Next Energy最新文献

The performance of commercial solar power plants degrades due to an increase in module temperatures for which standard PV-T air or water-cooling techniques are mostly used. In this study, a thermoelectric cooling system is studied for improving photovoltaic cell power efficiency and hence solar power generation. The cooling optimization requires solar cell temperature prediction of field operating PV modules, for which analysis of six models, is presented. The experimentation results show that TEC cooling maintains PV cell at 25 °C whereas PV cell without TEC operates at 55–63 °C, a higher temperature range, showing the effectiveness of the thermoelectric cooling system in precisely controlling PV cell temperature to operate at or near STC conditions in the field creating a temperature difference of 30–38 °C. The NOCT and Faiman model results are found close to the experimental values in comparison to other models. The potential for cooling and a corresponding increase in solar plant energy production is assessed using PV Syst modeling and simulation for three practical PV installation scenarios for 31 different climatic zone locations worldwide showing 6–27 % power loss due to elevated temperatures, which is not studied in previous studies adding novelty to the analysis. The results show that PV-TECS is an effective system to control the temperature of field operating PV modules, which can be used in future photovoltaic power plants. Field results and analysis of PV temperature models is crucial for the optimization and future development of PV-thermoelectric systems deployed under actual outdoor conditions as well as the expected cooling gains in different climatic locations. These aspects are collectively studied in the current work adding to the novelty of the study.

The increasing demand for clean energy and the urgent need to reduce greenhouse gas emissions have led to a growing awareness of the importance of thermal energy storage systems in the diversification of the electric matrix. Sensible heat and latent heat storage are the two main techniques, and the choice of storage system depends on the melting point of the substances and the thermal energy required for the processes. This study focused on waste salts obtained from the production process of lithium carbonate. The thermophysical properties of these salts, including specific heat, density, and thermal stability, were examined through various characterization techniques such as X-ray diffraction, chemical analysis, scanning electron microscopy, thermogravimetry, and differential scanning calorimetry. The results revealed promising thermal properties, chemical stability, and physical availability of the waste salts. Additionally, the study explored the potential benefits of reutilizing these waste salts, such as reducing environmental impact, promoting circular economy principles, and creating new market opportunities for commercial products. Overall, this research provides valuable insights into the thermophysical properties of waste salts from lithium carbonate production. The main results are heat capacity in the solid phase (0.767–3.143 J/g °C) and storable thermal energy (114–1153 TWh_t). These findings contribute to the design and optimization of thermal energy storage systems, highlighting the potential for sustainable and efficient energy storage solutions in the context of global clean energy transitions.

Increasing penetration of variable renewable energy, particularly wind power, necessitates improved grid integration strategies. One option that has received little attention to date is to adapt the design of the turbines in the direction of lower rotor power density. By simulating various turbine models with different specifications at selected locations, the effects on several performance indicators are investigated. Since no single suitable performance indicator exists, a comparison of widely used indicators is made, whereby the introduction of new parameters proves to be useful. Results show that lowering rotor power density through reduced generator output significantly mitigates power fluctuations in the 10-minute range. Considering an entire wind farm instead of an individual turbine, total annual production is slightly decreased, while the required connected load to the grid is substantially reduced.

Lithium-ion batteries (LIBs) is now a cornerstone technology to curb carbon emission by enabling electric vehicles and grid-scale energy storage. However, LIBs are highly materials-intensive, the cost and availability of the key materials, especially the lithium-containing cathode materials, are critical for the goal of decarbonization. High-voltage spinel LiNi_0.5Mn_1.5O₄ (LNMO) is a promising cobalt-free cathode material to cater to the surging demand for low-cost and high-energy-density LIBs. In this paper, the advantages of LNMO are quantified in terms of performance and sustainability, then the growing interest in the research and development (R&D) of LNMO is assessed by analyzing 559 related patents registered across 22 authorities. The analysis paints a comprehensive picture, including geographical distribution of patenting activities, major developers, and influential patents. Furthermore, the patents are categorized into four key innovation directions. A gradual shift from materials engineering to electrolyte design indicates that the development of novel high-voltage compatible electrolytes is expected to unlock LNMO for next-generation, sustainable, and high-performance batteries.

Solid-state electrolytes have been demonstrated immense potential with their high density and safety for Li, Na batteries. The discovery of novel crystals is of fundamental scientific and technological interest in solid-state chemistry. The discovery, synthesis and application of energetically favourable solid-state electrolytes has been bottlenecked by expensive trial-and-error approaches. Machine learning has brought breakthroughs to solid-state electrolytes. Numerous solid-state electrolyte candidates have been screened by different models at multiscale, i.e., interatomic potentials, molecular dynamics, ionic conductivity. Machine learning method also accelerate the synthesis prediction, mechanism discovery and interface design. This review would answer the question what can be done for solid-state electrolytes by machine learning, including descriptor, model, algorithm etc. This paper will promote fast integration between scientists in materials, software, computing discipline.

