Cleaner Energy Systems最新文献_第4页

Optimizing textile dyeing and finishing for improved energy efficiency and sustainability in fleece knitted fabrics 优化纺织染整，提高羊毛针织物的能效和可持续性

Cleaner Energy Systems

Pub Date : 2024-10-10 DOI: 10.1016/j.cles.2024.100154

Miraduzzaman Chowdhury , Mohammad Shohag Babu , Shahadat Hossain , Rony Mia , Shekh Md. Mamun Kabir

In the industrial range, optimizing dyeing and finishing energy is important to control environmental pollution. In the Dyeing stage to finishing of textiles gas, electricity, steam, and water are used 260 m³/hour, 591 kWh, 1.2 pounds/hour, and 8.69 tons/hour respectively. If textile professionals do not match the desired shade and quality of fabrics with the use of minimal resources the energy cost will be multiple times higher. This study investigates the change in the shade of fleece knitted fabrics from the dyeing unload to the finish stage and assumes a dyeing recipe adjustment, focusing on the impact of optimized dyeing and finishing processes. Also, it focuses on qualitative changes in properties across various color variations. Identical dyeing recipes for light, medium, and dark shades of red, blue, and navy. Properties such as GSM (grams per square meter), width, color strength, shade (darker/lighter, red/green, blue/yellow), shrinkage, spirality, pilling, bursting strength, and color fastness were analyzed. Dyeing to post-finishing, an increase in color strength (K/S) values was observed, with examples including minimum increases from 2.9 to 3.18 for light red and maximum from 19.3 to 22.9 for dark navy shade. Darker shades (DL*) were observed after stenter 1st pass (among all variants, red: 1.2 % to 8.1 %, blue: 4.5 % to 6.7 %, navy: 1.6 % to 2 %), while lighter shades (DL*) were observed following sueding and napping (among all variants, red: 3.1 % to 19.7 %, blue: 11.8 % to 19.7 %, navy: 14.8 % to 27.6 %). Greenish (Da*) and yellowish (Db*) tones are prominent across all colors in the finishing stages. Besides, other properties shrinkage, spirality, pilling, bursting strength, and color fastness significantly changed. These findings offer valuable guidance for dyeing professionals aiming to achieve the desired adjustment of shades that match the quality standard and produce sustainable fleece fabrics. To compensate for the shade lightening that occurs during the finishing process, it is recommended to keep the fabric shade slightly darker (5.70 % to 23.10 %) at the dyeing stage.

在工业范围内，优化染整能源对控制环境污染非常重要。在染色阶段到纺织品整理阶段，燃气、电、蒸汽和水的使用量分别为 260 立方米/小时、591 千瓦时、1.2 磅/小时和 8.69 吨/小时。如果纺织专业人员不能在使用最少资源的情况下获得理想的织物色调和质量，能源成本将成倍增加。本研究调查了羊毛针织物从染色卸载到后整理阶段的色调变化，并假设了染色配方调整，重点关注优化染色和后整理工艺的影响。此外，它还关注各种颜色变化的特性质变。红色、蓝色和藏青色的浅色、中色和深色的染色配方完全相同。对 GSM（每平方米克重）、幅宽、色强、色调（深/浅、红/绿、蓝/黄）、收缩率、螺旋度、起球、爆破强力和色牢度等特性进行了分析。从染色到后整理，色牢度（K/S）值都有所提高，例如浅红色的色牢度从 2.9 提高到 3.18，深海军蓝的色牢度从 19.3 提高到 22.9。在拉幅机第一道拉幅后，可以观察到较深的色调（DL*）（在所有变体中，红色：1.2 % 至 8.1 %，深蓝色：1.2 % 至 8.1 %）：红色：1.2 % 至 8.1 %，蓝色：4.5 % 至 6.7 %：在所有变种中，红色：1.2 % 至 8.1 %，蓝色：4.5 % 至 6.7 %，藏青色：1.6 % 至 2 %），而在播种和打盹后观察到较浅的色调（DL*）：在所有变体中，红色：3.1 % 至 19.7 %，蓝色：11.8 % 至 19.7 %：在所有变体中，红色：3.1 % 至 19.7 %，蓝色：11.8 % 至 19.7 %，深蓝色：14.8 % 至 27.6 %）。在后整理阶段，偏绿（Da*）和偏黄（Db*）的色调在所有颜色中都很突出。此外，其他性能收缩率、螺旋度、起毛起球、爆破强力和色牢度也发生了显著变化。这些发现为染色专业人员提供了宝贵的指导，使他们能够实现符合质量标准的理想色调调整，并生产出可持续发展的羊毛织物。为了补偿整理过程中出现的色调变浅，建议在染色阶段保持织物色调稍深（5.70 % 至 23.10 %）。

