Search Results (969)

With the support of the dual-carbon strategy and related policies, China’s offshore wind power has experienced rapid development. However, constrained by the inherent intermittency and volatility of wind power, large-scale expansion poses significant challenges to grid integration and exacerbates government fiscal burdens. To address these critical issues, this paper proposes a multi-market coupling trading model integrating energy storage-equipped offshore wind power into electricity–carbon–green certificate markets for large-scale grid networks. Firstly, a day-ahead electricity market optimization model that incorporates energy storage is established to maximize power revenue by coordinating offshore wind power generation, thermal power dispatch, and energy storage charging/discharging strategies. Subsequently, carbon market and green certificate market optimization models are developed to quantify Chinese Certified Emission Reduction (CCER) volume, carbon quotas, carbon emissions, market revenues, green certificate quantities, pricing mechanisms, and associated economic benefits. To validate the model’s effectiveness, a gradient ascent-optimized game-theoretic model and a double auction mechanism are introduced as benchmark comparisons. The simulation results demonstrate that the proposed model increases market revenues by 17.13% and 36.18%, respectively, compared to the two benchmark models. It not only improves wind power penetration and comprehensive profitability but also effectively alleviates government subsidy pressures through coordinated carbon–green certificate trading mechanisms. Full article

Developing multifunctional wound dressings with excellent mechanical properties, strong tissue adhesion, and efficient antibacterial activity is crucial for promoting wound healing. This study prepared a novel nanocomposite hydrogel dressing based on sodium alginate-polyacrylic acid dual crosslinking networks, incorporating tannic acid-coated cellulose nanocrystals (TA@CNC) and in-situ reduced silver nanoparticles for multifunctional enhancement. The rigid CNC framework significantly improved mechanical properties (elastic modulus of 146 kPa at 1 wt%), while TA catechol groups provided excellent adhesion (36.4 kPa to pigskin, 122% improvement over pure system) through dynamic hydrogen bonding and coordination interactions. TA served as a green reducing agent for uniform AgNPs loading, with CNC negative charges preventing particle aggregation. Antibacterial studies revealed synergistic effects between TA-induced membrane disruption and Ag⁺-triggered reactive oxygen species generation, achieving >99.5% inhibition against Staphylococcus aureus and Escherichia coli. The TA@CNC-regulated porous structure balanced swelling performance and water vapor transmission, facilitating wound exudate management and moist healing. This composite hydrogel successfully integrates mechanical toughness, tissue adhesion, antibacterial activity, and biocompatibility, providing a novel strategy for advanced wound dressing development. Full article

M-edge X-ray absorption spectroscopy (XAS), which probes 3p→3d transitions in first-row transition metals, provides detailed insights into oxidation states, spin-states, and local electronic structure with high element and orbital specificity. Operating in the extreme ultraviolet (XUV) region, this technique provides sharp multiplet-resolved features with high sensitivity to ligand field and covalency effects. Compared to K- and L-edge XAS, M-edge spectra exhibit significantly narrower full widths at half maximum (typically 0.3–0.5 eV versus >1 eV at the L-edge and >1.5–2 eV at the K-edge), owing to longer 3p core-hole lifetimes. M-edge measurements are also more surface-sensitive due to the lower photon energy range, making them particularly well-suited for probing thin films, interfaces, and surface-bound species. The advent of tabletop high-harmonic generation (HHG) sources has enabled femtosecond time-resolved M-edge measurements, allowing direct observation of ultrafast photoinduced processes such as charge transfer and spin crossover dynamics. This review presents an overview of the fundamental principles, experimental advances, and current theoretical approaches for interpreting M-edge spectra. We further discuss a range of applications in catalysis, materials science, and coordination chemistry, highlighting the technique’s growing impact and potential for future studies. Full article

