Search Results (7,053)

Power flow (PF) analysis is fundamental for power system operation and planning, yet traditional methods like Newton–Raphson face problems in convergence and computational efficiency. While deep learning (DL) offers promising solutions, its “black-box” nature and unstable training dynamics hinder practical adoption. This paper proposes a physics-informed neural network (PINN) framework integrated with a novel half-inverse gradient (HIG) mechanism to address these limitations. First, a systematic study of gradient scaling in PF optimisation found that the lack of enough inverse matrix compensation was the main cause of training instability. Second, we design a residual-driven HIG method that compensates gradient matrices via inverse operations, enabling accelerated convergence while maintaining numerical stability. Third, we develop parameterized voltage variables with differentiable activation functions to enforce hard operational constraints. The HIG optimizer leverages automatic differentiation and truncated singular value decomposition to balance diagonal/non-diagonal gradient information, achieving 99% accuracy in case4gs and case30 studies. Experiments on case118 demonstrate the framework’s scalability, with 65% accuracy compared to about 38% for baseline physics-informed approaches. Full article

The paper presents the results of the optimization of the paper production process as a function of paper grade, basis weight and key operating parameters of paper machines, wire speed V_s and reel speed V_n, using computer simulation. Based on empirical data from 2015 to 2020, the analysis accounts for web breaks, downtimes, and grade changes, all of which affect production continuity and the operation of the plant’s combined heat and power (CHP) system supplying thermal and electrical energy. Production interruptions reduce CHP stability; therefore, the optimization criterion was profit maximization, including energy-related effects associated with forecasting the availability of surplus energy for sale when participating in the capacity market. The results were compared with those obtained using numerical taxonomy methods, which confirmed the effectiveness of their application to the problem under consideration. Full article

This study proposes a coordinated allocation model for grid-forming energy storage and synchronous condensers considering transient stability constraints, with the following key aims: mitigate the continuous degradation of power systems’ capability to withstand inertia and the severe threats to dynamic rotor angle stability and frequency, while integrating renewable energy-centered frameworks using wind and photovoltaic power, and guarantee the secure and stable operation of transmitting power grids containing such bases. First, based on a virtual synchronous inertia quantification model of grid-forming energy storage and grid-forming wind and PV equipment, the inertia support capability of the renewable energy base is investigated. Subsequently, the impact of grid-forming equipment integration on transient rotor angle stability and frequency is studied, and a model of rotor angle stability and frequency constraints for the renewable energy base is established. Considering conditions such as investment cost constraints, transmission power constraints, and rotor angle stability and frequency constraints, a coordinated allocation model of grid-forming energy storage and synchronous condensers is formulated and solved to minimize the overall cost. Finally, the simulation verification results show that, compared with the configuration models that consider only the synchronous condenser or only the grid-forming energy storage, the proposed model reduces the comprehensive cost of the renewable energy base by 11.9% and 8.74%, respectively, reduces the minimized value of the power angle stability index by 80.95% and 78.95%, respectively, and meets the synchronous inertia demand of the renewable energy base throughout the period. Full article

With the increasing power fluctuations and growing pressure on grid stability resulting from the high penetration of renewable energy, the demand for exploring various energy storage technologies with large-scale, long-duration, and low-cost features has become increasingly urgent. This paper proposes a novel single-track gravity energy storage generation system. This system utilizes non-standardized masses (such as natural rocks) operating stably on an inclined track, and combines coordinated feedforward–feedback electromagnetic torque control, multi-station loading scheduling, and synchronous loading/unloading strategies to effectively smooth the power fluctuations of renewable energy sources such as wind power. The core innovations of this system lie in: (1) utilizing non-standardized mass units to achieve gravity energy storage, thereby expanding the application scenarios and design flexibility of solid gravity energy storage systems; and (2) introducing intelligent scheduling strategies and multi-station loading coordination to effectively smooth the power output fluctuations caused by load randomness, rendering the system insensitive to load variations. Simulation results verify that, for power smoothing in a 10 MW-level wind farm, the system can accurately track the target power and maintain a stable output over a long duration. The power fluctuations are controlled to under 0.2%, even when the total load varies by 10% and the instantaneous load fluctuates by 5%. This system demonstrates the theoretical feasibility and scalability of utilizing natural rock resources in mountainous terrains for long-duration energy storage, providing a novel solution for long-duration power smoothing in renewable energy systems. Full article