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引用次数: 0

Exploring the substitution within clean energy: Evidence from China's top 14 hydropower provinces 探索清洁能源的替代性：来自中国 14 个水电大省的证据

Cleaner Energy Systems

Pub Date : 2024-10-01 DOI: 10.1016/j.cles.2024.100152

Yubao Wang, Huiyuan Pan, Junjie Zhen, Boyang Xu

This paper quantitatively examines the substitution effects within China's clean energy sector, focusing on the hydropower and new energy generation sectors across the top 14 hydropower-producing provinces, which collectively contribute to over 80 % of the country's total hydropower output. To provide a comprehensive analysis of regions that significantly influence national trends, the study utilizes the Cross-Price Elasticity (CPE) and Morishima Elasticity of Substitution (MES). CPE measures how the quantity demanded of one energy source responds to a change in the price of another, while MES assesses the sensitivity of the ratio between two energy inputs to price changes. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model is employed to forecast energy substitution dynamics, offering robust predictive accuracy. The average MES between clean energy and thermal power is 0.663, indicating a moderate substitution relationship, with the effect more pronounced in summer. Additionally, the mean MES between hydropower and new energy generation is 2.067, reflecting a strong substitution effect between these two clean energy forms. Furthermore, the SARIMA model shows a mean squared error (MSE) as low as 0.0006 in some cases, demonstrating its robust predictive accuracy in forecasting energy substitution dynamics. These results offer empirical support for policies aimed at reducing reliance on thermal power and promoting clean energy development in key provinces.

本文定量研究了中国清洁能源行业的替代效应，重点关注水电产量最高的 14 个省份的水电和新能源发电行业，这 14 个省份的水电产量合计占全国水电总产量的 80% 以上。为了全面分析对全国趋势有重大影响的地区，研究采用了交叉价格弹性 (CPE) 和森岛替代弹性 (MES)。CPE 衡量一种能源的需求量如何对另一种能源的价格变化做出反应，而 MES 则评估两种能源投入之间的比率对价格变化的敏感性。采用季节自回归综合移动平均（SARIMA）模型来预测能源替代动态，具有很高的预测准确性。清洁能源与火力发电之间的平均 MES 为 0.663，表明两者之间存在适度的替代关系，夏季的替代效应更为明显。此外，水力发电与新能源发电之间的平均 MES 为 2.067，反映出这两种清洁能源形式之间存在较强的替代效应。此外，SARIMA 模型在某些情况下的均方误差（MSE）低至 0.0006，这表明该模型在预测能源替代动态方面具有很强的预测准确性。这些结果为重点省份减少对火电的依赖、促进清洁能源发展的政策提供了经验支持。

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引用次数: 0

Battery remaining useful life estimation based on particle swarm optimization-neural network 基于粒子群优化-神经网络的电池剩余使用寿命估算

