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Wind power has become an essential direction for transforming energy structures in energy-intensive seawater desalination under the dual goals of carbon peaking and carbon neutrality. In this study, the energy footprint of the case project is analyzed by combining the hybrid life cycle analysis and environmentally extended input-output modeling, which is compared with the traditional thermal desalination processes from the whole life cycle perspective. The analysis revealed that the total energy consumption of the seawater desalination driven by wind power generation can be reduced by 79.77% compared with the traditional thermal drive mode under the same water production scale. Although the energy consumption in the construction phase accounts for 24.97% of the total, the energy consumption per unit of water production can be reduced by about 80% after adopting wind power technologies. The payback period is 7.2 years, that is, the energy consumption can be balanced after around 7 years during the operation phase. The results showed that the wind-driven seawater desalination system can significantly decrease the energy consumption of the project, which attempts to provide implications for the upgrading of energy-intensive seawater desalination in coastal areas towards low-carbon transition.

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Solar photovoltaic (PV) projects are pivotal in addressing climate change and fostering a sustainable energy future. However, the complex landscape of renewable energy investments, characterized by high upfront costs, market uncertainties, and evolving technologies, demands innovative evaluation methods. The Real Options Approach has emerged as a powerful tool, offering strategic flexibility in decision-making under uncertainty. This paper comprehensively analyzes the application of real options for evaluating solar photovoltaic projects in 2008-2023. Analysis of document descriptors (author keywords, index keywords, and noun phrases extracted from titles and abstracts) reveals that the dominant research topics in the last ten years (2014-2023) include investment optimization, strategic analysis, energy policy, optimization of energy generation and investments in wind energy. These descriptors are used to analyze the evolution of research interests on a two-year basis and reveal the yearly evolution of the research topics. Finally, the concept of emergence is used to unveil emerging research trends, providing valuable insights for researchers and practitioners in the renewable energy sector. Ultimately, this work contributes to a deeper understanding of how real options analysis empowers decision-makers to make informed choices in advancing clean and sustainable energy solutions.

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Solar energy, with its sustainable properties, has garnered considerable attention for its potential to produce green electricity and clean water. This paper proposes a multistage energy transfer co-generation system (MWCNTs-covered thermoelectric module with aerogel and cooler, AC-CTEM) combining power generation and evaporative cooling. On the light-absorbing surface, the hot side of a thermoelectric module is covered with a hydrophobic coating made of PDMS and MWCNT. The cold side transfers heat to the evaporation zone using a heat sink. Aerogel evaporators are cross-linked with chitosan and polyurethane, which reduces the enthalpy of evaporation and facilitates efficient interfacial evaporation to remove heat and return it to refrigeration. Additionally, with the addition of Fresnel lenses and wind energy to the enhancement device, the system achieved an evaporation rate of 3.445 kg m-2 h-1 and an open-circuit voltage of 201.12 mV under 1 kW m-2 solar irradiation. The AC-CTEM system also demonstrated long-term stability and effectiveness in treating various types of non-potable water. Furthermore, we demonstrated the practical utility of the system by successfully cultivating grass seeds and powering electronic equipment. The AC-CTEM system exemplifies a practical energy-saving approach for the development of highly efficient co-generation systems.

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In order to generate clean electricity from thermal energy, a hybrid electrochemical system is conceptually developed by coupling the proton exchange membrane fuel cell (PEMFC) and solid oxide electrolyzer cell (SOEC). For evaluating the proposed hybrid system, firstly, the two subsystems are modeled numerically and then they are merged into an integrated SOEC-PEMFC system. Moreover, the SOEC-PEMFC is analytically modeled for further evaluation. The effects of important operational parameters are examined. The outcomes show that when the SOEC operating temperature increases from 823 to 1273 K, the efficiency increases from 18.7 % to 38 % and the net output power improves about 36 % while cost per unit of power of hybrid system decreases about 80 %. Furthermore, by increasing the PEMFC operating temperature from 323 to 348 K, the system net output power and efficiency increase about 16.7 % and 10 %, respectively, whilst the cost per unit of electricity decreases about 19 %. In addition by increasing operating pressure of system, the net output power and efficiency are also improved. The proposed system has maximum output power density of 3.9 kW.m-2 and maximum efficiency of 38 %. In addition, the SOEC-PEMFC system is compared with the previously studied proton exchange membrane electrolyzer cell-proton exchange membrane fuel cell (PEMEC-PEMFC) system. In comparison with the previous PEMEC-PEMFC system, the present system's cost per unit of power and efficiency are about 16 % and 17 % higher, respectively; while the output power density is about double that of the PEMEC-PEMFC system. Generally, because hydrogen-powered systems offer reliable operation from an economic and energetic perspective, the SOEC-PEMFC system represents a promising technological solution to the clean energy demands.