The rapid electrification of transportation and the proliferation of rooftop solar photovoltaics (PVs) in urban environments are reshaping the operational dynamics of power distribution networks. However, the inherent uncertainty in electric vehicle (EV) behavior—including arrival times, charging preferences, and state-of-charge—as well as spatially and temporally variable solar generation, presents a profound challenge to existing scheduling frameworks. This paper proposes a novel data-driven distributionally robust optimization (DDRO) framework for solar-powered EV charging coordination under spatiotemporal uncertainty. Leveraging empirical datasets of EV usage and solar irradiance from a smart city deployment, the framework constructs Wasserstein ambiguity sets around historical distributions, enabling worst-case-aware decision-making without requiring the assumption of probability laws. The problem is formulated as a two-stage optimization model. The first stage determines day-ahead charging schedules, solar utilization levels, and grid allocations across an urban-scale distribution feeder. The second stage models real-time recourse actions—such as dynamic curtailment or demand reshaping—after uncertainties are realized. Physical grid constraints are modeled using convexified LinDistFlow equations, while EV behavior is segmented into user classes with individualized uncertainty structures. The model is evaluated on a modified IEEE 123-bus feeder with 52 EV-PV nodes, using 15 min resolution over a 24 h horizon and 12 months of real-world data. Comparative results demonstrate that the proposed DDRO method reduces total operational costs by up to 15%, eliminates voltage violations entirely, and improves EV service satisfaction by more than 30% relative to deterministic and stochastic baselines. This work makes three primary contributions: it introduces a robust, tractable optimization architecture that captures spatiotemporal uncertainty using empirical Wasserstein sets; it integrates behavioral and physical modeling within a unified dispatch framework for urban energy-mobility systems; and it demonstrates the value of robust coordination in simultaneously improving grid resilience, renewable utilization, and EV user satisfaction. The results offer practical insights for city-scale planners seeking to enable the reliable and efficient electrification of mobility infrastructure under uncertainty. Full article

The large-scale integration of wind power, photovoltaic systems, and energy storage systems (ESSs) into power grids has increasingly influenced the transient stability of power systems due to their dynamic response characteristics. Considering the commercial confidentiality of core control parameters from equipment manufacturers, parameter identification has become a crucial approach for analyzing ESS dynamic behaviors during high-voltage ride-through (HVRT) and low-voltage ride-through (LVRT) and for optimizing control strategies. In this study, we present a multidimensional feature-integrated parameter identification framework for ESSs, combining a multi-scenario voltage disturbance testing environment built on a real-time laboratory platform with field-measured data and enhanced optimization algorithms. Focusing on the control characteristics of energy storage converters, a non-intrusive identification method for grid-connected control parameters is proposed based on dynamic trajectory feature extraction and a hybrid optimization algorithm that integrates an improved particle swarm optimization (PSO) algorithm with gradient-based coordination. The results demonstrate that the proposed approach effectively captures the dynamic coupling mechanisms of ESSs under dual-mode operation (charging and discharging) and voltage fluctuations. By relying on measured data for parameter inversion, the method circumvents the limitations posed by commercial confidentiality, providing a novel technical pathway to enhance the fault ride-through (FRT) performance of energy storage systems (ESSs). In addition, the developed simulation verification framework serves as a valuable tool for security analysis in power systems with high renewable energy penetration. Full article

Under the dual-carbon goals, with the rapid increase in the proportion of fluctuating power sources such as wind and solar energy, the regulatory capacity of traditional thermal power generation can no longer meet the demand for intra-day fluctuations. There is an urgent need to tap into the potential of flexible load-side regulatory resources. To this end, this paper proposes a low-carbon economic optimal dispatching strategy for virtual power plants (VPPs), considering the aggregation of diverse flexible and adjustable resources with the integration of wind and solar power. Firstly, the method establishes mathematical models by analyzing the dynamic response characteristics and flexibility regulation boundaries of adjustable resources such as photovoltaic (PV) systems, wind power, energy storage, charging piles, interruptible loads, and air conditioners. Subsequently, considering the aforementioned diverse adjustable resources and aggregating them into a VPP, a low-carbon economic optimal dispatching model for the VPP is constructed with the objective of minimizing the total system operating costs and carbon costs. To address the issue of slow convergence rates in solving high-dimensional state variable optimization problems with the traditional plant growth simulation algorithm, this paper proposes an improved plant growth simulation algorithm through elite selection strategies for growth points and multi-base point parallel optimization strategies. The improved algorithm is then utilized to solve the proposed low-carbon economic optimal dispatching model for the VPP, aggregating diverse adjustable resources. Simulations conducted on an actual VPP platform demonstrate that the proposed method can effectively coordinate diverse load-side adjustable resources and achieve economically low-carbon dispatching, providing theoretical support for the optimal aggregation of diverse flexible resources in new power systems. Full article

