Cleaner Energy Systems最新文献

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.

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.

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.

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.

Mosques are unique in terms of architectural design and operational efficiency. Architectural Design, building envelope characteristics, intermittent operating schedules, and occupancy patterns all impact the performance. Managing these factors poses challenges regarding reducing energy consumption and simultaneously achieving the occupants' thermal and visual comfort, especially in hot, arid climatic conditions. Also, the potential benefits of daylighting in reducing energy consumption in mosque buildings need to be addressed. Accordingly, this study evaluates the impact of various retrofit strategies on the operational performance of Al-Imam Al-Hussein Mosque, one of the large historic mosques in Cairo, considering the energy performance, thermal comfort, and daylighting performance. The current performance of the mosque has been analyzed using energy simulation software to determine the areas that affect its performance. Hence, five retrofitting strategies have been studied to assess their impact on improving the performance of the mosque. When changes are applied to the building envelope only by changing the glazing type, the visual discomfort is improved while sufficient daylighting is maintained inside the prayer area. By adding a cooling system and applying changes to the building envelope, thermal comfort was improved, and the visual discomfort decreased. However, this has led to an increase in energy consumption. Combining different strategies (as in strategy 5) by changing the glazing type, changing the operation scheme, adding LED lamps with dimmers, and adding a cooling system has improved the defined performance metrics. It has achieved a 23% decrease in the annual energy consumption, decreasing the visual discomfort by 30% while maintaining sufficient daylighting conditions inside the space, and enhancing occupants’ thermal comfort by 65%. The proposed approach aids in evaluating the retrofit strategies of mosque buildings, considering different criteria, including daylighting performance, to be energy efficient, sustainable, and maintain occupants’ visual and thermal comfort.

Municipal waste refers to a pool of different byproducts generated from domestic activities both in rural and urban areas. It is critical to consider strategies to effectively manage and treat municipal waste by establishing a waste-to-energy (WTE) system. However, waste-to-energy industries are facing several obstacles, including disruptive technologies, stringent government regulations, and some underdeveloped technological aspects. That is why, the researchers conducted a state-of-the-art review that aims to explore how machine learning models in WTE contribute to the achievement of sustainable development goals; second to highlight the strengths and weaknesses of machine learning techniques, and lastly to point out and evaluate the capabilities and flaws in the entire process and operation of WTE system through the use of machine learning, which would serve as a benchmark for a sound decision and policy-making as well as the basis to look into the areas for improvement. Results showed that within WTE systems, machine learning has greatly aided in the achievement of sustainable development goals (SDGs) by streamlining operations, increasing productivity, lessening environmental impact, and improving decision-making. Moreover, machine learning highlighted to foucus on solutions related to corrosion and deterioration occurring in the waste incinerator, chemical pollution in mechanical pre-treatment, and maintaining only an optimal emission in the WTE facility based on the prediction accuracies of 80% and 94% respectively.

This paper conducts a comprehensive assessment of the potential of water, solar, and wind resources for sustainable energy generation. The study is situated in a Moroccan region within eastern Saharan Africa. It presents a detailed comparative analysis between a photovoltaic system (PV) integrated with a pumped hydro storage (PHS), a wind turbine, and a conventional grid, considering both energy production and economic analysis using HOMER software. Moreover, the paper provides an initial social impact assessment of hybrid energy systems integrating locally available water resources, especially during the winter season, alongside photovoltaic and wind technologies. This evaluation delves into aspects of rural electrification and community development. The findings underscore the potential of sustainable energy solutions to drive economic and social progress in the studied area by harnessing the region’s water resources. We proposed this technology because the owners of the area do not greatly benefit from the seasonal groundwater that passes through the valley, despite the presence of a dam. Accordingly, we will exploit this water to generate energy and achieve energy self-sufficiency. By harnessing this underutilized resource, we aim to provide sustainable energy solutions and drive economic and social progress in the region. The results given by HOMER identify the most cost-effective system capable of serving the load at the lowest cost of energy (COE) of about $0.03831 and net present cost (NPC) of about $262,596 under the modeled conditions, and the most satisfactory system chosen by the HOMER optimizer is a PV/Wind/PHS-based hybrid energy system.

This study explores the economic, energetic, exergy efficiency, and environmental benefits of energy integration in pressure-swing distillation, focusing on the separation of tetrahydrofuran/water and acetone/chloroform azeotropes. Heat integration and heat pump techniques are applied to reduce energy consumption. Three energy-efficient configurations are examined, comparing total annual cost (TAC), total energy consumption (TEC), CO₂ emissions, and second-law efficiency. In the tetrahydrofuran/water system, heat integration and heat pump technologies outperform conventional processes, achieving up to 50.2% TAC reduction, 59.6% TEC reduction, 82.8% CO₂ emission reduction, and thermodynamic efficiencies up to 23.5%. In the acetone/chloroform system, similar improvements are observed, with up to 70.9% TAC reduction, 87.2% CO₂ emission reduction, and thermodynamic efficiencies up to 17.6%. These findings demonstrate the effectiveness of energy-saving strategies, endorsing process intensification for environmentally sustainable azeotropic mixture separations.

Diesel generators (DGs) are widely used in India by business and domestic consumers to provide resilience against unreliable power supplies, but have serious adverse environmental and health impacts. Low carbon alternatives to DGs are becoming more widely available and affordable, though technical and non-technical barriers remain to their widespread adoption. Targeted policy and financial interventions would help accelerate the deployment of these alternatives, where such interventions should be based on local needs. To this end, we use a Multi-Criteria Decision Analysis (MCDA) approach to identify appropriate technology alternatives for DGs in residential, industrial and agricultural applications in India. Within this study, the MCDA framework facilitates evidence-based decision-making through structured discussions with local stakeholders and for evaluating the most suitable option from a variety of available alternatives. Overall, our analysis concluded that a hybrid system combining solar PV and battery storage system are considered most suitable for residential, agricultural as well as industrial applications. This study sets out a pragmatic approach for decision makers considering how to minimise the adverse impacts of DGs while recognising the intricacies of requirements of different applications at a local level. Additionally, our approach showcases how co-creation of potential solutions, and ‘transparency’ in the process, can be accomplished in policy-making, which is critical for wider acceptance of interventions.

Forecasting wind power generation is crucial for ensuring grid security and the competitiveness of the power market. This paper presents an innovative approach that combines deep learning (DL) with Teaching-Learning-Based Optimization (TLBO) to predict wind power output accurately. Using a real dataset spanning diverse weather conditions and turbine specifications collected between January 2018 and March 2020, the study employs 18 features as inputs, including Ambient Temperature, Wind Direction, and Wind Speed, with real power output in kW as the target variable. Metaheuristic algorithms including Particle Swarm Optimization (PSO), Barnacles Mating Optimizer (BMO), Biogeography-Based Optimization (BBO), and Firefly Algorithm (FA) are comprehensively compared for model optimization. TLBO-DL consistently provides forecasts that closely align with actual wind power values across instances, substantiated by its low RMSE of 98.7601, indicating effective minimization of errors in wind power forecasting. Comparative analysis with other algorithms reveals that TLBO-DL outperforms PSO-DL (RMSE: 102.6627), BMO-DL (RMSE: 132.4839), BBO-DL (RMSE: 103.8517), and FA-DL (RMSE: 104.7282) in terms of overall forecasting accuracy. The variations in the performance of other algorithms across instances highlight the robustness and effectiveness of TLBO-DL in achieving accurate wind power forecasts. Overall, TLBO-DL emerges as a reliable and superior algorithm for wind power forecasting, consistently providing accurate forecasts across a range of instances.