Cleaner Energy Systems

Pub Date : 2024-09-29 DOI: 10.1016/j.cles.2024.100151

Zuriani Mustaffa , Mohd Herwan Sulaiman

Determining the Remaining Useful Life (RUL) of a battery is essential for several purposes, including proactive maintenance planning, optimizing resource allocation, preventing unforeseen failures, improving safety, extending battery lifespan, and achieving accurate cost savings. Concerning that matter, this study proposed hybrid Particle Swarm Optimization–Neural Network (PSONN) for estimating battery RUL. In the evaluation of the proposed method, the effectiveness is assessed using the metrics of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The dataset employed for this investigation comprises eight input parameters and one output variable, representing the battery RUL. In conducting an analysis, the performance of the PSONN model is compared with hybrid NN with Cultural Algorithm (CA-NN) and Harmony Search Algorithm (HSA-NN), as well as the standalone Autoregressive Integrated Moving Average (ARIMA). Upon examination of the findings, it becomes evident that the PSONN model outperforms the alternatives with an MAE of 2.7708 and an RMSE of 4.3468, significantly lower than HSA-NN (MAE: 22.0583, RMSE: 34.5154), CA-NN (MAE: 9.1189, RMSE: 22.4646), and ARIMA (MAE: 494.6275, RMSE: 584.3098). The PSONN also achieves the lowest maximum error of 104.7381 compared to 490.3125 for HSA-NN, 827.0163 for CA-NN, and 1,160.0000 for ARIMA. Additionally, the low two-tail probability values (P(T ≤ t)), all below the significance level of 0.05, indicate that the differences between PSONN and the other methods (HSA-NN, CA-NN, and ARIMA) are statistically significant. These results highlight the superior accuracy and robustness of the PSONN model in predicting battery RUL. This study contributes to the field by presenting the PSONN as a highly effective tool for accurate battery RUL estimation, as evidenced by its superior performance over alternative methods.

确定电池的剩余使用寿命（RUL）对于多个目的而言都至关重要，包括主动维护计划、优化资源分配、预防意外故障、提高安全性、延长电池寿命以及实现精确的成本节约。为此，本研究提出了粒子群优化-神经网络（PSONN）混合方法来估算电池的有效使用时间。在评估所提出方法的有效性时，使用了平均绝对误差（MAE）和均方根误差（RMSE）指标。本次研究采用的数据集包括八个输入参数和一个输出变量，代表电池 RUL。在进行分析时，将 PSONN 模型的性能与带有文化算法（CA-NN）和和谐搜索算法（HSA-NN）的混合 NN 以及独立的自回归综合移动平均法（ARIMA）进行了比较。研究结果表明，PSONN 模型的 MAE 为 2.7708，RMSE 为 4.3468，明显低于 HSA-NN（MAE：22.0583，RMSE：34.5154）、CA-NN（MAE：9.1189，RMSE：22.4646）和 ARIMA（MAE：494.6275，RMSE：584.3098）。PSONN 的最大误差也最小，为 104.7381，而 HSA-NN 为 490.3125，CA-NN 为 827.0163，ARIMA 为 1,160.0000。此外，PSONN 与其他方法（HSA-NN、CA-NN 和 ARIMA）的双尾概率值（P(T ≤ t)）均低于 0.05 的显著性水平，这表明 PSONN 与其他方法（HSA-NN、CA-NN 和 ARIMA）之间的差异具有显著的统计学意义。这些结果凸显了 PSONN 模型在预测电池 RUL 方面卓越的准确性和稳健性。与其他方法相比，PSONN 的性能更优越，这表明 PSONN 是准确估算电池 RUL 的高效工具，从而为该领域的研究做出了贡献。

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引用次数: 0

Wind power forecasting with metaheuristic-based feature selection and neural networks 利用基于元搜索的特征选择和神经网络进行风能预测

Cleaner Energy Systems

Pub Date : 2024-09-29 DOI: 10.1016/j.cles.2024.100149

Mohd Herwan Sulaiman , Zuriani Mustaffa , Mohd Mawardi Saari , Mohammad Fadhil Abas

Accurate forecasting of wind power generation is crucial for ensuring a stable and efficient energy supply, reducing the environmental impact of energy production, and promoting a cleaner and more sustainable energy supply. Inaccurate forecasts can lead to a mismatch between wind power generation and energy demand, resulting in wasted energy, increased emissions, and reduced grid stability. Therefore, improving the accuracy of wind power generation forecasting is essential for optimizing energy storage and grid management, reducing the reliance on fossil fuels, decreasing greenhouse gas emissions, and promoting a more sustainable energy future. This study proposes an innovative approach to enhance wind power generation forecasting accuracy by leveraging the strengths of metaheuristic algorithms for feature selection and integrating them with Neural Networks (NN). Specifically, five distinct algorithms - Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Teaching-Learning-Based Optimization (TLBO), and Evolutionary Mating Algorithm (EMA) - are integrated with NN model to identify optimal feature subsets from a comprehensive dataset of 18 diverse features. The results show that the GA consistently outperforms other algorithms in selecting the most influential features, leading to improved precision in wind power predictions. Notably, the GA achieves the best root mean square error (RMSE) of 37.1837 and the best mean absolute error (MAE) of 18.6313, outperforming the other algorithms and demonstrating the importance of feature selection in improving the accuracy of wind power forecasting. This innovative framework advances the field of renewable energy forecasting and provides valuable insights into optimizing feature sets for improved predictions across diverse domains.