Mobile Charging Robots (MCRs), essentially high-voltage batteries mounted on mobile platforms, offer a flexible solution for electric vehicle (EV) charging, particularly in environments like supermarket parking lots with photovoltaic (PV) generation. Unlike fixed charging stations, MCRs must be strategically dispatched and recharged to maximize operational efficiency and revenue. This study investigates a Model Predictive Control (MPC) approach using Mixed-Integer Linear Programming (MILP) to coordinate MCR charging and movement, accounting for the additional complexity that EVs can park at arbitrary locations. The performance impact of EV arrival and demand forecasts is evaluated, comparing perfect foresight with data-driven predictions using long short-term memory (LSTM) networks. A slack variable method is also introduced to ensure timely recharging of the MCRs. Results show that incorporating forecasts significantly improves performance compared to no prediction, with perfect forecasts outperforming LSTM-based ones due to better-timed recharging decisions. The study highlights that inaccurate forecasts—especially in the evening—can lead to suboptimal MCR utilization and reduced profitability. These findings demonstrate that combining MPC with predictive models enhances MCR-based EV charging strategies and underlines the importance of accurate forecasting for future smart charging systems. Full article

This paper presents a high-fidelity multi-physics dynamic model for electric vehicles, serving as a fundamental building block for intelligent vehicle-to-grid (V2G) integration systems. The model accurately captures complex vehicle dynamics of the powertrain, battery, and regenerative braking, enabling precise energy consumption evaluation, including in AI-driven V2G scenarios. Validated using real-world data from a Citroën Ami operating on urban routes in Naples, Italy, it achieved exceptional accuracy with a root mean square error (RMSE) of 1.28% for dynamic state of charge prediction. This robust framework provides an essential foundation for AI-driven digital twin technologies in V2G applications, significantly advancing sustainable transportation and smart grid integration through predictive simulation. Its versatility supports diverse fleet applications, from residential energy management and coordinated charging optimization to commercial car sharing operations, leveraging backup power during peak demand or grid outages, so to maximize distributed battery storage utilization. Full article

The structures of hexaammine solvated indium(III) and thallium(III) ions in liquid ammonia solution are determined by EXAFS. Both complexes have regular octahedral coordination geometry with mean In-N and Tl-N bond distances of 2.23(1) and 2.29(2) Å, respectively. Ammine solvated thallium(III) in liquid ammonia is characterized with ²⁰⁵Tl NMR measurements. Solvents such as liquid ammonia, N,N-dimethylthioformamide (DMTF), trialkyl and triphenyl phosphite and phosphine are strong electron pair donors and thereby able to form bonds with a large covalent contribution with strong electron pair acceptors. A survey of reported structures of ammine, DMTF, trialkyl and triphenyl phosphite and phosphine solvated metal ions in the solid state and solution is presented. The M-N and M-S bond distances in ammine and DMTF solvated metal ions are compared with the M-O bond distance in the corresponding metal ion hydrates, expected to form mainly electrostatic interactions with metal ions. The d¹⁰ metal ions have high ability to form bonds with a high degree of covalency with increasing ability down the group and with decreasing charge of the metal ion. The difference in M-N and M-O bond distances between ammine solvated and hydrated metal ions with the same coordination geometry decreases significantly with the increasing ability of the metal ion to form bonds with a large covalent contribution. This difference correlates well with the covalent bonding index, γ_M²*r. Full article