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The growing interest in wind power technology is motivating researchers and decision-makers to focus on maximizing wind energy extraction and enhancing the quality of power integrated into the grid. Over the past decades, significant advancements have been made in Wind Energy Conversion Systems (WECS), such as moving to variable speed wind turbines (VSWT), using various generator types, and interfacing with many power electronic converter topologies. Recently, the majority of wind turbine industries have adopted the VSWT, which is based on the permanent magnet synchronous generator (PMSG) and incorporates a fully controlled power electronic converter (FCPEC) topology due to its notable features of full controllability, ultimately enhancing the efficiency and power quality of the WECS. This paper presents a concise overview of the PMSG-VSWT system and comprehensively reviews the most recent control approaches developed for the FCPEC that play a crucial role in the operation and performance of the PMSG-VSWT system. The paper begins with a comprehensive review of the Maximum Power Extraction Algorithms (MPEA) used in the PMSG-VSWT system, as reported in esteemed research articles over recent years. It investigates the fundamental concepts of each MPEA, examining their advantages and disadvantages, providing critical comparisons, highlighting related work, and discussing the advancements achieved in this field. Subsequently, the paper reviews the prevalent control schemes for the Grid-Side Inverter and Machine-Side Rectifier (GSI/MSR) in the FCPEC. It covers common control approaches such as vector control, direct control, sliding mode control, and model productive control, including modern and intelligent techniques. Additionally, the paper details recent improvements and approaches adopted to address challenges in these common schemes, involving optimizing algorithms and adaptive techniques. The paper provides essential insights into trends, improvements, and challenges in the domain and acts as a crucial reference for researchers working with PMSG-VSWT systems.

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Dual-mode humidity sensors have received wide attention in recent years due to their great potential in multifunction applications. Herein, following a "two birds with one stone" strategy, a dual-mode and self-powered low humidity sensor based on LiBr-MOF-801 with high response and power generation is proposed. The optimized LiBr-MOF-801-based sensor exhibits impedance-voltage dual-mode sensitivity in the low humidity range of 0-23% relative humidity (RH) with high response (57.1 and 0.61 V), small hysteresis (0.3% RH) and good long-term stability at room temperature (20 °C). Moreover, an integrated humidity power generator is obtained by series connection of the self-powered humidity sensor within 15 cm2, and the output voltage reaches 2.6 V with an output power density of 110 nW cm-2, and can be used as energy, supplying power to commercial electronic equipment even in low humidity. This work provides a new sight for fabricating high-performance, dual-mode, and self-powered low-humidity sensors.

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As the most common energy source of spacecraft, photovoltaic (PV) power generation has become one of the hottest research fields. During the on-orbit operation of spacecraft, the influence of various uncertain factors and the unbalanced inertial force will make the solar PV wing vibrate and degrade its performance. In this study, we investigated the influence of mechanical vibration on the output characteristics of PV array systems. Specifically, we focused on a three-segment solar panel commonly found on satellites, analyzing both its dynamic response and electrical output characteristics under mechanical vibration using numerical simulation software. The correctness of the simulation model was partly confirmed by experiments. The results showed that the maximum output power of the selected solar panel was reduced by 5.53% and its fill factor exhibited a decline from the original value of 0.8031 to 0.7587, provided that the external load applied on the panel increased to 10 N/m2, i.e., the vibration frequency and the maximal deflection angle were 0.3754 Hz and 74.9871°, respectively. These findings highlight a significant decrease in the overall energy conversion efficiency of the solar panel when operating under vibration conditions.

