As a highly efficient thermochemical conversion method, hydrothermal treatment technology demonstrates broad application potential for the harmless disposal and value-added utilization of food waste and other organic biomass residues. In this paper, subcritical hydrothermal treatment was systematically investigated, and a polit-scale hydrothermal treatment system for food waste was developed with a processing capacity of 10 t/d and a total capital investment of 3.279 4 million yuan. The bio-oil yield obtained from food waste hydrothermal treatment system reached 9%, with a higher heating value of 38.63 MJ/kg, and low-boiling-point fractions (<343 ℃) accounted for 82.62% of the bio-oil, indicating its strong potential as an alternative fuel. The aqueous-phase yield was 85%, and the aqueous products could be formulated into humic acid-based liquid organic fertilizer. Economic evaluation showed that a net profit of 1 302.93 yuan could be achieved per ton of food waste treated, with an investment payback period of 0.84 years. The developed industrial hydrothermal treatment system successfully overcame several core technologies, including the efficient separation of inorganic components from organic solid waste and the cascade recovery and utilization of residual heat. Moreover, the system provides an integrated solution to key technological bottlenecks that have hindered the industrialization of food waste hydrothermal treatment. Furthermore, it systematically resolved the technical bottlenecks facing the industrialization of food waste hydrothermal treatment technology.

In order to investigate the product distribution and synergistic effect during co-liquefaction of plastic and biomass. Six types of lignocellulosic biomass and polypropylene (PP) were selected for co-liquefaction. Based on the characterization of raw materials, the liquefaction behavior and product distribution of different biomass with PP were examined. The results indicate that the biomass with higher volatile matter content and lower lignin content generally exhibits a higher conversion rate during solvent thermal liquefaction. Products distribution from co-liquefaction of biomass and PP varied with biomass types. Higher lignin content in biomass was associated with increased co-liquefied oil yield, suggesting a synergistic effect during the co-liquefaction process. Furthermore, the ash content in biomass influenced the extent of synergy in oil yield-higher ash content enhanced the synergistic improvement in oil production. Among the tested biomass samples, poplar showed the strongest synergy with PP in co-liquefaction oil yield. Individual liquefaction of PP and poplar gave conversion rates of 17% and 36%, respectively, whereas co-liquefaction reached a conversion rate of 51% with an oil yield of 45%, indicating that poplar promotes the hydrothermal degradation of PP. The co-solvent thermal liquefaction of biomass and PP demonstrates advantages in enhancing the formation of target oil products and improving oil quality.

This study establishes a model of a solid oxide fuel cell (SOFC) power generation system fueled by hydrogen-enriched natural gas using Cycle-Tempo software. It provides a detailed analysis of the impacts of key operating conditions-such as single-cell voltage and operating temperature-and critical parameters-including hydrogen blending ratio and anode fuel utilization-on the performance of the SOFC system. The results indicate that increasing the hydrogen blending ratio, inlet temperature, single-cell voltage, and anode fuel utilization can enhance power generation. Notably, a higher hydrogen blending ratio significantly reduces greenhouse gas emissions, particularly CO₂. Under the conditions of a gas flow rate of 0.043 7 kg/s, single-cell voltage of 0.75 V, fuel utilization rate of 80%, fuel cell inlet temperature of 700 ℃, and hydrogen blending ratio of 60%, the system achieves a power output of 1 220.33 kW with an efficiency of 52.67%, while reducing carbon emissions by 27.86%.

To address the limitations of traditional photovoltaic (PV) array modeling methods in terms of computational accuracy and portability, this study proposes a novel PV array modeling approach based on the Implicit Double Diode Model (IDDM) and the Reverse Bias Model (RBM). The proposed method first configures environmental parameters according to the actual operating conditions of the PV array. Subsequently, it calculates the current and voltage characteristics of PV cells under various operating states based on the theoretical frameworks of IDDM and RBM. Following this, the method determines the current and voltage characteristics of PV array substrings and finally outputs the current-voltage (I-V) characteristics of the PV array. Experimental results demonstrate that the proposed method achieves high simulation accuracy, with a theoretical-to-actual current sequence error of only 0.015 A. Additionally, the model effectively simulates the I-V characteristics of PV arrays under scenarios such as partial shading, short circuits, and degradation of equivalent series and parallel resistances. This modeling approach provides a robust theoretical foundation and data support for PV power plant fault diagnosis and analysis.

