Focusing on the optimization of the spacing parameters of a dual-float wave energy conversion （WEC） device, systematically investigating the influence of the wavelength-to-float spacing ratio on the energy harvesting performance and hinge loads under both regular and irregular wave conditions. Through the application of numerical simulation and model trial methods, the working mechanism of the wavelength-to-float spacing ratio on system performance is revealed. The findings indicate that under regular wave conditions, the energy harvesting width ratio of the system exhibits a significant wavelength dependency, reaching a peak value of 0.44 when the wavelength-to-float spacing ratio is 0.25, which is a 46.1% increase compared to the case with a ratio of 0.063. Under irregular wave conditions, appropriately adjusting the float spacing can reduce the horizontal hinge load at the connection joint by approximately 75% and the vertical hinge load by about 25%, thereby effectively enhancing the service life of the device. Oblique incident waves induce a nonlinear response in energy harvesting width ratio, and the system achieves the optimal energy harvesting width ratio when the incident angle is 15°, which is a 22% improvement compared to the case of vertical incidence.

Taking a horizontal pendulum wave energy converter （WEC） based on a spiral spring-flywheel power take-off （PTO） system as research object, considering the spring stiffness and flywheel moment of inertia as variables, a fully coupled numerical model of the WEC is constructed to investigate energy acquisition under multiple degrees of freedom through model test and numerical simulation. The research shows that installing a spiral spring-flywheel PTO can enhance the stability of energy acquisition and increase the power generation bandwidth of the WEC. Under regular wave conditions, the standard deviation of power is positively correlated with the spring stiffness and negatively correlated with the flywheel moment of inertia, with this phenomenon being more pronounced near the resonance period.

Taking a large public building in a hot summer and cold winter area as the research object, a simulation model of shallow medium deep geothermal cascade utilization coupled heating was built based on TRNSYS software. The operating characteristics of the shallow medium deep coupled heating system during the heating season were analyzed and compared with a single medium deep geothermal cascade utilization heating system and a shallow ground source heat pump system. The results show that the coupled system is superior to a single deep or shallow geothermal heating system in terms of end heat transfer, geothermal return water temperature, heat pump unit COP, and system energy utilization efficiency. The system is more stable and adaptable. The average energy utilization efficiency of the system reaches 0.95, verifying the feasibility of the coupled system operation.

To address the operational instability of the geothermal reheated dual-pressure organic Rankine cycle （R-DPORC） system caused by transient fluctuations in cold/heat sources, this study proposes two targeted control strategies： constant-load control and variable-load control. A dynamic simulation model of the R-DPORC system has been developed using Matlab/Simscape to analyze its dynamic response characteristics under varying cold/heat source conditions and load changes. The variations in system efficiency, degree of superheat, pressure, and mass flow rate are investigated. Results indicate that regulating the flow rate of working fluid pump is a critical method for achieving dynamic control of the R-DPORC system. Depending on specific operational scenarios, different operating modes can be flexibly combined. Through adjustments, the system can gradually achieve a new stable state and meet output requirements. Moreover, the optimized control strategy significantly improves the dynamic adaptability of the system under complex operating conditions, ensuring long-term stable operation and higher energy efficiency.

To address the high energy consumption and significant carbon reduction challenges in steel plate degreasing for industrial equipment manufacturing, this study proposes a novel PV/T-coupled ground-source heat pump heating system for degreasing （PV/T-GSHPHD） and its corresponding operational strategy. A comprehensive energy conversion model was established for system components, and an optimization configuration model was developed to enhance economic efficiency while ensuring soil thermal balance through typical scenario clustering analysis. The optimal system components and their respective capacities were determined based on these models. Results show that the optimal PV/T-GSHPHD system configuration consists of a PV/T module, a ground-source heat pump, a heat collection tank, and a thermal storage tank, with soil heat supplementation achieved via the PV/T module. Compared to a conventional electric boiler heating system, the PV/T-GSHPHD achieves 69.1% reduction in annual heating costs and 83.3% reduction in environmental costs, demonstrating significant economic and environmental advantages. The analysis of operating characteristics of the system on typical days indicates that the PV/T-GSHPHD effectively reduces grid peak loads year-round, with only 13% of its equivalent electricity sourced from the grid during summer, thereby alleviating regional electricity supply pressures. This study highlights the technical and economic viability of PV/T-GSHPHD as a sustainable alternative for industrial degreasing heating applications.

A numerical model based on the finite difference method is developed for deep borehole heat exchangers used in medium-deep ground source heat pump systems, and the heat transfer characteristics under dynamic thermal load conditions are analyzed. Using this model along with actual meteorological data from Qingdao and validation via field experiments, the effect of fluid velocity, geothermal gradient, and geological parameters on the thermal performance of the deep borehole heat exchanger are investigated. The results indicate that while the inlet and outlet temperatures of the exchanger fluctuate in response to dynamic thermal loads, the outlet temperature exhibits greater stability. Although increasing fluid velocity enhances heat extraction, it also leads to higher heat loss caused by reverse heat transfer in shallow rock and soil layers. Higher geothermal gradients significantly improve heat extraction performance, and heat exchange efficiency is found to be greater in deeper regions compared to shallow zones.