Utilizing a macro-scale energy model alongside 44 years of New England’s hourly weather data from MERRA-2, this study investigates the benefits of various combinations of renewable energy technologies, particularly wind and solar, and their integration with long-duration energy storage (LDES) systems. The potential of these renewable energy sources to enhance both the reliability and affordability of New England’s power system is evaluated. The findings demonstrate that incorporating LDES into the technology mix leads to a remarkable 55% reduction in overall system costs compared to relying solely on short-duration energy storage (SDES), such as lithium-ion batteries. Additionally, the cost reductions of LDES lead to approximately a 35% increase in the utilization of solar generation. The results suggest that total system costs for LDES are more responsive to relative decreases in power-capacity costs than decreases in energy-capacity costs. Furthermore, the analysis indicates that a combination of different LDES storage technologies can offer the most cost-effective solution for wind-solar power systems. Overall, by examining the interplay between different storage technologies and their impact on system costs, this research contributes significantly to the discourse on sustainable energy solutions.

A parameterized mathematical model for Lithium-ion battery cell is presented in this paper for performance analysis with a particular focus on battery discharge behavior and electrochemical impedance spectroscopy profile. The model utilizes various physical properties as input and consists of two major sub-models in a complementary manner. The first sub-model is an adapted Doyle-Fuller-Newman (DFN) framework to simulate electrochemical, thermodynamic, and transport phenomena within the battery. The second sub-model is a calibrated solid-electrolyte interphase (SEI) layer formation model. This model emphasizes the electrical dynamic response in terms of the reaction process, layer growth, and conductance change. The equivalent circuit component values are derived from the outputs of both sub-models, reflecting the battery’s changing physical parameters. The simulated discharge curves and electrochemical impedance spectroscopy (EIS) profiles are then provided with a comparison against empirical results for validation, which exhibit good agreement. This modeling methodology aims to bridge the gap between the physical model and the equivalent circuit model (ECM), enabling more accurate battery performance predictions and operation status tracking.

The Paris Agreement's ambitious target of limiting the global temperature rise to 1.5 °C remains elusive, with the world falling short of its climate commitments. The electricity sector, responsible for a substantial 39% of global carbon emissions, plays a pivotal role in curbing climate change. Transitioning to renewable energy sources is not just an environmental imperative but also a pressing economic challenge, especially for governments tasked with financing renewable projects. This study explores the economic dimensions of decarbonizing electricity grids, shedding light on the costs and benefits. This research quantifies the costs of current fossil fuel-based electricity production versus achieving net-zero emissions across ten Latin American countries by evaluating annual electricity generation costs and estimating the expenses of transitioning to renewables. We find that decarbonizing these countries’ electricity grids would lead to annual savings close to USD 21 billion annually, potentially contributing positively to their economies. The study also compares the energy policies for each of the ten Latin American nations included, evaluating the role of renewable-oriented policies in decarbonizing power generation. We also find that policies vary in effectiveness across countries, and have to be in line with geographical constraints and economic realities.

This study explores the utilization of Delonix regia (DR) (Gulmohar flower) as a natural photosensitizer in the fabrication of self-powered photosensors via dye-sensitized solar cells (DSSCs). Various photoanodes, including ZnO, ZnO/TiO₂, and ZnO/TiO₂/Nb₂O₅, were investigated for their performance enhancement in DR-based photosensors. The photoanodes were fabricated using different deposition methods sensitized with DR photosensitizer and characterized through X-ray diffraction analysis, Field emission scanning electron microscopy (FE-SEM), UV–visible, Fluorescence spectroscopy, and Fourier transform infrared spectroscopy (FTIR) analysis. The absorbance spectrum results indicate improved photosensitizer adsorption in visible light for the ZnO/TiO₂/Nb₂O₅/DR photoanode compared to other configurations. The scanning electron microscopy (SEM) cross-section image of ZnO/TiO₂/Nb₂O₅ confirmed the formation of a multilayer photoanode. Chronoamperometry experiments were conducted to evaluate the photo sensing behavior, focusing on sensitivity, photoresponsivity, specific detectivity, and quantum efficiency within the solar spectrum range. Among the devices, ZnO/TiO₂/Nb₂O₅ exhibited the highest photoresponsivity (1.2 × 10 ⁻³ A/W) and photodetectivity (26.78 × 10⁴ Jones), demonstrating promising advancements in DR-sensitized photosensors. This research highlights the exceptional performance of DR-sensitized photoanodes, particularly ZnO/TiO₂/Nb₂O₅, in advancing the capabilities of photo-sensing devices. The investigated configuration exhibited a rapid response speed of 723 ms and a remarkable sensitivity of 89.87% in detecting photo signals. These findings emphasize the potential of DR photosensitizers to enhance photo-sensing activities, with significant implications for diverse applications in photodetection. By leveraging the unique properties of DR, particularly its incorporation into ZnO/TiO₂/Nb₂O₅, this study underscores the promising prospects for developing efficient self-powered photosensors. The insights gleaned from this work pave the way for further exploration and optimization in organic photosensors.

Zero-dimensional metal halide perovskite quantum dots (PQDs) display distinct chemical, physical, electrical, and optical properties compared to their bulk counterparts. These unique characteristics make PQDs highly promising materials for a broad range of applications spanning solar cells and light-emitting diodes (LEDs), to lasers and quantum technologies. Despite the recent advances, the translations of PQDs into commercially viable materials are hindered by several drawbacks; for example, an unclear understanding of their formation mechanism(s), the complex chemistry and dynamic instabilities at the PQDs surface, and the inefficient or unbalanced charge transportation in PQDs-based devices. In this review, we present an in-depth analysis of the current progress and challenges in the field of PQDs and their applications. Additionally, we offer insights into potential future research directions in this exciting area of study.