准确预测风力发电量对于确保稳定高效的能源供应、减少能源生产对环境的影响以及促进更清洁、更可持续的能源供应至关重要。不准确的预测会导致风力发电与能源需求不匹配，造成能源浪费、排放增加和电网稳定性降低。因此，提高风力发电预测的准确性对于优化能源存储和电网管理、减少对化石燃料的依赖、减少温室气体排放以及促进更可持续的能源未来至关重要。本研究提出了一种创新方法，利用元启发式算法的优势进行特征选择，并将其与神经网络（NN）相结合，从而提高风力发电预测的准确性。具体来说，五种不同的算法--遗传算法（GA）、粒子群优化（PSO）、蚁群优化（ACO）、基于教学学习的优化（TLBO）和进化交配算法（EMA）--与神经网络模型相结合，从包含 18 种不同特征的综合数据集中识别出最佳特征子集。结果表明，在选择最有影响力的特征方面，GA 始终优于其他算法，从而提高了风能预测的精度。值得注意的是，GA 的最佳均方根误差 (RMSE) 为 37.1837，最佳平均绝对误差 (MAE) 为 18.6313，优于其他算法，证明了特征选择在提高风力预测精度方面的重要性。这一创新框架推动了可再生能源预测领域的发展，并为优化特征集以改进不同领域的预测提供了宝贵的见解。

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引用次数: 0

Integrated rooftop solar PV-based residential advanced energy management system: An economic involvement of energy systems for prosumers 基于屋顶太阳能光伏发电的一体化住宅高级能源管理系统：能源系统对专业消费者的经济影响

Cleaner Energy Systems

Pub Date : 2024-09-29 DOI: 10.1016/j.cles.2024.100150

Abu Shufian , Shaikh Anowarul Fattah

The growing adoption of rooftop solar photovoltaic (PV) systems, coupled with the ability to sell surplus energy back to the national grid, presents a promising opportunity for residential energy management. This research introduces an innovative Advanced Energy Management System (AEMS) that integrates rooftop solar PV with energy-efficient appliances, offering a transformative approach to optimizing household energy consumption. By leveraging advanced demand-side management (DSM) techniques, the AEMS enables users to strategically shift energy usage away from peak hours, thereby reducing reliance on grid energy and minimizing costs. Empirical evaluations reveal that the AEMS significantly outperforms conventional energy management systems, achieving cost reductions of 28.59–35.48 %. The user-friendly interface and robust optimization strategies of the proposed model ensure operational efficiency, making it a valuable tool for maximizing energy savings and enhancing grid stability. Focusing on the specific context of Bangladesh, this study provides a comprehensive techno-economic analysis, demonstrating the practical applicability and long-term sustainability of suggested AEMS. The findings underscore the potential of the proposed model to revolutionize residential energy management, positioning it as a key enabler of both economic and environmental benefits for prosumers in emerging markets.

屋顶太阳能光伏（PV）系统的应用日益广泛，再加上可以将剩余能源卖回国家电网，这为住宅能源管理带来了大有可为的机遇。这项研究介绍了一种创新的先进能源管理系统（AEMS），它将屋顶太阳能光伏发电与节能电器集成在一起，为优化家庭能源消耗提供了一种变革性的方法。通过利用先进的需求方管理（DSM）技术，AEMS 使用户能够战略性地将能源使用从高峰时段转移到其他时段，从而减少对电网能源的依赖并最大限度地降低成本。实证评估显示，AEMS 的性能明显优于传统能源管理系统，可降低 28.59%-35.48% 的成本。拟议模型的用户友好界面和强大的优化策略确保了运行效率，使其成为最大限度节约能源和提高电网稳定性的重要工具。本研究以孟加拉国的具体情况为重点，提供了全面的技术经济分析，证明了所建议的 AEMS 系统的实际适用性和长期可持续性。研究结果强调了所建议的模式在彻底改变住宅能源管理方面的潜力，并将其定位为新兴市场中为消费者带来经济和环境效益的关键因素。