In order to investigate the influence of structural parameters on the flow characteristics in the ejector and to evaluate the performance of the ejector heat pump system under different working conditions, this study constructed a three-dimensional model of the ejector and a mathematical model of the ejector heat pump system, investigated the influence of geometrical parameters, such as the ejector throat diameter (D_t) and the throat-orifice position (Nozzle Exit Position, NXP), on the performance of the ejector and analyzed the influence of the different working conditions (The effects of different operating conditions (evaporating temperature T_e, condensing temperature T_c, and generating temperature T_g) on the system performance and injector characteristics were analyzed. It is found that increasing D_t can significantly improve the pressure rise ratio of the injector, but it will lead to the reduction of the elicitation coefficient, and the pressure rise ratio decreases with the increase of NXP, and the elicitation coefficient reaches the peak value at NXP=7 mm. When T_c is 40 ℃ and T_g is 70 ℃, the system cooling coefficient (Coefficient of Performance (COP)) increases from 0.94 to 2.64 as T_e increases from 0 ℃ to 15 ℃, the pressure rise ratio of the injector decreases from 1.61 to 1.31, and the priming coefficient increases from 0.51 to 0.89. Among them, the optimization of D_t and NXP can significantly improve the pressure-to-lift ratio and the elicitation efficiency, while the reasonable matching of T_e, T_g and T_c plays a decisive role in the system performance.

The intermittence and volatility of photovoltaic power generation threaten the stability of the power grid, and high-precision prediction can provide key support for power system scheduling. Solar irradiance prediction methods based on meteorological data and sky images have been widely studied, but the mining of multi-scale temporal features and sky image features is still very limited, and the correlation between features is not fully utilized. Therefore, this paper proposes an ultra-short-term solar irradiance prediction model based on multi-source data and Transformer framework. First, the long-term and short-term memory neural network-Transformer is used to learn the long-term and short-term temporal variation characteristics of multivariate meteorological sequences. Secondly, the convolution visual Transformer is used to model the global information and local structure of the sky image, and the deep image features are extracted. Finally, a cross-modal feature fusion module based on attention mechanism is proposed, which uses image features to represent temporal features, realizes cross-modal feature fusion, and outputs prediction results by decoder. Experimental results show that the proposed method has better prediction performance than the state-of-the-art methods. Compared with the case of only using meteorological data, the NRMSE of the model is reduced by 7.4% on average. The model significantly improves the prediction accuracy by mining the features of multi-source data (meteorological time series and sky images) and using the cross-modal attention mechanism to effectively mine their correlation.

In response to the weak generalization of cross-domain fault diagnosis for rolling bearings in wind turbine units, a Wind Turbine Bearing Fault Diagnosis Method via Combined Features Adaptation is proposed. Firstly, a data selection method based on one-dimensional convolutional neural networks is introduced to filter data from the source domain that is similar to the target domain, forming a new source domain to reconstruct the training dataset and mitigate the negative transfer effect. Secondly, the continuous wavelet transform is applied to obtain two-dimension time-frequency features of the training data, these features are afforded for the residual neural network, which generates one-dimensional feature vectors as the initial features for transfer learning, ensuring comprehensive extraction of the training data's features. Thirdly, a new combined feature adaptation loss function is presented. In it, not only the feature transfer loss of the source domain and the target domain, but also the classification loss of the source domain is considered. This enables effective feature transfer from the source domain to the target domain. Finally, the softmax classifier of the constructed residual neural network is used for the diagnosis classification of the fault. By the simulation under different kinds of migration tasks between source domain and target domain, the proposed model is of higher diagnosis performance in the cross-domain environment.