Prussian blue analogs (PBAs) are widely recognized as promising candidates for aqueous zinc-ion batteries (AZIBs) due to their stable three-dimensional framework structure. However, their further development is limited by their low specific capacity and unsatisfactory cycling performance, primarily caused by phase transformation during charge–discharge cycles. Herein, we employed a high-entropy strategy to introduce five different metal elements (Fe, Co, Ni, Mn, and Cu) into the nitrogen–coordinated M_a sites of PBAs, forming a high-entropy Prussian blue analog (HEPBA). By leveraging the cocktail effect of the high-entropy strategy, the phase transformation in the HEPBA was significantly suppressed. Consequently, the HEPBA as an AZIB cathode delivered a high reversible specific capacity of 132.1 mAh g⁻¹ at 0.1 A g⁻¹, and showed exceptional cycling stability with 84.7% capacity retention after 600 cycles at 0.5 A g⁻¹. This work provides innovative insights into the rational design of advanced cathode materials for AZIBs. Full article

The electrochemical reduction of carbon monoxide (COER) offers a promising route for generating value-added multi-carbon (C₂₊) products, such as ethanol, but achieving high catalytic performance remains a significant challenge. Herein, we performed comprehensive density functional theory (DFT) computations to evaluate CO-to-ethanol conversion on single metal atoms anchored on graphitic carbon nitride (TM/g–CN). We showed that these metal atoms stably coordinate with edge N sites of g–CN to form active catalytic centers. Screening 20 TM/g–CN candidates, we identified V/g–CN and Zn/g–CN as optimal catalysts: both exhibit low free-energy barriers (<0.50 eV) for the key ^*CO hydrogenation steps and facilitate C–C coupling via an Eley–Rideal mechanism with a negligible kinetic barrier (~0.10 eV) to yield ethanol at low limiting potentials, which explains their superior COER performance. An analysis of d-band centers, charge transfer, and bonding–antibonding orbital distributions revealed the origin of their activity. This work provides theoretical insights and useful guidelines for designing high-performance single-atom COER catalysts. Full article

Achieving high quantum yields for Yb³⁺ ion emission in complexes with organic ligands is a challenging task, as most Yb³⁺ complexes with such ligands typically exhibit efficiencies below 3.5%. Our research demonstrates that the introduction of heavy atom-containing ancillary ligands, such as TPPO or TPAO, along with the careful engineering of the main β-diketone ligand, can increase the luminescence efficiency up to 20-fold by the alteration of the energy migration pathway. It is demonstrated that the combination of two distinct organic ligands leads to the blockage of singlet–triplet intersystem crossing (ISC), alongside electronic energy transfer from β-diketone to Yb³⁺ ions through charge transfer states. The synthesized complexes exhibit quantum yields of 6.5% and 7.0% in the solid state, which places them at the top globally among this class of materials with simple non-deuterated and non-fluorinated ligands. Full article

In the context of the “double carbon” goals and energy transformation, the integration of energy and transportation has emerged as a crucial trend in their coordinated development. Wind power prediction serves as the cornerstone technology for ensuring efficient operations within this integrated framework. This paper introduces a wind power prediction methodology based on an NGBoost–GRU fusion model and devises an innovative dynamic charging optimization strategy for electric vehicles (EVs) through deep collaboration. By integrating the dynamic feature extraction capabilities of GRU for time series data with the strengths of NGBoost in modeling nonlinear relationships and quantifying uncertainties, the proposed approach achieves enhanced performance. Specifically, the dual GRU fusion strategy effectively mitigates error accumulation and leverages spatial clustering to boost data homogeneity. These advancements collectively lead to a significant improvement in the prediction accuracy and reliability of wind power generation. Experiments on the dataset of a wind farm in Gansu Province demonstrate that the model achieves excellent performance, with an RMSE of 36.09 kW and an MAE of 29.96 kW at the 12 h prediction horizon. Based on this predictive capability, a “wind-power-charging collaborative optimization framework” is developed. This framework not only significantly enhances the local consumption rate of wind power but also effectively cuts users’ charging costs by approximately 18.7%, achieving a peak-shaving effect on grid load. As a result, it substantially improves the economic efficiency and stability of system operation. Overall, this study offers novel insights and robust support for optimizing the operation of integrated energy systems. Full article