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引用次数: 0

Operational greenhouse gas emissions of various energy carriers for building heating 用于建筑供暖的各种能源载体的运行温室气体排放量

Cleaner Energy Systems

Pub Date : 2024-09-26 DOI: 10.1016/j.cles.2024.100148

Jordi F.P. Cornette, Julien Blondeau

The decarbonisation of the building heating sector requires a shift from decentralised fossil fuel heating appliances to systems converting energy carriers with low greenhouse gas (GHG) emissions. However, for certain energy carriers, a considerable portion of GHG emissions arises upstream during production, processing and transportation, rather than during energy conversion. Accurately quantifying these indirect GHG emissions typically requires life cycle assessments, which are often resource-intensive and impractical during the early stages of energy system design. This study introduces operational GHG emissions as a pragmatic metric for the preliminary assessment of energy carrier environmental impact in building heating applications. These operational GHG emissions include both direct CO

_{2}

emissions and indirect CO

_{2}

, CH

_{4}

and N

_{2}

O emissions. Based on a comprehensive literature analysis, average estimates are proposed for the operational GHG emissions of various energy carriers within a European context, including natural gas, oil, coal and wood, as well as the average European and Belgian electricity grid, and hydrogen from various production methods. The findings underscore the significant contribution of indirect GHG emissions, as the selection of the energy carrier with the lowest environmental impact hinges on whether direct emissions alone or the broader operational GHG emissions are considered. By integrating operational GHG emissions into the early design stages of energy systems, stakeholders can make more informed decisions about which energy systems warrant further investigation, thereby facilitating more sustainable energy system development from the outset.

建筑供暖领域的去碳化要求从分散的化石燃料供暖设备转向温室气体（GHG）排放量低的能源载体转换系统。然而，对于某些能源载体而言，相当一部分温室气体排放产生于上游的生产、加工和运输过程中，而非能源转换过程中。要准确量化这些间接的温室气体排放，通常需要进行生命周期评估，而在能源系统设计的早期阶段，这种评估往往需要大量资源，而且不切实际。本研究引入了运行过程中的温室气体排放量，作为初步评估建筑供热应用中能源载体环境影响的实用指标。这些运行温室气体排放包括直接二氧化碳排放和间接二氧化碳、甲烷和氧化亚氮排放。根据全面的文献分析，提出了欧洲范围内各种能源载体的运行温室气体排放量的平均估计值，包括天然气、石油、煤炭和木材，以及欧洲和比利时的平均电网和各种生产方法产生的氢气。研究结果强调了间接温室气体排放的重要作用，因为选择对环境影响最小的能源载体取决于考虑的是直接排放还是更广泛的运行温室气体排放。通过将运行过程中的温室气体排放纳入能源系统的早期设计阶段，利益相关者可以就哪些能源系统值得进一步研究做出更明智的决定，从而从一开始就促进能源系统的可持续发展。

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引用次数: 0

Optimization of a proposed biomass generator: Harnessing citizen waste with electric vehicle charging infrastructure 优化拟议的生物质发电机：利用电动汽车充电基础设施处理市民垃圾

Cleaner Energy Systems

Pub Date : 2024-09-16 DOI: 10.1016/j.cles.2024.100146

Akib Chowdhury , Nusrat Chowdhury , Wahiba Yaïci , Michela Longo

Global municipal solid waste production is rising, causing significant environmental, health, and economic issues. Developed countries have advanced recycling technologies, but cities like Dhaka, Bangladesh—among the most densely populated-struggle with inadequate waste management. This feasibility study aims to improve environmental protection and create new energy sources by proposing a waste management system across Dhaka, focusing on waste valorization for bioenergy with optimized efficiency and minimal impact. The study includes design and optimization of a biomass-based power plant to meet the energy needs of EV charging stations and the national grid, evaluating its economic performance through discounted cash flow and payback period analyses. The paper explores the integration of an EV charging station powered by biogas, addressing the growing need for EV infrastructure in Dhaka. By evaluating biomass generators as a greener alternative to fossil fuels, the study analyzes the technical, economic, and environmental feasibility, including CO2 emissions, using HOMER Pro.