For large floating wind turbines, the influence of blade deformation on wind turbine performance cannot be ignored. Based on the free wake model of lift line and the geometrically accurate beam model, the effects of aerodynamic coupling effects on the load characteristics of EI-15 MW wind turbine under rolling conditions are studied in this paper. The results show that the additional velocity brought by the rolling motion of the floating platform will change the inflow characteristics at the section of the airfoil, so that the wind turbine load will fluctuate, and the wind turbine load will decrease after considering the flexible deformation of the blade. Compared with the fixed condition, the asymmetry of the load distribution on the rotor plane is more obvious in the rolling condition, which leads to the fluctuation amplitude of the yaw and pitching moment of the rotor. In addition, the shape variable of the blade will fluctuate periodically under rolling conditions, and the shape variable gradually increases from blade root to blade tip. The effective Angle of attack decreases due to torsional deformation, and the Angle of attack of the flexible blade at the same profile section is smaller than that of the rigid blade, which is the direct reason why the load of the flexible blade is lower than that of the rigid blade.

Windmills are important facilities that convert wind energy into electrical energy. This paper addresses the issues of missed detections and false detections in the process of identifying small-scale windmills in remote sensing images by constructing a new GF-2 windmill dataset. It proposes the YOLO-WM algorithm for the small target detection task of windmills, adding a tiny object detection layer based on the YOLOv8 object detection algorithm, and incorporates a BSAM dynamic attention mechanism module in the backbone section. It replaces the C2f module with the lightweight C2f_RepGhost module and finally utilizes the SAHI slicing algorithm in the prediction stage to complete the windmill target detection task. Experimental results show that the YOLO-WM algorithm can better retain shallow convolutional features while reducing algorithm complexity, allowing for more accurate capture of small-scale target information. This method compensates for the shortcomings of small object detection datasets, and it has been verified that the improved algorithm performs better in detecting small-scale windmills than other algorithms. This research provides valuable insights for the rational optimization and allocation of wind energy resources and the joint analysis of geographical elements.

Due to the current-magnitude limit, controlled phase angle, and absence of zero-sequence current of converters, fault-location errors occur when two-phase short-circuit faults happen in offshore wind-farm collector cables. This paper analyzes the composite-sequence network under such faults and exploits the capability of both the VSC-HVDC link and the wind-turbine converters to suppress negative-sequence current during the fault. By using the locally measured negative-sequence voltage, the positive-sequence voltage at the fault point is computed on-line, a fault-distance equation is established, and the fault position is solved in real time. The proposed on-line algorithm remains accurate for large and non-resistive fault resistances and is immune to the reactive-power support strategy of the offshore wind farm, thus enhancing the safe and stable operation of offshore wind power systems.

Diode rectifier unit (DRU) based offshore wind power delivery scheme can reduce the volume and weight of offshore platforms and thus save a lot of operation and maintenance costs. However, the DRU does not have commutation controllability, and its flexibility is poor when power fluctuations occur in the wind farm. This paper first establishes a topological model of a wind farm composed of three-phase permanent magnet synchronous generators connected in parallel with a DRU and an auxiliary voltage-source converter (VSC) for power transmission.) Secondly, the grid-froming control strategy of the grid side converter and the grid-following control strategy of VSC are adopted respectively. Finally, a RT-LAB semi-physical simulation system is built based on Matab/simulink. The results show that the proposed control strategy realizes the auxiliary VSC control of reactive power and DRU output power of wind turbines.

With the large-scale access of wind turbines with frequency regulation functions to the power system, the influence of frequency regulation control on the transient power angle stability of the system is increasingly prominent. In this paper, an extended two-machine system with a wind turbine connected to the sending end area and a three-phase short-circuit fault at the intermediate node of the tie line is taken as the research background. Aiming at the influence mechanism of the droop control of the wind turbine on the stability of the first swing of the power angle of the system, firstly, based on the equal area rule, the relationship between the speed difference of the synchronous machine and the transient energy of the system is established, and the WDPAS index, including the influence factors such as the output of the wind turbine, the grid-connected position, and the inertia distribution of the system, is proposed to analyze the influence of the droop control of the wind turbine on the speed difference of the synchronous machine. Then, based on the system state of the maximum power point tracking (MPPT) control of the wind turbine, the dynamic changes of the system transient energy during the fault and after the fault clearance are studied. Finally, the first swing stability of the system power angle is evaluated by the maximum offset of the system power angle first pendulum, and an extended two-machine system simulation model is built in the DIgSILENT/PowerFactory simulation software to verify the rationality of the index and the correctness of the conclusion.

