Energy storage has the characteristics of strong flexibility and fast response, which can effectively alleviate load fluctuations, voltage instability and other problems caused by new energy access. This paper proposes a doublelayer power distribution based on an improved manta ray foraging optimization algorithm. The network energy storage site selection and capacity strategy aims to minimize energy storage investment costs, daily voltage fluctuations and daily load fluctuations, establish a twolayer site selection and capacity model, and introduce elite reverse learning strategies and adaptive tumbling factor improvements. The manta ray foraging optimization algorithm solution model was used, and the proposed method was simulated and verified using the connected new energy IEEE33 node distribution network as an example. The results showed that the proposed site selection and capacity optimization scheme can significantly reduce system voltage and load fluctuations, effectively reducing system investment costs.

This paper studies the discharge performance and capacity changes of lithiumion batteries in wind farm energy storage systems. Through a welldesigned experimental scheme and advanced testing technology, the performance and capacity and temperature changes of lithiumion batteries at different discharge rates are systematically discussed. In the state of natural heat dissipation, the maximum temperature of the battery center is 69.87 °C. After adding liquid cooling heat dissipation method, the maximum temperature of the battery center is 63.25 °C, the temperature drops by 9.475%, the required heat dissipation time is reduced from 45 min to 25 min, and correspondingly, the temperature is reduced from 48.87 °C to 35.00 °C. Compared with natural heat dissipation, liquid cooling heat dissipation has a significant effect in the process of highrate discharge, which not only reduces the temperature rise but also shortens the time required to reduce the temperature. The research results show that lithiumion batteries exhibit good discharge performance in wind farm energy storage system applications under high discharge rate, but the discharge capacity will decrease to a certain extent as the rate increases. The research results of this paper not only provide theoretical support for the application of lithiumion batteries in wind farm energy storage systems, but also provide practical guidance for optimizing their use and heat dissipation technology.

Carbonbased nanofluid has a higher heat transfer coefficient than other nanofluids and is relatively cheap. Graphene nanofluid has a high heat transfer coefficient, and reduced graphene oxide nanofluid has good stability. So, this article developed a new type of carbonbased composite nanofluid (waterbased graphene/reduced graphene oxide composite nanofluid) as the heat transfer medium in PV/T collectors by twostep method. The radiation intensity, flow rate, and composite nanofluid were analyzed. The effect of proportion on the water temperature at the outlet of the collector was studied, and the proportion of waterbased graphene/reduced graphene oxide with better heat transfer performance was obtained in different compound proportion ranges of the experiment. Experimental results show: the higher the radiation intensity, the faster the flow rate, the higher the water temperature at the outlet of the PV/T collector, the better the heat transfer effect of the nanofluid; when the composite ratio is 8:2, the heat transfer effect of the nanofluid is the best, the thermal efficiency and electrical efficiency of the PV/T system are 23.49% and 20.18% respectively. Compared with waterbased graphene and reduced graphene oxide, nanofluid has better heat transfer effect. The research of this article provides reference for the practical development of carbon nanofluids in heat transfer media.

In order to ensure the safe gridconnected operation of all DC wind power system, the system voltage stability control is crucial. At present, when the DC voltage stability control of all DC wind power system adopts the proportionalintegral (PI) control, the dynamic response speed is relatively slow under the nonnormal operation condition, the control accuracy is not high enough, and the PI parameter is more and more cumbersome and complicated to be calibrated. To address the above problems, this paper proposes a system DC voltage stabilization control strategy based on the principle of Finite Control SetModel Predictive Control (FCSMPC) to control the switching state of transistors of bridge arms of the system converter. The strategy combines the current prediction models of machineside rectifiers and gridconnected inverters, constructs a cost function with the output current of the converter as the control variable, takes the cost function as the optimization objective, introduces the delay compensation to improve the control accuracy in order to avoid the control delay caused by the computational delay, and introduces the weight coefficients to realize the multiobjective optimization, and generates the optimal switching combinations of the signals to trigger the converter through the traversal calculation. In this paper, the simulation model of all DC wind power system is established in Matlab/Simulink, and the proposed strategy is compared with the traditional PI control in different working conditions, and the simulation results effectively verify the static and dynamic performance of the proposed control strategy.

In order to reveal the influence of structural design parameters on design boundaries and improve the efficiency of wind turbine blade structure design, a structural optimization design method was proposed by combining the improved genetic algorithm NSGAII with the wind turbine blade structure design software FOUCS, and constructing an optimization design simulation system suitable for complex wind turbine blade structures. The 15 MW wind turbine benchmark model IEA15240RWT developed by NREL was chosen as the research object. The lamination, positioning, and width of the spar were treated as variables, while the blade weight and flapping stiffness were set as the optimization objectives. The selfdeveloped structural optimization design system was used to obtain the optimal solution set under different design conditions, and the influence of spar design parameters on the Pareto front was investigated. The calculation results demonstrate that for this particular blade of IEA15240RWT, a spar width of 1 000 mm, with the spar centerline positioned at 50% of the chord length, exhibits higher structural efficiency. The feasibility and effectiveness of the optimization design system for blade structure design were verified, and the system possesses strong scalability, providing new insights for more complex blade design tasks.

