Improve Power Generation Research Articles

Potential of Cellulomonas fimi for polysaccharide-fueled microbial fuel cells

Abstract This study examined the feasibility of using Cellulomonas fimi and Shewanella oneidensis as microbial fuel cells fueled by starch, cellulose, chitin, and chitosan. To our knowledge, this is the first report of power generation using C. fimi fueled by these polysaccharides other than cellulose, furthermore the first report of S. oneidensis fueled by chitosan. No differences were observed in the power generation capacities between C. fimi and S. oneidensis when chitin and chitosan were used. However, C. fimi demonstrated effective power generation from starch and cellulose, showing a maximum current density of 17.4 mA m−2 for starch and 38.8 mA m−2 for cellulose. S. oneidensis could not utilize these fuels to generate electricity. Power generation using C. fimi fueled by starch and cellulose produced acetic acid, lactic acid, and formic acid. However, when chitin and chitosan were used, only acetic acid was produced. These results indicate that electron transfer from C. fimi to the anode may be inefficient. The accumulation of organic acid in the anode solution indicates insufficient electron transfer to the anode. To improve power generation efficiency, it may be necessary to enhance electron transfer from the cells to the anode, for example, by adding a mediator.

Wear fault diagnosis in hydro-turbine via the incorporation of the IWSO algorithm optimized CNN-LSTM neural network

When a hydropower unit operates in a sediment-laden river, the sediment accelerates hydro-turbine wear, leading to efficiency loss or even shutdown. Therefore, wear fault diagnosis is crucial for its safe and stable operation. A hydro-turbine wear fault diagnosis method based on improved WT (wavelet threshold algorithm) preprocessing combined with IWSO (improved white shark optimizer) optimized CNN-LSTM (convolutional neural network-long-short term memory) is proposed. The improved WT algorithm is utilized to denoise the preprocessing of the original signals. Chaotic mapping, bird flock search, and cosine elite variation strategies are introduced to enhance the WSO algorithm's robust performance, and the CNN-LSTM model's hyperparameters are optimized using the IWSO algorithm to improve the diagnostic performance. The experimental results show that the accuracy of the proposed method reaches 96.2%, which is 8.9% higher than that of the IWSO-CNN-LSTM model without denoising. The study also found that the diagnostic accuracy of hydro-turbine wear faults increased with increasing sediment concentration in the water. This study can supplement the existing hydro-turbine condition monitoring and fault diagnosis system. Meanwhile, diagnosing wear faults in hydro-turbines can improve power generation efficiency and quality and minimize resource consumption.

Research on short-term optimization and scheduling of multi-energy complementary systems based on forecast scenario dynamic correction

The inherent unpredictability and instability of renewable energy sources, such as wind and solar power, hinder the precise execution of power generation plans in complementary systems, posing significant challenges to their integration into power grids. Therefore, this study proposes a dynamic correction method for wind and solar output forecast scenarios in the short-term scheduling of wind-solar-hydro complementary systems. The method utilizes statistical analysis of forecast errors in wind and solar power outputs to characterize uncertainty patterns across different forecast levels and constructs a typical forecast scenario set based on single-day forecasts. This approach probabilistically models each scenario according to the temporal migration patterns of wind and solar power outputs and develops a neural network-based dynamic correction fusion model to refine the forecasts. Application of this method in a case study of the Yalong River Basin demonstrated that, after applying dynamic correction to the forecast scenarios, the mean absolute error in total wind and solar output predictions during the wet and dry seasons was reduced by 50.73 % and 47.95 %, respectively. Additionally, the dynamic correction reduced the maximum residual load on typical wet and dry days by 82.70 % and 62.37 %, respectively, and decreased the total intraday residual electricity by 91.17 % and 73.24 %, compared to single-day forecasts. The study concludes that the proposed dynamic correction method enhances power system stability and improves power generation efficiency and reliability.

A novel array connection strategy for improving power generation efficiency of photovoltaic systems during localized shading

A novel array connection strategy for improving power generation efficiency of photovoltaic systems during localized shading

Resistance training leading to repetition failure increases muscle strength and size, but not power-generation capacity in judo athletes.