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Nanostructured lead telluride PbTe is among the best-performing thermoelectric materials, for both p- and n-types, for intermediate temperature applications. However, the fabrication of power-generating modules based on nanostructured PbTe still faces challenges related to the stability of the materials, especially nanoprecipitates, and the bonding of electric contacts. In this study, in situ high-temperature transmission electron microscopy observation confirmed the stability of nanoprecipitates in p-type Pb0.973Na0.02Ge0.007Te up to at least ∼786 K. Then, a new architecture for a packaged module was developed for improving durability, preventing unwanted interaction between thermoelectric materials and electrodes, and for reducing thermal stress-induced crack formation. Finite element method simulations of thermal stresses and power generation characteristics were utilized to optimize the new module architecture. Legs of nanostructured p-type Pb0.973Na0.02Ge0.007Te (maximum zT ∼ 2.2 at 795 K) and nanostructured n-type Pb0.98Ga0.02Te (maximum zT ∼ 1.5 at 748 K) were stacked with flexible Fe-foil diffusion barrier layers and Ag-foil-interconnecting electrodes forming stable interfaces between electrodes and PbTe in the packaged module. For the bare module, a maximum conversion efficiency of ∼6.8% was obtained for a temperature difference of ∼480 K. Only ∼3% reduction in output power and efficiency was found after long-term operation of the bare module for ∼740 h (∼31 days) at a hot-side temperature of ∼673 K, demonstrating good long-term stability.

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Passive daytime radiative cooling is a zero-energy consumption cooling technology, which can dissipate heat to outer space via infrared radiation. Recently, coupling radiative cooling technology and thermoelectric devices to generate electricity has attracted much attention. However, existing radiative cooling integrated thermoelectric devices still suffer from low-temperature gradient and output voltage. Here, based on the Mie scattering and internal reflection enhancing principle, an impact-inducing geometry reconstruction approach was proposed to fabricate hierarchical nanostructured cellulosic coatings with good daytime cooling performance to achieve stable electricity generation function, which can be realized by using a scalable and facile wet ball milling technology. Guided by the theoretical simulations of the finite difference time domain method (FDTD), the cellulose and TiO2 nanoparticles can assemble into spherical envelope structured coatings drying by the shear, impact, and friction interaction in the ball milling process, dramatically enhancing the Mie scattering and internal reflection of coatings. The cellulosic coatings exhibit sunlight reflectivity of 0.962 and infrared emissivity of 0.94, resulting in a daytime radiative cooling efficiency of 5.9 °C under direct sunlight. Energy Plus stimulation demonstrated 35 % cooling energy and 468.9 kWh of cooling energy can be saved annually in China. Meanwhile, this cellulosic coating-based thermoelectric device can deliver a high voltage output of 150 mV under 1 Sun due to the strong bonding and high-temperature gradient formation (30 °C), which is higher than previous reports. This study will facilitate the development of sustainable power generation device for the goal of green future.

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This study investigated the effects of different external circuit loading mode on pollutants removal and power generation in microbial fuel cells (MFC). The results indicated that MFC exhibited distinct characteristics of higher maximum power density (Pmax) (named MFC-HP) and lower Pmax (named MFC-LP). And the capacitive properties of bioanodes may affect anodic electrochemistry. Reducing external load to align with the internal resistance increased Pmax of MFC-LP by 54.47 %, without no obvious effect on MFC-HP. However, intermittent external resistance loading (IER) mitigated the biotoxic effects of sulfamethoxazole (SMX) (a persistent organic pollutant) on chemical oxygen demand (COD) and NH4+-N removal and maintained high Pmax (424.33 mW/m2) in MFC-HP. Meanwhile, IER mode enriched electrochemically active bacteria (EAB) and environmental adaptive bacteria Advenella, which may reduce antibiotic resistance genes (ARGs) accumulation. This study suggested that the external circuit control can be effective means to regulate electrochemical characteristics and pollutants removal performance of MFC.