With the rapid advancement of artificial intelligence and wearable technologies, the demand for soft, multifunctional electronic materials has grown substantially. Hydrogels have emerged as a promising platform due to their intrinsic softness, stretchability, and biocompatibility. Among them, cellulose-based conductive hydrogels uniquely integrate the sustainability of natural polymers with tunable electrical functionality, offering significant potential for flexible and biointegrated electronics. This review provides a comprehensive and critical perspective on the recent progress in cellulose-based conductive hydrogels. We systematically summarize key design strategies, including physical and chemical crosslinking and interpenetrating network engineering. More importantly, we present a comparative analysis of distinct conductive mechanisms, including ionic conduction, conductive polymers, metallic nanostructures, and carbon-based fillers, highlighting the inherent trade-offs among electrical conductivity, mechanical robustness, and environmental stability. Emerging applications in flexible electronics, energy storage, bioelectronics, and self-powered systems are discussed through structure–property relationships. Finally, we outline current challenges and future directions, emphasizing multifunctional integration, scalable fabrication, and long-term operational stability, thereby providing a framework for the rational design of next-generation sustainable electronic materials. Full article

Deep eutectic solvents (DESs) have emerged as powerful media for enhancing the solubility of poorly water-soluble active pharmaceutical ingredients (APIs). However, their rational design remains challenging due to the complex interplay of intermolecular interactions and non-ideal thermodynamic behavior. This study develops a comprehensive, data-driven taxonomy of solute–solvent systems by integrating COSMO-RS-derived descriptors with principal component analysis (PCA) and unsupervised clustering. This approach establishes a constrained, evidence-based decision framework, which is more appropriate for complex physicochemical systems like DESs than traditional empirical rules. The analysis successfully reduces the multidimensional descriptor space to five physically interpretable axes: solvation driving force, API thermodynamic stability, solvent interaction profile, hydrogen-bond network strength, and hydration effects. Two primary solubilization mechanisms are identified: interaction-driven solvation, characterized by high API–DES affinity, and destabilization-driven solvation. Furthermore, comparison of dry and water-containing systems reveals that water acts as a thermodynamic structuring agent, fundamentally reducing system dimensionality and promoting the emergence of more distinct solvation regimes. Validated through the projection of benzocaine and lidocaine, this framework enables a transition from trial-and-error screening to mechanism-guided formulation design, providing a robust roadmap for navigating the complex solubility landscape of pharmaceutical DESs. Full article

In power engineering, various mathematical models are used, for example, to study stability, forecasting, etc., obtained using analytical methods, machine learning, and artificial intelligence. The present authors pursue a novel direction in modeling the development of the power system as an intelligent control system using data from 1990–2024 under conditions including a growing level of renewable energy production and an increased level of electrical energy saving. As a result of the modeling carried out in the MATLAB and Simulink environment, two types of highly accurate development models were obtained: a regression machine learning ARX model and a multilayer perceptron (MLP) neural network. For the neural model, MAPE errors ranged from 0.73% to 3.37%, and the coefficient of determination R² ranged from 0.9478 to 0.9868. The accuracy of the ARX models was close to 100%. Using an ARX model converted into a state-space (SS) model, it was observed that the subsystems of conventional electricity production and renewable energy were observable and controllable. The presented methodology is modern, enabling the study of large development systems using development models in terms of control and systems theory and artificial intelligence methods. Full article