More and more renewable energy is incorporated into the power system through power electronic equipment, resulting in reduced system strength and physical inertia. This paper proposes a grid-forming control strategy for modular multilevel converter based high voltage direct current transmission system (MMC-HVDC) with MMC internal energy control which is connected with a weak AC system. Firstly, based on the droop grid-forming control,, inertia link and PI control are added, so that MMC can automatically track the load fluctuation and adjust its own power output, and realize zero steady-state error regulation of AC voltage and frequency. Then, aiming at the problem of internal energy fluctuation of MMC controlled by grid-forming control, a MMC internal energy compensation controller is designed, which can effectively reduce fluctuation of DC voltage of submodule capacitance. Finally, based on the PSCAD/EMTDC electromagnetic transient simulation platform, a MMC-HVDC transmission system was established, and two operating conditions of AC grid load increase and AC grid load reduction are selected to verify the feasibility and effectiveness of the proposed control strategy.

To address the power fluctuations in the distribution network caused by the integration of distributed new energy, this article proposes an economic dispatch strategy for the distribution network that considers the frequency regulation capability and source load correlation of electric vehicle battery swapping stations (BSS). This strategy aims to minimize the total system cost, coordinate and schedule frequency modulation resources including BSS, and constrain BSS charging and discharging behavior while maintaining power balance. Firstly, a frequency modulation signal prediction model is constructed: a long short-term memory neural network is used to predict new energy and load power, and the Copula function is used to characterize their correlation, deriving the conditional probability distribution of the frequency modulation signal. Secondly, a prediction driven economic dispatch method based on source load correlation is proposed: Wasserstein distance is used to process the predicted data and construct a fuzzy set, combined with distribution robust optimization and opportunity constraints to establish an economic dispatch model considering the cost of secondary frequency modulation; Further transform the opportunity constraint through conditional value at risk (CVaR) approximation, and use Wasserstein duality to transform the model into a linear programming problem for solution. The simulation results show that the proposed strategy is economically superior to traditional methods.

Driven by the goals of "Carbon Peak" and "Carbon Neutrality", the rural energy transition is faced with the problems of irrational energy structure, low proportion of renewable energy supply and low utilisation efficiency. For rural areas endowed with renewable energy sources such as biomass and solar energy, a rural integrated energy system (RIES) planning method considering photo-thermal assisted biogas production is proposed based on the effect of temperature on the efficiency of biogas production. Firstly, a RIES architecture is designed based on the coupled use of biomass and solar energy resources, and secondly, an economically optimal integrated rural energy planning optimisation model is constructed, which contains constraints such as energy balance and equipment operation. Finally, the example compares two planning scenarios with and without photothermal-assisted biogas production using a rural area in Shanxi as a scenario, and the results show that the biogas production rate is improved, the pressure on energy storage is alleviated, and the system planning cost and carbon emission are sig-nificantly reduced, which verifies the validity and economy of the proposed planning model.

To address the issue of low wave energy capture efficiency caused by the nonlinear effects in the actual operating conditions of wave energy conversion devices, a model-based improved power optimization strategy is proposed. Considering the resultant force acting on the free state of the float, an equivalent approximation is carried out using series expansion, and an independent iterative algorithm is introduced. Based on the Kalman filter with statistical similarity measurement, the polynomial coefficients are determined, and then the Euler-Lagrange equations are established to obtain the optimal control law. By tracking the control current, the maximum power can be captured. Simulation results show that the improved Kalman filter can reduce the influence of noise and the parameter identification error. The proposed strategy can effectively enhance the output power of the system under actual operating conditions.