This paper takes Spirodela polyrhiza as the experimental object to study the effects of the dilution multiple of aquaculture wastewater and the initial inoculation amount on the growth and crude protein accumulation of Spirodela polyrhiza, as well as the absorption and purification patterns of nitrogen and phosphorus nutrients in aquaculture wastewater by Spirodela polyrhiza. The results show that with the increase of the dilution multiple of aquaculture wastewater, the removal rates of NH4+N, NO3N, and PO43P by Spirodela polyrhiza gradually increase, and the absorption and purification of NH4+N by Spirodela polyrhiza is earlier than that of NO3N. With the increase of the initial inoculation amount of Spirodela polyrhiza, the removal rates of PO43P and NO3N both increase. When the initial inoculation amounts of Spirodela polyrhiza are 50% and 75%, the removal rates of NH4+N reach 97.6% and 94.1%, respectively. The crude protein content of Spirodela polyrhiza decreases with the increase of the dilution multiple of aquaculture wastewater. The highest growth amount and growth rate of Spirodela polyrhiza is achieved in the aquaculture wastewater diluted 20 times. Under the conditions of 20 times dilution of aquaculture wastewater and an initial inoculation amount of 75% of Spirodela polyrhiza, the annual protein yield of Spirodela polyrhiza can reach 0.91 t/hm².

To evaluate the importance of the startup process of the compressed air energy system (CAES), a mathematical model of the entire system was established based on a 300 MW CAES power plant. A series of analyses were conducted to evaluate the changes in the main parameters during the startup and the corresponding dynamic responses were obtained. These analyses include the antisurge operation of the compressors, 1drive4 variable frequency startup of the compressor train, the programmed startup of the turbine train, and the power regulation process under the airdistribution scheme. The results indicate that the optimization of startup process can shorten the start time of the compressor and turbine trains and improve the system efficiency. In addition, the qualitative data during many important operation process was determined. The study provides valuable date and theoretical basis for the safe and efficient operation of the CAES unit.

This study constructs a full lifecycle model for BMF, spanning from cradle to grave, and assesses the carbon footprint throughout its life cycle. The research findings reveal significant variations in carbon emissions at different stages, notably during the processing phase, making it a key emission stage. The carbon footprint is influenced by various factors such as raw material types, composition ratios, processing technology differences, and transportation mode choices. In the discussion, the study emphasizes the importance of carbon footprint assessment in policy and market contexts, and analyzes potential strategies for reducing carbon emissions. This research provides crucial insights for decisionmakers, the energy industry, policymakers, and researchers, contributing to a better understanding of the carbon footprint of biomass molding fuel products and promoting sustainable energy production and utilization.

In response to the issues of limited carbon reduction methods on the load side and poor coordination of carbon reduction methods across generation, load, and storage in current lowcarbon dispatching of power systems, a multidimensional carbon reduction coupling strategy based on carbon potential indicators is proposed. This involves the establishment of a duallayer optimization dispatch model for the power system, which includes lowcarbon economic objectives. Initially, a carbon flow tracing model for loads and energy storage is developed based on the theory of carbon emissions flow in power systems. Subsequently, a dual lowcarbon demand response model integrating carbon flow theory is established on the load side, and a lowcarbon dispatch model based on nodal carbon potential is developed for the energy storage side. Then, a duallayer optimization dispatch model for the power system characterized by time ofuse electricity pricing and nodal carbon potential is constructed, with the upper and lower layers aimed at optimal economic and lowcarbon objectives, respectively. Finally, the strategy is tested using a modified IEEE14node system, and the simulation results demonstrate that this dispatch strategy can effectively tap into the system's carbon reduction potential, enhance its carbon reduction capability, and improve its economic benefits.

With the continuous development of userside distributed energy resources, interactions among multiagent resources have gradually emerged. Due to autonomous regulation of distributed energy equipment and diversification of operational methods among renewable energy and load entities, it is imperative to establish multiagent gametheoretic optimization models to satisfy diverse interests. This paper focuses on multiparklevel integrated energy systems and constructs a twolayer gameoptimized scheduling model. First, a ladder carbongreen certificate trading model incorporating an equivalent offset mechanism is proposed, considering carbon emissions generated by parks during production and operational activities. Second, based on actual cooperative scenarios among parks, a multipark gametheoretic optimization model is developed to study dynamic pricing strategies of integrated system operators and the optimal operational scheduling of parks. Finally, case studies demonstrate that the proposed model achieves economic efficiency while reducing system carbon emissions, unifying economic and carbon reduction benefits.

Aiming at the problem that there are a large number of horizontal or vertical distribution outliers in the wind speedpower data collected by SCADA system when wind turbine is in abnormal operation, an abnormal data processing method based on median absolute deviation method (MADM) and quartile method (QM) is proposed to solve it, namely MADM –QM algorithm. Firstly, based on the relationship model of wind speedpitch angle, the wind speedpitch angle data outside of ±4.5 MAD are discarded by solving the median absolute deviation (MAD) in the wind speedpitch angle data set of the wind speed interval. Secondly, based on the wind speedpower relationship model, the abnormal values in the wind speedpower data set of the power interval are eliminated, and then the abnormal values in the wind speedpower data set of the wind speed interval are eliminated to complete the abnormal data processing. Finally, the actual operation data of wind turbine under complex working conditions of a wind farm are taken as examples for verification, and comparison with MADM, QM and densitybased spatial clustering (DBSCAN) method. The results indicate that the proposed method can not only effectively identify abnormal data but also efficiently and stably clean them. Compared with the other three methods, to a certain extent, it proves that MADMQM can achieve good efficiency of abnormal data processing and optimal cleaning quality on the abnormal data.