This paper studies the vulnerability assessment of distribution lines with multitype distributed power access. Firstly, in view of the uncertainty of distributed generation, the Latin hypercube sampling and synchronous back substitution method are used to generate the classic output scenarios of wind and light. Based on the complex network theory and power flow analysis, the vulnerability assessment indexes such as improved line betweenness, improved line degree, line voltage stability and fault loss are proposed from three aspects of power grid structure, operation state and fault influence. Secondly, a comprehensive evaluation model of game theoryVIKOR based on the correction weight of time series fluctuation characteristics is proposed to evaluate the vulnerability of the line. Then, the simulation verification is carried out based on the example of IEEE33 node system. The results show that the constructed index and evaluation model can accurately reflect the realtime fragile state of the line and conform to the time series fluctuation characteristics of source and load. Finally, based on the comprehensive vulnerability of lines in each period, combined with the distribution characteristics of line vulnerability in each period, the real vulnerability of the whole system line in different periods is further analyzed, which provides a theoretical basis for risk aversion of distribution network with distributed generation.

To address the issues of low thermal efficiency and poor economy in the ocean temperature differencedriven ocean thermal energy conversion (OTEC) system, OTEC combined with air conditioning (OTECAC) test system was designed and built.The system utilizes the cold energy of deep seawater in a graded manner by generating electricity and then cooling the air, thus significantly improving the conversion efficiency of ocean temperature difference energy. Performance evaluation metrics such as expansion output power,refrigeration capacity, and overall thermal efficiency were defined based on thermodynamic principles.Experimental tests were conducted to analyze the performance variations of the OTECAC system under different operating conditions. A comparison between OTECAC and standalone OTEC systems was also conducted. The results show that: the optimal expansion pressure ratio exists in the power generation system when the isentropic efficiency of the corresponding expander reaches a peak of 21.83% ; lowering the deep sea water temperature and increasing the chilled water flow rate can significantly improve the performance of the OTECAC system, and when the deep sea water temperature is lowered from 9 °C to 4 °C, the integrated exergy efficiency of the system increases from 47.25% to 51.60%; Under the same operating conditions, the OTECAC system has a power generation capacity of 97 W and a cooling capacity of 5 386 W.The thermal efficiency of the system increases from 1.21% to 17.60% after conversion compared to the standalone OTEC system.

In response to the issue that the randomness and volatility of wind power can affect the vulnerability assessment of the power grid and the identification of critical nodes, this paper proposes an intervalbased Electrical DebtRank algorithm to identify vulnerable nodes within the power grid. The method first incorporates the node's offset status and characteristics to improve the traditional Electrical DebtRank algorithm. Then, interval numbers are used to represent the randomness and volatility of wind power generation, leading to the development of the interval – based Electrical DebtRank algorithm to identify vulnerable nodes in a windintegrated power system. Finally, simulation results on the IEEE118 bus system demonstrate that when the vulnerable nodes identified by the proposed method are attacked, the system's power supply capability drops to 33% of its normal state, with a significant reduction in the system's power transmission capacity.

As China's new energy capacity grows and offshore wind power advances, controlling wind farms becomes more crucial. The study focuses on wake effect modeling and active control strategies within wind farm clusters. It optimizes wake estimation using the Gaussian FLORIDyn model, with search area pruning to speed up calculations without sacrificing precision or efficiency. A novel multiagent reinforcement learning method, guided by a GCNbased proxy wake model, is introduced. This model, grounded in wind farm wake dynamics, captures complex turbine interactions affecting output. Enhanced by wake aware reward sharing, the system improves optimization. Simulations test pruning's benefits and validate control strategies, confirming that advanced wake modeling and control tactics significantly contribute to solving wind farm control problems.

Aiming at the impact of thermal stratification on storage, the Fourzone storage model is proposed and Helmholtz energy equation is used to calculate the thermal properties of hydrogen in the study. According to different experimental conditions, the Nusselt number correlation formula is proposed, and the heat transfer coefficient of condensation surface is modified, and the modified Fourzone model is built. Compared with the experimental data, the error of the modified model is less than 3%, which has higher accuracy. On this basis, the influence of different filling ratio on selfpressurized storage is studied. The result shows that with the increase of filling ratio, the selfpressurized rate of storage tank first slows down and then becomes faster. There is an optimal filling ratio when liquid hydrogen is stored at low temperature, which makes the safe storage time the longest.