全球城市固体废物产量不断增加，造成了严重的环境、健康和经济问题。发达国家拥有先进的回收利用技术，但像孟加拉国达卡这样人口最稠密的城市却因废物管理不善而苦苦挣扎。本可行性研究旨在通过在达卡建立一个废物管理系统来改善环境保护和创造新的能源，重点是以最优化的效率和最小化的影响将废物价值化为生物能源。该研究包括设计和优化生物质发电厂，以满足电动汽车充电站和国家电网的能源需求，并通过贴现现金流和投资回收期分析评估其经济效益。论文探讨了沼气供电电动汽车充电站的整合问题，以满足达卡对电动汽车基础设施日益增长的需求。通过评估生物质发电机作为化石燃料的绿色替代品，该研究使用 HOMER Pro 分析了技术、经济和环境可行性，包括二氧化碳排放量。

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引用次数: 0

Impact of trapezoidal ribs on the performance of solar air collector: A numerical solution with optimized rib dimensions for better performance 梯形肋条对太阳能空气集热器性能的影响：优化肋条尺寸以提高性能的数值解决方案

Cleaner Energy Systems

Pub Date : 2024-09-13 DOI: 10.1016/j.cles.2024.100147

Mosuru Hari Krishna , Shekher Sheelam , Chandramohan V․P․

In the present study, a 2D numerical analysis of the solar air collector (SAC) of an indirect solar dryer having trapezoidal corrugations on the absorber plate was performed. Corrugation pitch, p (twelve values ranged from 20 to 160 mm) and height, e (six values ranged from 1 to 10 mm) were varied and analyzed for six values of Reynolds numbers (Re). The output characteristics such as Nusselt number (Nu), friction factor (f) and thermo-hydraulic performance index (T_hp) were calculated for different p, e and Re. The total work was categorized into two parts (part-I for optimizing p and part-II for optimizing e). 18 domains (twelve for part-I and six for part-II simulations) were generated and 108 simulations were executed to find the optimum dimensions (p, e and corrugation angle, α) of the corrugation. ANSYS Fluent-v15 was used to solve the problem. The maximum Nu for the corrugated sheet was 2.663 times greater than the flat absorber plate. The maximum T_hp range was from 1.435 to 1.699 and obtained at the optimal values of p = 140 mm, e = 4 mm and α = 38.66° The numerical results were compared with the existing literature.

在本研究中，对吸收板上有梯形波纹的间接太阳能干燥器的太阳能空气集热器（SAC）进行了二维数值分析。波纹间距 p（12 个值，从 20 毫米到 160 毫米不等）和高度 e（6 个值，从 1 毫米到 10 毫米不等）发生了变化，并对 6 个雷诺数 (Re) 值进行了分析。计算了不同 p、e 和 Re 的输出特性，如努塞尔数 (Nu)、摩擦因数 (f) 和热液压性能指数 (Thp)。全部工作分为两部分（第一部分用于优化 p，第二部分用于优化 e）。生成了 18 个域（12 个用于第一部分模拟，6 个用于第二部分模拟），并执行了 108 次模拟，以找到波纹的最佳尺寸（p、e 和波纹角 α）。ANSYS Fluent-v15 用于解决问题。波纹板的最大 Nu 是平面吸收板的 2.663 倍。数值结果与现有文献进行了比较。

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引用次数: 0

Prospects and challenges of anode materials for lithium-ion batteries–A review 锂离子电池负极材料的前景与挑战--综述

Cleaner Energy Systems

Pub Date : 2024-09-12 DOI: 10.1016/j.cles.2024.100145

Md․ Helal Hossain , Md․ Aminul Islam , Mohammad Assaduzzaman Chowdhury , Nayem Hossain

This review provides a comprehensive examination of the current state and future prospects of anode materials for lithium-ion batteries (LIBs), which are critical for the ongoing advancement of energy storage technologies. The paper discusses the fundamental principles governing the operation of LIBs, with a focus on the electrochemical performance of various anode materials, including graphite, silicon, tin, and transition metal oxides. Each material's theoretical capacity, cycle life, and structural stability are analyzed, highlighting the intrinsic challenges such as volumetric expansion, formation of the solid-electrolyte interphase (SEI), and degradation mechanisms that limit their practical application. The review also explores novel materials and composite approaches aimed at overcoming these limitations, such as the incorporation of nanostructured materials, doping strategies, and the development of hybrid anode systems. The integration of advanced characterization techniques and computational modeling is emphasized as crucial for understanding the complex interactions at the nanoscale and for guiding the design of next-generation anodes with enhanced performance metrics. Despite significant progress, the paper identifies several key challenges that remain, including the need for improved safety, higher energy density, and cost-effective manufacturing processes. The discussion extends to emerging trends and potential future directions in the field, such as the exploration of non-lithium-based systems and the development of solid-state batteries. The review concludes by addressing the critical need for continued interdisciplinary research efforts to drive innovation and achieve the commercialization of high-performance anode materials for LIBs.