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The combination of solid oxide fuel cells (SOFCs) and wood gasification has the potential to significantly increase renewable electricity production and decrease emissions. Depending on the quality of the wood gas, degradation processes have a significant impact on the reliability and lifetime of the SOFC. Using electrochemical impedance spectroscopy (EIS) and subsequent distribution of relaxation times (DRT) analysis, the impact on the degradation of coupling wood gasification with a commercial SOFC stack is determined in this study. The thermal behavior of the SOFC stack under various operating conditions, as well as various synthetic wood gas mixtures classified by their hydrogen-to-carbon (H/C) ratio, was assessed. The decrease in the H/C ratio from 8 to 1, observed during syngas and real wood gas operation, leads to a rightward shift in the Nyquist plots, suggesting an increase in the SOFC stack's impedance. Correlations between variations in the H/C ratio and their effects on anodic electrooxidation, ionic conduction, gas transport, and diffusion were identified using DRT analysis to interpret the EIS results. By incorporating an upstream desulfurization system and ensuring an H/C ratio greater than 2, the coupling of biomass gasification with the SOFC stack was stable to degradation issues.

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Objective: To explore the risk factors of insomnia among employees in the thermal power generation industry and the network relationships between their interactions, and to provide scientific basis for personalized interventions for high-risk groups with insomnia. Methods: In November 2022, 860 employees of a typical thermal power generation enterprise were selected as the research subjects by cluster sampling. On-site occupational health field surveys and questionnaire surveys were used to collect basic information, occupational characteristics, anxiety, depression, stress, occupational stress, and insomnia. The interaction between insomnia and occupational health psychological factors was evaluated by using structural equation model analysis and Bayesian network construction. Results: The detection rates of anxiety, depression and stress were 34.0% (292/860), 32.1% (276/860) and 18.0% (155/860), respectively. The total score of occupational stress was (445.3±49.9) points, and 160 workers (18.6%) were suspected of insomnia, and 578 workers (67.2%) had insomnia. Structural equation model analysis showed that occupational stress had a significant effect on the occurrence of insomnia in thermal power generation workers (standardized load coefficient was 0.644), and occupational health psychology had a low effect on insomnia (standardized load coefficient was 0.065). However, the Bayesian network model further analysis found that anxiety and stress were the two parent nodes of insomnia, with direct causal relationships, the arc strength was-8.607 and -15.665, respectively. The model prediction results showed that the probability of insomnia occurring was predicted to be 0 in the cases of no stress and anxiety, low stress without anxiety, and no stress with low anxiety. When high stress with low anxiety and low stress with high anxiety occurred, the predicted probability of insomnia occurring were 0.38 and 0.47, respectively. When both high stress and high anxiety occurred simultaneously, the predicted probability of insomnia occurring was 0.51. Conclusion: Bayesian network risk assessment can intuitively reveal and predict the insomnia risk of thermal power generation workers and the network interaction relationship between the risks. Anxiety and stress are the direct causal risks of insomnia, and stress is the main risk of individual insomnia of thermal power generation workers. The occurrence of insomnia can be reduced based on scientific intervention of stress conditions.

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To build a comprehensive framework for virtual power plant (VPP) development aligned with market dynamics and to devise effective strategies to foster its growth, this study undertakes several key steps. Firstly, it constructs a VPP development framework based on market conditions, to drive the evolution of new power systems and facilitating energy transformation. Secondly, through a blend of theoretical analysis and model construction, the fundamental principles of VPP are systematically elucidated, and a decision model for the VPP development framework, focusing on price demand response, is formulated. Lastly, an optimal scheduling model for the new power system is developed, with its efficacy validated across three distinct scenarios. The findings underscore the critical importance of integrating energy storage technologies, particularly pumped storage hydropower systems, for achieving balance and optimization within new power systems. Model verification reveals that the incorporation of energy storage power stations significantly enhances system stability and efficiency, particularly in addressing the volatility associated with renewable energy sources. Additionally, the analysis indicates that while the adoption of energy storage technologies may marginally increase overall power generation costs, the total power generation cost declines with the integration of battery storage and pumped storage hydropower stations. This suggests that leveraging energy storage technologies not only enhances system operational reliability but also contributes to reducing the overall cost of power production to a certain extent. In summary, this study presents an economic and environmentally sustainable scheduling model for new power systems within the context of market trading environments. By offering both theoretical insights and practical guidance, it aims to support sustainable development and energy transformation initiatives. Ultimately, the study is poised to foster the adoption of clean energy, facilitate the establishment of smart grids, and bolster the sustainable utilization of energy resources, thereby advancing environmental conservation efforts.