Strength-trained athletes has less trainability in muscle size and function, because of their adaptation to long-term advanced training. This study examined whether resistance training (RT) leading to repetition failure can be effective modality to overcome this subject. Twenty-three male judo athletes completed a 6-week unilateral dumbbell curl training with two sessions per week, being added to in-season training of judo. The participants were assigned to one of three different training programs: ballistic light-load (30% of one repetition maximum (1RM)) RT to repetition failure (RFLB) (n = 6), traditional heavy-load (80% of 1RM) RT to repetition failure (RFHT) (n = 7), and ballistic light-load (30% of 1RM) RT to non-repetition failure (NRFLB) (n = 10). Before and after the intervention period, the muscle thickness (MT) and the maximal voluntary isometric force (MVC) and rate of force development (RFDmax) of elbow flexors were determined. In addition, theoretical maximum force (F0), velocity (V0), power (Pmax), and slope were calculated from force-velocity relation during explosive elbow flexion against six different loads. For statistical analysis, p < 0.05 was considered significant. The MT and MVC had significant effect of time with greater magnitude of the gains in RFHT and NRFLB compared to RFLB. On the other hand, all parameters derived from force-velocity relation and RFDmax did not show significant effects of time. The present study indicates that ballistic light-load and traditional heavy-load resistance training programs, leading to non-repetition failure and repetition failure, respectively, can be modalities for improving muscle size and isometric strength in judo athletes, but these do not improve power generation capacity.

Parametric analysis of reverse electrodialysis process for improved power generation: Influence of spacer thickness, feed solution flow rate, membrane, and concentration

Reverse electrodialysis (RED) technology produces electrical energy from a salinity gradient by transporting ions through ion-exchange membranes (IEM). Although research into this technology has recently seen considerable growth, there is still interest in improving its efficiency. As a result, a mathematical model to study the RED process was developed using Aspen Custom Modeler software, focusing on three membranes (Fumasep (FAD/FKD), Fujifilm Type 10, and Selemion (AMV/CMV)). A parametric analysis was conducted, considering spacer thickness, salinity gradient, and feed-solution flow rates. Evaluation of internal resistance, gross power density, and net power density was also performed. Results showed that using a thicker spacer (270 μm) in the high compartment and a thinner spacer (150 μm) with a high Reynolds number in the low compartment led to higher net power densities of 2.17 W·mcp−2 and 6.74 W·mcp−2 for Fumasep (FAD/FKD) membrane with (brackish/seawater) and (brackish/brine), respectively. Among the membranes tested, Fumasep (FAD/FKD) and Fujifilm Type 10 had similar results, with the former slightly exceeding the latter. However, the Selemion membrane (AMV/CMV) performed poorly in all tests.

Staying positive: producing net power.

The Spherical Tokamak for Energy Production (STEP) prototype powerplant (SPP) will be a first-of-a-kind powerplant-its prime objective is to export electrical power, to the national power transmission system ('grid'), above 100 MWe. As part of a wider issue, addressing the STEP concept design, this article seeks to explore how electrical power will be generated from a spherical tokamak heat source. Accordingly, the following key functions of the SPP power infrastructure are reviewed.Cooling the tokamak: cooling the tokamak while extracting useful thermal energy.Generating power: conversion of thermal energy to electrical energy (power generation).Managing energy: management of the site-wide distribution, storage and energy export.In each of these areas, the design scope, challenges and solution spaces have been discussed. This has shaped the design of the SPP power infrastructure, which in turn has ensured a powerplant design focused on operability and performance. Furthermore, it has been demonstrated that the SPP will achieve its prime objective in generating net power, which is enabled by a unique power infrastructure. Confidence in the ability to generate net power will be refined as the design matures. Finally, this article recommends key opportunities that STEP could use to improve power generation and reduce the parasitic load of the SPP.This article is part of the theme issue 'Delivering Fusion Energy - The Spherical Tokamak for Energy Production (STEP)'.