The development of metallic coatings as Ni-Co alloys, with particular emphasis on their homogeneity, processability, and sustainability, is of the utmost significance. To address these challenges, the utilization of phenylsalicylimines (PSIs) as additives within deep eutectic solvents (DES) was investigated, assessing their influence on the electrodeposition process of these metals at an intermediate temperature of 60 °C, while circumventing aqueous reaction conditions. The findings demonstrated that the incorporation of PSIs markedly enhances coating uniformity, resulting in an optimal cobalt content of 37% and an average thickness of 24 µm. Electrochemical evaluations revealed improvements in charge and mass transfer, thereby optimizing process efficiency. Moreover, computational studies confirmed that PSIs form stable complexes with Co (II), modulating the electrochemical characteristics of the system through the introduction of the diethylamino electron-donating group, which significantly stabilizes the coordinated forms with both components of the DES. Additionally, the coatings displayed exceptional corrosion resistance, with a rate of 0.781 µm per year, and achieved an optimal hardness of 38 N HRC, conforming to ASTM B994 standards. This research contributes to the development of electroplating bath designs for metallic coating deposition and lays the groundwork for the advancement of sophisticated technologies in functional coatings that augment corrosion resistance and mechanical properties. Full article

In this study, 3-chlorothiophene-2-carboxylic acid (HL) was used as a main ligand to successfully synthesize four novel complexes: [Cu(L)₂(Py)₂(OH₂)₂] (1), [Co(L)₂(Py)₂(OH₂)₂] (2) (Py = pyridine), [{Ni(L)₂(OH₂)₄}₂{Ni(L)(OH₂)₅}]L•5H₂O (3), and [{Co(L)₂(OH₂)₄}₂{Co(L)(OH₂)₅}]L•5H₂O (4). All four compounds were identified by elemental analysis and ESI mass spectrometry, and subsequently characterized by IR spectroscopy, UV-visible diffuse reflectance spectroscopy, electron paramagnetic resonance spectroscopy, thermogravimetric analysis, single-crystal X-ray crystallography, and cyclic voltammetry. X-ray analyses revealed that complexes 1 and 2 exhibit a centrosymmetric pseudo-octahedral coordination geometry; the copper (II) and cobalt (II) metal ions, respectively, are located at the crystallographic center of inversion. The coordination sphere of the copper (II) complex is axially elongated in accordance with the Jahn–Teller effect. Intriguingly, for charge neutrality, compounds 3 and 4 crystallized as three independent mononuclear octahedrally coordinated metal centers, which are two $\left[\mathrm{M}{\mathrm{L}}_2{\left({\mathrm{O}\mathrm{H}}_2\right)}_4\right]$ complex molecules and one ${\left[\mathrm{M}\mathrm{L}{\left({\mathrm{O}\mathrm{H}}_2\right)}_5\right]}^{+}$ complex cation (M = ${\mathrm{N}\mathrm{i}}^{\mathrm{I}\mathrm{I}}$ and ${\mathrm{C}\mathrm{o}}^{\mathrm{I}\mathrm{I}}$, respectively), with the ligand anion ${\mathrm{L}}^{-}$ serving as the counter ion. The anticancer activities of these complexes were systematically assessed on human leukemia K562 cells, lung cancer A549 cells, liver cancer HepG2 cells, breast cancer MDA-MB-231 cells, and colon cancer SW480 cells. Among them, complex 4 shows significant inhibitory effects on leukemia K562 cells and colon cancer SW480 cells. Full article