start system, and the fast responsiveness and stability of the control system is improved. Finally, real operational data is compared and analyzed. The results indicate that the proposed control strategy can accurately identify different black start scenarios, with lower configuration costs and good dynamic performance. Full article

The sustainable management of organic waste is a global priority due to its environmental impact and the increasing pressure on urban and rural systems, particularly in regions with limited technological infrastructure. This study introduces and validates a comprehensive methodology based on Digital Twins (DTs) to optimize production and service systems in organic waste management. The methodology includes contextual and propositional phases and is built on a modular three-layer architecture (physical, cloud, and virtual) that enables real-time monitoring, simulation, and feedback. It was validated through a field implementation in a composting facility in Cajamarca, Colombia. The results showed a 10% increase in composting efficiency and a monthly gain of 1200 kg of compost. A statistical analysis confirmed a significant increase in process efficiency (p < 0.001) and a reduction in performance variability (p < 0.01). The return on investment reached 18,957.6% using low-cost technology. These findings demonstrate the viability and adaptability of the proposed methodology for low-tech environments and support its potential for scaling in circular economy applications across waste management and agriculture. Full article

A company’s base, or physical location, is often a criterion or condition for inclusion in regional development programmes that offer investment incentives such as reduced taxes, subsidised loan rates, or funding for research and development projects. However, these programmes, aimed at strengthening communities lagging behind in economic development, are often the target of malicious exploitation by companies that have a virtual headquarters in the region without actually contributing to local economies. This study proposes the use of the Expenses Localness Indicators (ELI) model as a reliable indicator of a company’s real contribution to a local economy. The ELI model can measure and highlight attempts to erode a company’s headquarters, and also assess a company’s integration into the local economy. By simulating a virtual economic environment and generating synthetic transaction data, the effectiveness of the ELI model in detecting false location claims and quantifying regional participation is evaluated. The results show that companies that prioritise local partnerships maintain higher locality scores, while those that partner with low locality entities weaken their local economic footprint, regardless of physical location. The ELI model provides a transparent and reliable tool that can be used both to grant regional incentives and to monitor their performance. Its integration into policy design can support more equitable, evidence-based approaches to regional economic development and social investment. Full article

To promote low-carbon transformation and achieve carbon peak and neutrality in the energy field, this study proposes an operational optimization model considering the energy- and load-side dual response (ELDR) mechanism for electrothermal coupled virtual power plants (VPPs) containing a carbon capture device. The organic Rankine cycle (ORC) waste heat boiler (WHB) is introduced on the energy side. The integrated demand response (IDR) of electricity and heat is performed on the load side based on comprehensive user satisfaction (CUS), and the carbon capture system (CCS) is used as a flexible resource. Additionally, a carbon capture device operation mode that makes full use of new energy and the valley power of the power grid is proposed. To minimize the total cost, an optimal scheduling model of virtual power plants under ladder-type carbon trading is constructed, and opportunity-constrained planning based on sequence operation is used to address the uncertainty problems of new energy output and load demand. The results show that the application of the ELDR mechanism can save 27.46% of the total operating cost and reduce CO₂ emissions by 45.28%, which effectively improves the economy and low carbon of VPPs. In particular, the application of a CCS in VPPs contributes to reducing the carbon footprint of the system. Full article

This study evaluates the feasibility of repurposing produced water—an abundant byproduct of hydrocarbon extraction—for green hydrogen production in Wyoming, USA. Analysis of geospatial distribution and production volumes reveals that there are over 1 billion barrels of produced water annually from key basins, with a general total of dissolved solids (TDS) ranging from 35,000 to 150,000 ppm, though Wyoming’s sources are often at the lower end of this spectrum. Optimal locations for hydrogen production hubs have been identified, particularly in high-yield areas like the Powder River Basin, where the top 2% of fields contribute over 80% of the state’s produced water. Detailed water-quality analysis indicates that virtually all of the examined sources exceed direct electrolyzer feed requirements (e.g., <2000 ppm TDS, <0.1 ppm Fe/Mn for target PEM systems), necessitating pre-treatment. A review of advanced treatment technologies highlights viable solutions, with estimated desalination and purification costs ranging from USD 0.11 to USD 1.01 per barrel, potentially constituting 2–6% of the levelized cost of hydrogen (LCOH). Furthermore, Wyoming’s substantial renewable-energy potential (3000–4000 GWh/year from wind and solar) could sustainably power electrolysis, theoretically yielding approximately 0.055–0.073 million metric tons (MMT) of green hydrogen annually (assuming 55 kWh/kg H₂), a volume constrained more by energy availability than water supply. A preliminary economic analysis underscores that, while water treatment (2–6% LCOH) and transportation (potentially > 10% LCOH) are notable, electricity pricing (50–70% LCOH) and electrolyzer CAPEX (20–40% LCOH) are dominant cost factors. While leveraging produced water could reduce freshwater consumption and enhance hydrogen production sustainability, further research is required to optimize treatment processes and assess economic viability under real-world conditions. This study emphasizes the need for integrated approaches combining water treatment, renewable energy, and policy incentives to advance a circular economy model for hydrogen production. Full article

Distributed power trading is becoming the future development trend of electric energy trading, and virtual power plant (VPP), as a kind of aggregated optimization scheme to enhance energy utilization efficiency, has received more and more attention for studying distributed trading among multiple VPPs. However, how to guarantee the economy, credibility, security, and efficiency of distributed transactions is still a key issue to be overcome. To this end, a multi-VPP power sharing trusted transaction strategy based on dual-chain blockchain is proposed. First, a dual-chain blockchain electric energy transaction architecture is proposed. Then, the VPP-independent operation cost model is constructed, based on which, the decision model of multi-VPP electric energy sharing transaction based on Nash negotiation theory is constructed. Again, an improved-Practical Byzantine Fault Tolerant (I-PBFT) consensus algorithm combining the schnorr protocol with the Diffie–Hellman key exchange algorithm and a smart contract for multi-VPP electricity trading are designed to realize trusted, secure, and efficient distributed transactions. Finally, the example results verify the effectiveness of the strategy proposed in this paper. Full article