Evaluation of Seasonal Prediction of Extreme Wind Resource Potential over China Based on a Dynamic Prediction System SIDRI-ESS V1.0

Wind resources play a pivotal role in building sustainable energy systems, crucial for mitigating and adapting to climate change. With the increasing frequency of extreme events under global warming, effective prediction of extreme wind resource potential can improve the safety of wind farms and other infrastructure, while optimizing resource allocation and emergency response plans. In this study, we evaluate the seasonal prediction skill for summer extreme wind events over China using a 20-year hindcast dataset generated by a dynamical seamless prediction system designed by Shanghai Investigation, Design and Research Institute Co., Ltd. (Shanghai, China) (SIDRI-ESS V1.0). Firstly, the hindcast effectively simulates the spatial distribution of summer extreme wind speed thresholds, even though it tends to overestimate the thresholds in most regions. Secondly, high prediction skills, measured by temporal correlation coefficient (TCC) and normalized root mean square error (nRMSE), are observed in northeast China, central east China, southeast China, and the Tibetan Plateau (TCC is about 0.5 and the nRMSE is below 0.9 in these regions). The highest skills emerge in southeast China with a maximum TCC greater than 0.7, and effective prediction skill can extend up to a 5-month lead time. Ensemble prediction significantly enhances predictive skill and reduces uncertainty, with 24 ensemble members being sufficient to saturate TCC and 12–16 members for nRMSE in most key regions and lead times. Furthermore, we show that the prediction skill for extreme wind counts is strongly related to the prediction skill for summer mean wind speeds, particularly in southeast China. Overall, SIDRI-ESS V1.0 shows promising performance in predicting extreme winds and has great potential to provide services to the wind industry. It can effectively help to optimize wind farm operating strategies and improve power generation efficiency. However, further improvements are needed, particularly in areas where prediction skills for extreme winds are influenced by smaller-scale weather phenomena and areas with complex underlying surfaces and climate characteristics.

Enhancing the performance of PVT-TEG power generation systems by heat pipes and Fe3O4 nanofluids

In recent years, enhancing the cooling of photovoltaic (PV) panels to improve overall energy efficiency has become a significant focus. In this study, we propose an improved power generation system integrating semiconductor thermoelectric generators (TEGs). The integration involves the installation of several heat pipes and serpentine copper tubes on the back of PV panels, with nanofluids flowing through the copper tubes as the working fluid, effectively cooling the PV panels. The copper tube is connected to the heat exchanger on the high-temperature side of the TEGs, forming a closed-loop circulation system. Consequently, the cooling fluid transfers heat to the hot surface of the TEGs, utilizing waste heat generated by the PV panels for electricity generation. The TEGs consist of 10 thermoelectric modules, with the low-temperature end in contact with the finned aluminum plate (TEGs radiator) and actively cooled by natural air cooling. Additionally, to validate the design rationale, multiple sets of comparative experiments were arranged. The experiments utilized nanofluids with volume fractions of 0.5 %, 1 %, 2 %, and 3 %, and water, as the cooling fluid. The experimental results demonstrated that the maximum operating temperature of the reference PV panel can reach 81.9 °C, and the average and maximum exergy efficiencies throughout a day are recorded at 16.91 % and 31.84 %, respectively. It’s noteworthy that the most significant cooling effect occurs when the nanofluid volume fraction is 2 %, reducing the maximum temperature of the PVT panel to 61.5 °C. Simultaneously, the average daily exergy efficiency and exergy efficiency of the PVT-TEG power generation system reached peak values of 21.06 % and 39.87 %, respectively. Compared to the reference PV panels, the output power of the PVT-TEG system increases by up to 30.51 %.

Research on Aerodynamic Characteristics of Three Offshore Wind Turbines Based on Large Eddy Simulation and Actuator Line Model

Investigating the aerodynamic performance and wake characteristics of wind farms under different levels of wake effects is crucial for optimizing wind farm layouts and improving power generation efficiency. The Large Eddy Simulation (LES)–actuator line model (ALM) method is widely used to predict the power generation efficiency of wind farms composed of multiple turbines. This study employs the LES-ALM method to numerically investigate the aerodynamic performance and wake characteristics of a single NREL 5 MW horizontal-axis wind turbine and three such turbines under different wake interaction conditions. For the single turbine case, the results obtained using the LES-ALM method were compared with the existing literature, showing good agreement and confirming its reliability for single turbine scenarios. For the three-turbine wake field problem, considering the aerodynamic performance differences under three cases, the results indicate that spacing has a minor impact on the power coefficient and thrust coefficient of the middle turbine but a significant impact on the downstream turbine. For staggered three-turbine arrangements, unilateral turbulent inflow to the downstream turbine causes significant fluctuations in thrust and torque, while bilateral turbulent inflow leads to more stable thrust and torque. The presence of two upstream turbines causes an acceleration effect at the inflow region of the downstream single turbine, significantly increasing its power coefficient. The findings of this study can provide methodological references for reducing wake effects and optimizing the layout of wind farms.