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The fourth energy revolution is characterized by the incorporation of renewable energy supplies into intelligent networks. As the world is shifting towards cleaner energy sources, there is a need for efficient and reliable methods to predict the output of renewable energy plants. Hybrid machine learning modified models are emerging as a promising solution for energy generation prediction. Renewable energy generation plants, such as solar, biogas, hydropower plants, wind farms, etc. are becoming increasingly popular due to their environmental benefits. However, their output can be highly variable and dependent on weather conditions, making integrating them into the existing energy grid challenging. Smart grids with artificial intelligent systems have the potential to solve this challenge by using real-time data to optimize energy production and distribution. Although by incorporating sensors, analytics, and automation, these grids can manage energy demand and supply more efficiently, reducing carbon emissions, increase energy security, and improve access to electricity in remote areas. However, this research aims to enhance the efficiency of solar power generation systems in a smart grid context using machine learning hybrid models such as Hybrid Convolutional-Recurrence Net (HCRN), Hybrid Convolutional-LSTM Net (HCLN), and Hybrid Convolutional-GRU Net (HCGRN). For this purpose, this study considers various parameters of a solar plant such as power production (MWh), irradiance or plane of array (POA), and performance ratio (PR). The HCLN model demonstrates superior accuracy with the RMSE values of 0.012027 for MWh, 0.013734 for POA and 0.003055 for PR, along with the lowest MAE values of 0.069523 for MWh, 0.082813 for POA, and 0.042815 for PR. The obtained results suggest that the proposed machine learning models can effectively enhance the efficiency of solar power generation systems by accurately predicting the required measurements.

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The influence of sulfamethoxazole (SMX) on the electrochemical activity, bacterial community, and metabolic state of anode respiring microbes was investigated in constructed-wetland-coupled microbial fuel cells (CW-MFCs). Results suggested that SMX shortened the acclimatisation period and enhanced the maximal power density of the CW-MFC at 0.1 mg/L. Cyclic voltammetry (CV) results indicated that SMX may trigger an electrocatalytic process related to an extra redox-active compound. Exposure to SMX significantly altered the bacterial communities, leading to decreased abundances of Desulfurivibrio and Pseudomonas, while increasing the contents of Rhodobacter and Anaerovorax. Furthermore, metabolites related to amino acids and nucleotide metabolism were suppressed at 10 mg/L SMX, while the related metabolites increased at 0.1 mg/L SMX. The upregulated pathway of biofilm formation indicated that the bacteria tended to form biofilms under the influence of SMX. This study provides valuable insights into the complex interactions between SMX and electrochemically active bacteria.

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This paper presents a novel, state-of-the-art predictive control architecture that addresses the computational complexity and limitations of conventional predictive control methodologies while enhancing the performance efficacy of predictive control techniques applied to three-level voltage source converters (NPC inverters). This framework's main goal is to decrease the number of filtered voltage lifespan vectors in each sector, which will increase the overall efficiency of the control system and allow for common mode voltage reduction in three-level voltage source converters. Two particular tactics are described in order to accomplish this. First, a statistical approach is presented for the proactive detection of potential voltage vectors, with an emphasis on selecting and including the vectors that are most frequently used. This method lowers the computational load by limiting the search space needed to find the best voltage vectors. Then, using statistical analysis, a plan is presented to split the sectors into two separate parts, so greatly limiting the number of voltage vectors. The goal of this improved predictive control methodology is to reduce computing demands and mitigate common mode voltage. The suggested strategy's resilience is confirmed in a range of operational scenarios using simulations and empirical evaluation. The findings indicate a pronounced enhancement in computational efficiency and a notable diminution in common mode voltage, thereby underscoring the efficacy of the proposed methodology. This increases their ability to incorporate renewable energy sources into the electrical grid.

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This paper presents a new energy-efficient space vector pulse width modulation (SVPWM) for controlling the switches of a New three-phase inverter (NTPI) for photovoltaic (PV) applications to reduce switching losses, the peak value, and the dv/dt of the common mode voltage (CMV) with fewer number of switches. The proposed system offers a reliable operation in PV energy system with less leakage current and increased efficiency because of the reduction of the CMV, the source of leakage current in PV inverter-based application. Moreover, this also optimizes the operation of electric vehicle application with lower bearing failure. The performance of the proposed system with the new SVPWM is evaluated to the existing PWM in the literature, as well as the active zero state pulse width modulation (AZSPWM) of the two-level inverter introduced under identical conditions. Experiments and MATLAB simulations have both been used in this study.