The growing use of electrochemical batteries in renewable energy systems has intensified the need for storage architectures that can sustain power delivery while limiting transient electrical stress and voltage instability challenges. This study addresses the research gap in experimentally establishing a physically interpretable framework that links battery-centered hybrid storage behavior at the DC bus to AC-side inverter performance under load and source disturbances. A laboratory-scale renewable microgrid integrating photovoltaic and wind generation, programmable load variation, inverter-based AC delivery, and hybrid battery–supercapacitor storage is experimentally implemented and evaluated against a battery-only baseline, supported by a unified analytical framework that quantifies how transient buffering improvements propagate through the power conversion chain. The results show that the hybrid configuration reduces DC-bus voltage droop from about 1.1 V to 0.6 V under heavy-load transitions, and from approximately 0.85 V to 0.44 V during source-side variability (e.g., photovoltaic and wind turbine variations). The hybrid system also improves AC-side behavior, yielding unified stabilization indices of 103.03% for the root-mean-square voltage and 79.51% for the peak-to-peak voltage. These findings demonstrate that the experimentally implemented lab-scale renewable microgrid with hybrid battery–supercapacitor storage provides an effective pathway for improving battery-supported microgrid stability, waveform quality, and transient resilience. Full article

Biosensors have evolved significantly since their invention in the mid-twentieth century. From a simple electrochemical device to the current inclusion of AI, these sophisticated tools are capable of label-free, real-time multiplex detection. To make these sensing systems even more powerful, the incorporation of DNA origami has allowed this technology to become extremely precise, recognisable, and programmable to a range of molecules. This paper systematically summarises the incorporation of DNA origami with biosensors such as fluorescence, surface-enhanced Raman spectroscopy (SERS), surface plasmon resonance (SPR), and electrochemical sensors as well as approaches that are used to design DNA origami nanostructures. These tools allow a range of targets to be detected, ranging from small molecules to larger biological species. Collectively, these studies demonstrate that DNA origami-based biosensors provide high sensitivity; precise spatial control; and rapid, modular detection capabilities. Furthermore, their versatility enables applications across a diverse range of sectors. However, key challenges including limited reproducibility, structural instability, photobleaching, and non-specific binding continue to hinder their widespread adoption. This review proposes future directions aimed at overcoming key limitations, including enhancing biocompatibility and structural stability, to support the development of more advanced and clinical point-of-care-applicable biosensors. Full article

In petroleum drilling, conventional automatic drilling systems still rely heavily on manual intervention, which often leads to poor stability, limited multivariable coordination, and large fluctuations in drilling pressure. To address this problem, this study develops a hydraulic drawworks-based autodriller system with integrated power, drive, actuation, and control units, and establishes a mechanical-hydraulic-control co-simulation model for the coordinated regulation of drill-string hoisting speed and surface weight-on-bit (SWOB). Based on this platform, a dual-loop control framework is developed in which the inner loop uses linear active disturbance rejection control (LADRC) for rapid disturbance estimation and compensation, while the outer loop uses PID control for tracking regulation. Feedforward compensation is introduced to handle predictable load variation, and PSO-assisted fuzzy tuning is used to improve adaptability under varying operating conditions. Simulation results show that, compared with conventional cascaded PID control, the proposed controller reduces drawworks speed and SWOB overshoot by 12.5% and 40%, respectively, while the corresponding settling times are shortened by 1.805 s and 2.443 s. Prototype experiments on a scaled test platform further show that the proposed controller can be implemented on physical hardware and can maintain stable real-time regulation under laboratory conditions. These results support the feasibility of the proposed framework for coordinated hydraulic drawworks control under the simulated and laboratory-scale conditions considered in this study. Full article