Role of primary drivers leading to emission reduction of major air pollutants and CO2 from global power plants

Electricity production is a significant source of air pollution. Various factors, including electricity demand, generation efficiency, energy mix, and end-of-pipe control measures, are responsible for the emission changes during electricity generation. Although electricity production more than doubled from 1990 to 2017, air pollutant emissions showed a moderate increase or decrease, which was attributed to mitigating drivers such as increased clean energy use, improved power generation efficiency, and widespread installation of end-of-pipe control facilities. The absence of these mitigating drivers would have increased CO2, fine particulate matter (PM2.5), black carbon, SO2, and NOx emissions in 2017 by 165 %, 403 %, 1070 %, 614 %, and 274 % than their actual levels, respectively. The improved electricity generation efficiency reduced potential CO2, PM2.5, SO2, and NOx emissions by 30 %, 295 %, 119 %, and 52 % compared to actual emissions, respectively. Meanwhile, the installation of end-of-pipe facilities reduced potential SO2 and PM2.5 emissions by 34.7 and 4.0 Tg, respectively. Considerable differences in emissions among countries were found to be attributable to their differences in electricity demand and the implementation of local mitigating polices.

Enhancing thermodynamic performance with an advanced combined power and refrigeration cycle with dual LNG cold energy utilization

Utilizing waste heat to drive thermodynamic systems is imperative for improving energy efficiency, thereby improving sustainability. A combined cooling and power systems (CCP) utilizes heat from a temperature source to deliver both power and cooling. However, CCP systems utilizing LNG cold energy suffers from low second law efficiency due to significant temperature differences. To address this, an “Advanced Power and Cooling with LNG Utilization (ACPLU)” system is proposed, integrating a cascaded transcritical carbon dioxide (TCO2)-LNG cycle with an Organic Rankine cycle (ORC) for improved power generation and an absorption refrigeration system (ARS) for simultaneous cooling. This study evaluates the second law efficiency, net work output, and exergy destruction performance through a sensitivity analysis, optimizing variables such as heat source temperature, superheater temperature difference, ORC and CO2 turbine inlet and condenser pressures, evaporator temperature, and pinch point temperatures of heat exchangers and generator. Compared to previous studies on CCP systems, the ACPLU shows a superior performance, with a second law efficiency of 27.3 % and a net work output of 11.76 MW. Cyclopentane as an ORC working fluid resulted in the highest second law efficiency of 29.06 % and net work output of 12.27 MW. Parametric analysis suggested that heat source temperature significantly impacts net power output. The exergy analysis concluded that a high-pressure ratio and good thermal match between the heat exchangers enhance overall performance. Utilizing artificial neural network (ANN) to produce a multiple-input-multiple-output (MIMO) objective function and performing multi-objective optimization (MOO) using genetic algorithm (GA), an improved second law efficiency and net power output by 28.11 % and 14.16 MW respectively, with pentane as the working fluid, is demonstrated. An average cost rate of 9.121 $/GJ was observed through a thermo-economic analysis. The ACPLU system is promising for medium temperature waste heat recovery, such as, pharmaceutical manufacturing plants.

A comprehensive review and comparison of cooling techniques for photovoltaic panels: An emphasis on experimental setup and energy efficiency ratios

This study delves into exploring and comparing various cooling technologies for PV panels, with a special focus on revealing the harmful effect of excessive heat absorption on solar energy efficiency. Effective temperature management and dissipation of excess heat are essential to protect the integrity of PV panels and improve power generation. With a strong emphasis on the importance of experimental setups, this comprehensive research examines the methodologies and cooling methods used across a wide range of experimental studies, illustrating the complexities of data collection and analysis. Supported by schematic illustrations depicting various experimental setups, this study demystifies the complexities inherent in distinct PV cooling methods. Furthermore, the research extends beyond experimentation to include a comprehensive examination of the energy and economic aspects associated with various cooling technologies, including the calculation and presentation of energy efficiency ratios. It is worth noting that the results confirm the superiority of passive cooling techniques, including heat sinks (43 %), PCM (25), evaporation techniques (36 %), and spray cooling systems (54 %), in contrast to active cooling methods, as indicated by their net energy efficiency ratios. Passive cooling is advantageous for its low maintenance and cost-effectiveness, whereas active cooling, though effective in hot climates and enhancing PV efficiency, incurs higher initial costs and maintenance requirements. The novelty of this research lies in its detailed classification of cooling techniques and the specific materials used for each method, coupled with a thorough examination of the measuring tools employed. Furthermore, this study includes a comprehensive comparative analysis based on energy and economic aspects, alongside the advantages and disadvantages of each cooling technique. These invaluable insights hold the potential to revolutionize the development of efficient and dependable cooling strategies for PV systems, thereby elevating the feasibility and sustainability of solar energy as a pivotal cornerstone in our future energy landscape.