这篇综述全面探讨了锂离子电池（LIB）负极材料的现状和未来前景，这对储能技术的不断进步至关重要。论文讨论了锂离子电池运行的基本原理，重点介绍了石墨、硅、锡和过渡金属氧化物等各种负极材料的电化学性能。文章分析了每种材料的理论容量、循环寿命和结构稳定性，强调了限制其实际应用的内在挑战，如体积膨胀、固体-电解质间相（SEI）的形成和降解机制。综述还探讨了旨在克服这些局限性的新型材料和复合方法，如加入纳米结构材料、掺杂策略和开发混合阳极系统。文章强调了先进表征技术与计算建模的结合对于理解纳米尺度的复杂相互作用以及指导设计具有更高性能指标的下一代阳极至关重要。尽管取得了重大进展，但论文指出了仍然存在的几个关键挑战，包括需要提高安全性、更高的能量密度和具有成本效益的制造工艺。讨论延伸到该领域的新兴趋势和潜在未来方向，如探索非锂基系统和开发固态电池。综述最后指出，亟需继续开展跨学科研究，以推动创新，实现高性能锂离子电池负极材料的商业化。

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引用次数: 0

Effects of prediction errors on CO2 emissions in residential smart energy management systems with hybrid thermal-electric storage 预测误差对热电混合蓄能住宅智能能源管理系统二氧化碳排放的影响

Cleaner Energy Systems

Pub Date : 2024-09-06 DOI: 10.1016/j.cles.2024.100138

Aleksandr Zaitcev , Alexander Alexandrovich Shukhobodskiy , Tatiana Pogarskaia , Giuseppe Colantuono

Modern residential smart energy management systems allow for more efficient use of renewable energy through the application of various data-driven control strategies. Such strategies typically rely on predicting renewable power generation, domestic power demand, energy price and grid CO2 index. While the generation of such forecasts is well-researched, the impact of the associated prediction errors remains understudied.

This manuscript presents a generalised study of the effect of forecast errors on smart energy system performance. Results are obtained using multiple control optimisation techniques and real life data from residential dwellings spanning over multiple seasons.

Our analysis reveals that ideal forecasts can achieve up to 71.3% CO2 emissions savings compared to a baseline house without a smart energy system. The most significant performance decrease was caused by time lags in all three forecasts (grid CO2 index, solar power generation, and power demand). Among these, the CO2 index forecast was the most sensitive to errors, with an average performance deterioration of approximately 5% per 30 min of time lag. In contrast, errors in solar power generation and power demand forecasts had less impact, causing performance decreases of 18% and 21%, respectively, for extreme changes in forecast profile scale. This research identifies critical points in smart energy system design and offers insights to prioritise improvements in forecast models.

现代住宅智能能源管理系统可通过应用各种数据驱动控制策略，更有效地利用可再生能源。这些策略通常依赖于对可再生能源发电量、国内电力需求、能源价格和电网二氧化碳指数的预测。虽然对此类预测的生成进行了深入研究，但相关预测误差的影响仍未得到充分研究。本手稿对预测误差对智能能源系统性能的影响进行了概括性研究。我们的分析表明，与未安装智能能源系统的基线房屋相比，理想的预测最多可减少 71.3% 的二氧化碳排放量。在所有三种预测（电网二氧化碳指数、太阳能发电量和电力需求）中，性能下降最明显的原因是时间滞后。其中，二氧化碳指数预测对误差最为敏感，平均每 30 分钟的时滞会导致性能下降约 5%。相比之下，太阳能发电量和电力需求预测误差的影响较小，在预测轮廓尺度发生极端变化时，性能分别下降 18% 和 21%。这项研究确定了智能能源系统设计的关键点，为优先改进预测模型提供了启示。

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