Accurate estimation of lithium battery capacity is the basis for its safe and reliable operation. However, the lithium battery capacity attenuation mechanism is not clear, and its capacity shows nonlinear attenuation during use, making it extremely difficult to accurately estimate the lithium battery capacity. In order to solve these problems, this paper proposes a method to estimate the capacity of lithium batteries by using constant current charging data and a one-dimensional convolutional neural network model. Firstly, two aging feature sequences are extracted from the constant current charging stage： the constant current charging capacity and the time interval of equal charging voltage rise. Secondly, in order to make full use of the measured charging information and extract the key capacity attenuation factors in aging feature sequences, a new one-dimensional convolutional neural network capacity estimation model is constructed. Finally, the proposed capacity estimation model is verified and analyzed on the aging data set of lithium batteries in Maryland. The experimental results show that the proposed method can achieve an accurate and robust estimation of battery capacity on the complete constant current charging curve and partial constant current charging curve, which is better than other capacity estimation methods. The root mean square error （RMSE） and mean absolute error （MAE） can be controlled within 0.68% and 0.50%. Furthermore, after transfer learning with a small amount of date, the proposed model can accurately estimate battery discharge capacity at other discharge rates. After transfer learning for the first 50 cycles, the root mean square error of the model at a 0.5C discharge rate is only 0.58%.

The new power system in agricultural parks faced the problems of poor system operation stability and low comprehensive economic efficiency due to power fluctuation on the generation side and diverse and uncertain load demand on the consumption side. For this reason, this paper constructed a model of optimized allocation of energy storage capacity based on load division, aiming to improve the stability and economic efficiency of the system. Firstly, a two-stage energy storage allocation tactic was determined in accordance with load segmentation. In this regard, the conventional load demand was preferentially supplemented by photovoltaic power, and the root-mean-square （RMS） envelope technique was harnessed to quell the unsteady power oscillations. Secondly, for the agricultural load demand, the net present value （NPV） was constructed as the optimization objective function. This was then conjoined with the time-of-use tariff strategy to impel the energy storage capacity to perform peak shaving, valley filling, and consumption compensation. Finally, by leveraging the summer and winter data of an agricultural park and deploying the particle swarm optimization algorithm, the outcomes of the two-stage allocation were attained. Namely, the overall power oscillations of the summer and winter loads are abated by around 32% and 27% respectively. The peak-to-valley ratios of the loads are lessened by 13.1% and 14.4% respectively. Moreover, the comprehensive economic return of energy storage is enhanced by 21% when compared with the direct allocation method. The exemplifications corroborated that the methodology proposed in this paper could efficaciously attenuate the microgrid power fluctuation quandary and augment the comprehensive economic benefits engendered by energy storage allocation.

Currently, the centralized optimization regulation strategy for conventional energy storage systems struggles to meet the operational requirements of centralized-distributed energy storage systems in rural areas. To address this challenge, this study examines a typical village in the Central Shaanxi Plain, Shaanxi Province, China, and integrates the usage patterns of agricultural machinery batteries into energy storage regulation. A peer-to-peer （P2P） trading mechanism for distributed energy storage, based on a combinatorial double auction, is proposed. In the trading environment, a double-layer optimal regulation model for rural centralized-distributed energy storage systems is established using a Stackelberg game approach and computationally solves it. The results show that 1）P2P trading are highly influenced by agricultural production patterns in both quantity and time. 2）P2P trading reduces the operating cost of each subject in the centralized-distributed energy storage system by 4.96%, reduces carbon emissions by 10.41%, and increases the PV consumption rate by 18.04%. 3）The annual electricity cost of different types of rural households is reduced by the increase of decentralized energy storage capacity, the reduction is more significant after the introduction of P2P trading, and the cost reduction is especially prominent for rural households with large rooftop PV installations, small electricity demand, and sufficient energy storage resources. Energy storage operator will suffer from the increase in revenue due to the increase in decentralized energy storage capacity, and the revenue will be further reduced after the introduction of P2P trading, but can be compensated for by appropriately charging operation and management fees.

A full-scale simulation model of a small air-cooled PEMFC stack is established in ANSYS/Fluent software, and the solution is obtained using computational fluid dynamics （CFD） methods. The effects of different altitudes and cathode stoichiometric ratios on the output performance, temperature, and oxygen concentration of the fuel cell stack are investigated. The results indicate that when the altitude increases to 3200 m, the peak power density of the fuel cell stack decreases by 21.6%. Increasing the cathode stoichiometric ratio can compensate for the decrease in oxygen concentration caused by higher altitudes, improve the output performance of the fuel cell stack and reduce the internal temperature differences within the stack.