Research on detection method of photovoltaic cell surface dirt based on image processing technology

In view of the reduced power generation efficiency caused by ash or dirt on the surface of photovoltaic panels, and the problems of heavy workload and low efficiency faced by manual detection, this study proposes a method to detect dust or dust on the surface of photovoltaic cells with the help of image processing technology to timely eliminate hidden dangers and improve power generation efficiency.This paper introduces image processing methods based on mathematical morphology, such as image enhancement, image sharpening, image filtering and image closing operation, which makes the image better highlight the target to be recognized. At the same time, it also solves the problem of uneven image binarization caused by uneven illumination in the process of image acquisition. By using the image histogram equalization, the gray level concentration area of the original image is opened or the gray level is evenly distributed, so that the dynamic range of the pixel gray level is increased, so that the image contrast or contrast is increased, the image details are clear, to achieve the purpose of enhancement. When identifying the target area, the method of calculating the proportion of the dirt area to the whole image area is adopted, and the ratio exceeding a certain threshold is judged as a fault. In addition, the improved A* path planning algorithm is adopted in this study, which greatly improves the efficiency of the unmanned aerial vehicle detection of photovoltaic cell dirt, saves time and resources, reduces operation and maintenance costs, and improves the operation and maintenance level of photovoltaic units.

Fabrication and optimization of bioelectrochemical system using tetracycline-degrading bacterial strains for antibiotic wastewater treatment

In this study, a microbial fuel cell was constructed using Raoultella sp. XY-1 to efficiently degrade tetracycline (TC) and assess the effectiveness of the electrochemical system. The degradation rate reached 83.2 ± 1.8 % during the 7-day period, in which the system contained 30 mg/L TC, and the degradation pathway and intermediates were identified. Low concentrations of TC enhanced anodic biofilm power production, while high concentrations of TC decreased the electrochemical activity of the biofilm, extracellular polymeric substances, and enzymatic activities associated with electron transfer. Introducing electrogenic bacteria improved power generation efficiency. A three-strain hybrid system was fabricated using Castellaniella sp. A3, Castellaniella sp. A5 and Raoultella sp. XY-1, leading to the enhanced TC degradation rate of 90.4 % and the increased maximum output voltage from 200 to 265 mV. This study presents a strategy utilizing tetracycline-degrading bacteria as bioanodes for TC removal, while incorporating electrogenic bacteria to enhance electricity generation.

Multi-objective RSM-based optimization of diesel-diethyl ether blends in diesel engine to achieve sustainable development goals

With the plummeting reserves of fossil fuels at present, there has been a growing imperative for sustainable energy technologies over the last couple of decades. This imperative highlight the need for a shift towards renewable sources, not just to fulfill the current energy demands but also to lessen environmental degradation, fostering an eco-conscious and sustainable future. One way to accomplish such a goal is the utilization of diesel-diethyl ether (D-DEE) blends, instead of neat diesel in compression ignition (CI) engines. This study presents the multi-objective RSM-based analysis and optimization of D-DEE blends (0,5 and 10 %. vol) at manifold engine speeds (1200, 1400, and 1600 rpm). The responses that were recorded in this research included brake power (B.P), brake thermal efficiency (BTE), brake-specific fuel consumption, and CO2 emissions. The results demonstrated an increment in brake power as well as brake thermal efficiency, along with the enhanced CO2 emissions whereas the BSFC decreased while using the Di-ethyl ether enrichment in a 4-stroke CI engine. Upon implementing optimization parameters, the results illustrated the optimal blend ratio of 9.5786 %. vol at 1600 rpm, along with the associated values of B.P, BTE, BSFC, and CO2 emissions which were, 1.26726 kW, 20.861 %, 0.3354, and 7.837 % vol., respectively for this optimized fuel. This study addresses the lack of research on optimizing engine conditions for diesel-DEE blends in CI engines, specifically focusing on diethyl ether as a renewable fuel. Using response surface methodology (RSM), it aims to bridge this gap and contribute to the understanding of optimal blend utilization. The current research also aligns with the attainment of an important sustainable development goal (SDG) which includes the availability of responsible consumption (SDG 12) and improved power generation, contributing towards a greener and cleaner future.