The uneven illumination intensity causes the output curve of the photovoltaic array to be a multimodal curve, and the traditional maximum power point tracking (MPPT) control algorithm cannot track the global maximum power. Based on this, a MPPT control method for photovoltaic power generation systems is proposed, which is based on the improved sparrow search algorithm (ISSA) and disturbance and observation method (P&O). Firstly, in the early stage of tracking, chaotic mapping is used to increase the diversity of ISSA population and enhance the algorithm's wide search ability. To prevent the algorithm from getting stuck in local optima, the firefly perturbation algorithm is used to perturb and update individual sparrows. Secondly, in the later stage of tracking, P&O is used to prevent the system from oscillating near the maximum power point, ensuring stable output at the maximum power point. Finally, through numerical analysis, the proposed MPPT control method achieves fast tracking and accurate output in different scenarios, and can be well applied in photovoltaic hybrid power generation systems.

In recent years, spectral crossover photovoltaic/thermal (CPV/T)composite technology has attracted much attention by decoupling the crossover from the heat of the PV cell and avoiding problems such as ultratemperature of the PV cell and restricted taste of the system output thermal energy. However, the research in this field mainly focuses on simulation calculations and lacks experimental studies on thermal and electrical performance under actual meteorological and lighting conditions. In order to investigate the real operating performance of outdoor CPV/T systems, this paper builds a lowfrequency concentrated light crossover CPV/T system and a nonconcentrated light PV system, and compares and analyses the thermal and electrical output characteristics under concentrated light and nonconcentrated light conditions. The effects of the optical properties of the frequencysharing liquid on the thermal and electrical performance of the concentratingfrequencyshared CPV/T system are further investigated. The results show that the frequency divided CPV/T system has a higher electrical output power compared to the nonconcentrated PV system, with an electrical output power of 79.7 W and 72.9 W when using deionised water frequencydividing and silver/water nanofluid frequencydividing, respectively, compared to 45 W for the nonconcentrated PV system under the same conditions; meanwhile, after the frequencydividing liquid absorption characteristics are strengthened, the temperature of the cell is lowered, the filling factor is enlarged, and the cell At the same time, after the enhancement of the crossover liquid absorption property, the cell temperature is reduced, the filling factor is increased, the cell performance is improved, and the thermal efficiency of the system is increased by 2.7%, but the crossover liquid absorption property reduces the incident solar irradiation on the surface of the cell, which results in the reduction of the total electrical efficiency of the system by nearly 0.6%. Experimental data support is provided for a crossovertype CPV/T system at low convergence multiples.

Proton exchange membrane (PEM) water electrolysis technology holds significant promise in the field of hydrogen production. To conduct an indepth investigation into the performance and optimization potential of this technology, this paper employs the commercial software Comsol Multiphysics to establish a threedimensional, twophase, nonisothermal fully coupled model of a proton exchange membrane electrolysis cell, taking into account the transport of water within the membrane. The research findings demonstrate that the trapezoidal channel design outperforms the rectangular channel configuration, resulting in a 5.5% performance enhancement at a working voltage of 2.4 V. Through an analysis of water/gas distribution, temperature profiles, membrane water content, and membrane conductivity variations with voltage, it is revealed that the trapezoidal channel exhibits superior gas/liquid transport performance compared to the rectangular channel. At 2.4 V voltage, the trapezoidal channel's anode catalytic layer exhibits a 7.92% increase in water saturation relative to the rectangular channel, a 10.36% reduction in oxygen concentration, a 1.22% elevation in membrane water content, and a 1.75% increase in membrane conductivity, despite the temperature differences within the membrane being relatively insignificant.

With the largescale integration of distributed power sources, the shortcircuit current characteristics of large power grids become more complex and difficult to predict. Based on this, this article proposes a new energy grid shortcircuit current prediction technology based on improved convolutional neural networks. Firstly, analyze the characteristics of shortcircuit current, perform variational mode decomposition on shortcircuit current, and obtain the intrinsic mode function; Secondly, the convolutional neural network is improved by utilizing multiscale feature extraction to maximize the features of current fault data, introducing attention mechanisms to extract important information, and using skip connections during the convolutional process to prevent information loss during forward transmission, which is beneficial for improving the accuracy of prediction. A shortcircuit current prediction model based on the improved convolutional neural network is constructed; Finally, the PSCAD/EMTDC power grid model was validated, and the experimental results showed that the proposed method has high accuracy in predicting the peak shortcircuit current. Compared with common limit learning machines and support vector machines, the average relative error decreased by 0.61% and 1.09%, respectively. This verified the effectiveness of the proposed method and laid the foundation for limiting shortcircuit current in large power grids.