Industrial isolated power systems are highly sensitive to load disturbances due to their limited inertia and absence of large-grid support. This article analyzes the dynamic stability of an isolated system with a current available generation contribution of approximately 24 MW, evaluating the integration of a new production plant planned to be integrated in two construction phases of 2 MW each (total 4 MW). The system operates with local generation at 13.8 kV and distribution at 34.5 kV; therefore, demand expansion requires a detailed assessment to maintain safe operating conditions. In addition, the study verifies compliance with spinning reserve requirements for Phase 1 and Phase 2 in accordance with applicable industrial power system criteria, including IEEE 3007.1 and IEEE C37.106, as part of the N−1 security assessment. The developed stability analysis is based on time-domain dynamic simulations using IEEE AC8C excitation models and a UG-8 governor. The results show that, under severe contingencies, the frequency nadir can reach deviations close to 1.5 Hz and RoCoF values above 4 Hz/s. The results indicate that Phase 1 (2 MW) can be incorporated while maintaining acceptable spinning reserve margins, whereas the additional 2 MW corresponding to Phase 2 cannot be integrated under the current operating conditions without violating reserve criteria. However, the system remains stable when generators operate under automatic voltage control, while fixed power factor mode produces less robust responses. Based on this result, the dynamic analysis is focused on the Phase 1 condition under critical contingencies, particularly the sudden outage of the 5 MW and 8 MW generating units, with special emphasis on the outage of the largest generator, mitigated through spinning reserve support and a RoCoF-based load shedding scheme of approximately 4.4 MW. Likewise, the energization of the new plant through the 8 km line requires the evaluation of the available reactive compensation resources, including the use of capacitor banks/reactive support, to prevent underexcitation and maintain acceptable voltage conditions. Full article

The integration of distributed energy resources (DERs), such as solar photovoltaics, wind turbines, and energy storage systems, into distribution networks necessitates accurate estimation of hosting capacity (HC). This paper presents an optimization-based approach for HC assessment and enhancement, which considers both overvoltage and line overload constraints and incorporates the reactive power (VAR) capabilities of DER inverters. Furthermore, the methodology is extended to include network reconfiguration, leveraging switchable branches to alleviate network congestion and further enhance DER integration. The proposed method utilizes a linearized power flow model to ensure computational efficiency and formulates the problem as a convex optimization task when considering only inverter VAR capabilities. The framework jointly addresses overvoltage, line overload, and inverter VAR capability constraints through linear and second-order cone constraints. In the extended formulation that includes network reconfiguration, binary decision variables are introduced to model switch statuses, resulting in a mixed-integer optimization problem. Simulation results based on the IEEE 33-bus system demonstrate that reactive power optimization can effectively redistribute HC across nodes, improving power quality in congested networks. Additionally, the incorporation of network reconfiguration provides further HC enhancement, particularly in scenarios where fixed network topology severely limits DER integration. Simulation studies are further extended to the UKGDS 95-bus system, which is derived from a real UK distribution network and incorporates a 33/11 kV on-load tap changer (OLTC) transformer, thereby providing a more practically representative validation platform. The results demonstrate that the proposed framework is effective across networks of different scales and complexities. The proposed approach offers a flexible and efficient tool for modern distribution network planning, supporting high-penetration DER integration while maintaining grid stability and operational reliability. Full article

Despite the many advantages of renewable energy sources, the stochastic nature of their generation creates a mismatch between electricity production and demand timing. Without appropriate storage solutions, surplus energy remains unused. Although battery energy storage systems are increasingly applied to improve the flexibility and reliability of power systems, there is still a research gap in forecasting the optimal power and storage capacity of solar power plant–battery energy storage system energy complexes operating in parallel with the grid under short-term forecasting conditions, particularly when economic aspects such as partial leasing of storage capacity are considered. Therefore, the development of energy complexes based on solar power plants with the integration of battery energy storage systems, as well as the development of corresponding computational models, becomes critical for ensuring the stability, flexibility, reliability, and efficiency of power systems. Battery energy storage systems are widely used due to their availability, high response speed, significant energy density, and sufficient power capacity; however, their cost remains relatively high. This paper proposes a methodology and a calculation model for determining the optimal forecasted capacity and the rational storage requirements of an energy complex consisting of a solar power plant and a battery energy storage system operating in parallel with the grid at constant power under short-term forecasting conditions (day-ahead or longer). The proposed approach makes it possible to minimise the costs of energy companies associated with the short-term lease of part of a battery energy storage system when they do not own one, or, if such a system is available, to lease out its unused capacity and obtain corresponding profits. The validation of the computational model uses a dataset of hourly daily power outputs of solar power plants in the Integrated Power System of Ukraine for 2018. Statistical analysis of the obtained results shows that the probability of occurrence of maximum deviations for the optimal capacity of the energy complex (5.4%), as well as for the power and capacity of the battery energy storage system (13% and 18%, respectively), does not exceed 0.05 during the year. The results confirm that the proposed methodology provides a reliable basis for determining optimal parameters of solar power plant–battery energy storage system energy complexes and enables economically efficient use of storage capacity through short-term leasing mechanisms. Although the proposed methodology is applied using solar power plant generation data for the national power system as a whole, it can also be used for individual solar power plants located in different regions and countries with different climatic conditions. Certainly, the calculated coefficients differ, but the methodology itself and the sequence of its application remain the same. Full article