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An effective strategy for utilizing anaerobic digestates is required to promote biomass power generation. We developed an anaerobic digestate tablet using liquid dairy cattle manure derived from a small mesophilic anaerobic digester installed on a dairy farm. Anaerobic digestate tablets are intended for use in the fertilization of oligotrophic coastal seas to promote primary production. The purpose of this study was to evaluate (1) the dissolution behavior of nutrients from anaerobic digestate tablets and (2) the effect of the application of anaerobic digestate tablets on the growth of benthic microalgae using a culture experiment. Batch experiments were conducted to investigate the dissolution behavior of the nutrients. Cumulative amounts of dissolved inorganic nitrogen and phosphate in the anaerobic digestate tablet ranged from 110 to 28.9 µg g-1 after 28 days. The dissolved inorganic nitrogen in the anaerobic digestate tablet was mainly ammonium nitrogen and accounted for 92.4-96.9%, which is advantageous for the growth of microalgae. The growth curve of the benthic microalga Nitzchia longissima was monitored using f/2 medium added to the anaerobic digestate tablet. The growth of Nitzchia longissima was two orders of magnitude greater than that of the positive control. The enhanced growth of Nitzchia longissima by the anaerobic digestate tablet was considered a concomitant effect of moderate dissolution of ammonium nitrogen and high affinity for benthic microalgae. In conclusion, the anaerobic digestate tablets prepared in this study have the advantage of supplying nitrogen to benthic microalgae. This study proposes a new method for utilizing anaerobic digestates.

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The discrepancy between the operating and design capacities of solar plants in eastern Uganda is alarming; about 35 % underperformance in solar power generation is observed. The goal of the current study is to minimize this disparity by improving the design models. Considering only cell temperature in the power generation model is responsible for the observed difference in design and operational solar power generated, the present study used a thermocouple to directly measure cell temperature, an anemometer to measure wind speed, and a solar power meter to measure irradiance. These extrinsic factors were used to modify the power generation model based only on cell temperature through the direct correlation of cell temperature, wind speed, and irradiance with solar power generation. Thus, the absence of extrinsic factors (wind speed and irradiance) in the design models is responsible for the colossal drop in solar power generated. Empirically, the missing extrinsic factors were used to transform the implicit solar power model into an explicit model. The development of a solar power generation model, multiple differential models, simulation and experimentation with a pilot solar rig served as alternate model for the prediction of solar power generation. The second-order differential model validated well with empirical solar power generated in Busitema, Mayuge, Soroti, and Tororo study areas based on RMSEs (0.6437, 0.6692, 0.2008, 0.1804, respectively), thus, narrowing the gap between the designed and operational solar power generated. Mayuge and Soroti recorded the highest solar power generation of 9.028 MW compared to Busitema (8.622 MW) and Tororo (8.345 MW), suggesting that it has a conducive site for installing future solar plants. The above results support the use of empirical explicit (triple) and second-order differential models for the design and operation of power plants.

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Electrochromic smart windows can achieve controllable modulation of color and transmittance under an external electric field with active light and thermal control capabilities, which helps reduce energy consumption caused by building cooling and heating. However, electrochromic smart windows often rely on external power circuits, which greatly affects the independence and portability of smart windows. Based on this, an electrochromic smart window driven by temperature-difference power generation was designed and implemented. This smart window provides automatic and manual control of the reversible cycle of electrochromic glass from light blue to dark blue according to user requirements and changes in the surrounding environment, achieving adaptive adjustment of visual comfort and reducing energy consumption. The infrared radiation rejection (from 780 to 2500 nm) of the electrochromic smart window is as high as 77.3%, and its transmittance (from 380 to 780 nm) fluctuates between 39.2% and 56.4% with changes in working state. Furthermore, the temperature in the indoor simulation device with electrochromic glass as the window was 15 °C lower than that with ordinary glass as the window after heating with a 250 W Philips infrared lamp for ten minutes. After 2000 cycles of testing, the performance of the smart window was basically maintained at its initial values, and it has broad application prospects in buildings, vehicles, and high-speed rail systems.