Three-dimensional biochar aerogel anode derived from expired yogurt for enhancing power efficiency and electron transfer at the bacteria-anode interface in microbial fuel cells

Microbial fuel cells (MFCs) are a promising and sustainable form of fuel cells that utilize electroactive microorganisms to generate electricity. However, the practical application of MFCs is limited by their low power density, inadequate biocompatibility, and suboptimal electrochemical properties. To overcome these challenges, we developed a three-dimensional high performance biochar aerogel anode (YP) made from a mixture of expired yogurt and polyvinyl alcohol (PVA) and investigated its effect on the performance of the MFC. The incorporation of PVA into the YP anodes resulted in improved electrolyte wettability and hydrophilic surfaces, thereby enhancing biocompatibility and bacterial adhesion, especially with Geobacter, reaching up to 89.6% coverage. The biofilm content reached 68.1 mg, which is 1.7 times that of carbon cloth, which improved the electron transfer efficiency at the bacteria-anode interface. Moreover, the mechanical strength of the YP anodes was increased, improving anode stability and durability. In addition, the aerogel anode exhibited impressive power density and current density values of 15.87 W m−3 and 43.76 A m−3, respectively. These values are 3.16 times and 3.60 times higher than the 5.03 W m−3 and 12.13 A m−3 of carbon cloth. This study makes a significant contribution to improving power generation in MFCs, optimizing electron transfer at the bacteria-anode interface, and paving the way for future practical applications of MFCs.

Model development and preliminary study of the concentrated-radiative cooling based thermoelectric generator

Photovoltaic (PV) cells, widely adopted in solar energy applications, achieve a maximum efficiency of only 30 % and remain inactive during nighttime. In contrast, solar-thermal power generation has surpassed 50 % efficiency. To address these limitations, we introduce the Solar- Radiative cooling -Thermoelectric system (Solar-RC-TE), integrating solar energy, radiative cooling (RC), and thermoelectric power generation. This system enhances energy density, reduces consumption, lowers costs, and minimizes environmental impact while enabling nighttime power generation. For the optimization of the Solar-RC-TE model to further improve power generation efficiency, we employ the comprehensive three-dimensional methodology of COMSOL, considering material properties, structural configurations, and environmental factors. Research findings reveal that a 710 mm × 710 mm radiative cooling film can generate 386.12 mV of voltage on the thermoelectric generator (TEG). Environmental factors, such as increased solar irradiance, lead to temperature elevation, affecting efficiency. Overheating and higher wind speeds also contribute to decreased TEG efficiency. Regarding materials, improving the thermoelectric figure of merit (ZT) and film emissivity positively impact power generation efficiency. Elevating the film emissivity from 0.7 to 0.89 significantly enhances performance by 1.2 %. These outcomes provide a tangible and practical solution for addressing energy crises and environmental concerns.

Solar photovoltaic automatic tracking device based on IoT and BDS

Solar photovoltaic automatic tracking device based on IoT and BDS

Application of Fuzzy Control and Neural Network Control in the Commercial Development of Sustainable Energy System

Sustainable energy systems (SESs) occupy a prominent position in the modern global energy landscape. The purpose of this study is to explore the application of fuzzy control and neural network control in photovoltaic systems to improve the power generation efficiency and stability of the system. By establishing the mathematical model of a photovoltaic system, the nonlinear and uncertain characteristics of photovoltaic system are considered. Fuzzy control and neural network control are used to control the system, and their performance is verified by experiments. The experimental results show that under the conditions of low light and moderate temperature, the fuzzy neural network control achieves a 3.33% improvement in power generation efficiency compared with the single control strategy. Meanwhile, the system can still maintain relatively stable operation under different environmental conditions under this comprehensive control. This shows that fuzzy neural network control has significant advantages in improving power generation efficiency and provides beneficial technical support and guidance for the commercial development of SESs.