This study presents a systematic bibliometric analysis and critical review of assessment indicators and multi-criteria decision-making methods for rural energy systems from 2010 to 2025. It examines the evolving definitions and regional variations in these indicators and methods. The research hotspots of rural energy systems have shifted from basic rural electrification to multi-dimensional assessment indicators and hybrid multi-criteria decision-making methods. The assessment indicators for rural energy systems demonstrate a marked imbalance, dominated by economic and technical dimensions. Specifically, economic evaluations for rural energy systems frequently utilize net present cost and levelized energy cost, shifting from static capital comparisons to comprehensive lifecycle assessments. Meanwhile, loss of power supply probability is identified as the primary inherent constraint among technical assessment indicators for rural energy systems. Geographically, assessment indicators for rural energy systems priorities exhibit significant divergence. Developing regions prioritize basic power supply and affordability, whereas developed regions focus on grid stability and market risk resilience. In addition, environmental evaluations for rural energy systems remain fixated on carbon emissions. Developed nations emphasize global climate benefits, while developing nations focus on localized dividends like indoor air quality improvement. Critically, despite an increasing focus on rural livelihoods, social indicators remain systematically marginalized in rural energy systems, leading to the neglect of local requirements and increasing technical risks. The field of rural energy system assessment is advancing toward multi-criteria decision-making indicators. Future methodologies must integrate robust, dynamic adaptive mechanisms that respond to evolving developmental priorities in order to effectively address inherent data scarcity and complex socio-economic uncertainties of rural energy systems. Full article

Hydropower stations, as critical infrastructure for basic energy supply, play a pivotal role in ensuring the reliability of power systems through their safe and stable operation. Grounding grids operating long-term in complex soil environments are prone to corrosion and degradation, disrupting current distribution balance and causing spatial asymmetry in the voltage field, thereby compromising system safety. Corrosion branch resistance increment identification based on the electrical network method is typically modeled as a parameter inversion optimization problem. However, this problem exhibits underdetermination and other characteristics, making it difficult for traditional analytical methods to obtain stable solutions. To address this, this paper proposes a quantum perturbation scheduling candidate pool-guided sine–cosine algorithm (QSPSCA). Building upon the classical sine–cosine algorithm framework, it incorporates a dynamic candidate pool with multi-source attractor points and a quantum-inspired long-tail scheduling local refinement operator. This achieves an enhanced and smooth transition between global exploration and local refinement. Comparative experiments based on the CEC2017 benchmark and a hydropower station grounding grid corrosion diagnosis case demonstrate that QSPSCA outperforms multiple comparison algorithms in terms of average optimality and result stability. Furthermore, QSPSCA is applied to three typical engineering-constrained optimization problems. Results demonstrate that, whilst satisfying engineering constraints, this method consistently yields higher-quality feasible solutions with superior convergence accuracy and stability compared to alternative algorithms. Therefore, QSPSCA is not only applicable to underdetermined inversion diagnostics but also provides a solution framework with broad applicability for complex engineering optimization problems under structural symmetry perturbations. Full article