Utilization Of Renewable Energy Sources Research Articles

Mathematical Modeling and Statistical Analysis of Novel Swarm Computing Based Charging Management Framework for Electric Vehicles

This paper presents a novel swarm computing-based charging management framework for electric vehicles (EVs), designed to optimize charging operations and enhance the grid's capacity to handle EV load without compromising system reliability. We introduce a mathematical model that incorporates swarm intelligence algorithms, particularly Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), to efficiently manage the charging schedules of EVs across multiple charging stations. The framework aims to minimize energy costs, balance load during peak hours, and reduce charging time by distributing the charging load in a more organized manner.A statistical analysis is conducted to validate the effectiveness of the proposed model. Using real-time data and simulation, the study evaluates various scenarios to analyze the impact of the swarm-based approach on overall grid stability and EV charging efficiency. The results indicate significant improvements in grid management, with a reduction in peak load demands and enhanced utilization of renewable energy sources. The analysis also demonstrates the scalability and adaptability of the model to different urban settings and EV penetration rates.The paper concludes with a discussion on the potential integration of this model into existing grid systems and its implications for future smart grid and EV infrastructure developments. The proposed framework not only contributes to more sustainable energy management practices but also opens new avenues for research in the domain of intelligent transportation systems and smart grid integration.

Evaluation of Organic Waste Long-Term Effects on Cellulose, Hemicellulose and Lignin Content in Energy Grass Species Grown in East-Central Poland

Biomass can be used for electricity generation, especially in developing countries, but also in developed ones, where the utilization of renewable energy sources is being integrated into a sustainable economy. There are considerable differences in the scale of biomass use and in the technology of its processing. One of the most important sources of biofuel is the biomass of grass. This research aimed to determine the long-term effects of organic fertilizers on cellulose, hemicellulose, and lignin content in the biomass of three grass species: giant miscanthus (Miscanthus × giganteus), prairie cordgrass (Spartina pectinata), and switchgrass (Panicum virgatum L.) in the first three years of growth. The experiment was established in four replications on microplots of 2 m2 in April 2018. Before planting grass rhizomes, municipal sewage sludge (SS) and spent mushroom substrate (SMS) were introduced into the soil in various combinations. Biomass is harvested in December every year. The content of structural polysaccharides in the grass species statistically significantly varied in response to organic waste. Compared to other fertilizer combinations, SS application increased the content of cellulose in the biomass of Miscanthus giganteus (43.66% of DM) and Spartina pectinata (37.69% of DM) and hemicellulose in Spartina pectinata (27.80% of DM) and Panicum virgatum (23.64% of DM). Of the three species of grass, the chemical composition of Miscanthus giganteus cell walls was the most favorable for biofuel production, with the most cellulose and hemicellulose and the least lignin compared to other grass species. The content of lignin in the biomass of Miscanthus × giganteus and Spartina pectinata was the greatest on the plot with SMS and amounted to 7.79% of DM and 12.32% of DM, respectively. In the case of Panicum virgatum, the average content of lignin was similar across all fertilized plots, with 15.42% DM.

Coordinated planning for offshore wind and electricity–hydrogen system based on SDDP: A case study of coastal provinces in China

The coordinated development of offshore wind and electricity–hydrogen systems (OW-EHS) has the potential to enhance the utilization of renewable energy sources and achieve carbon neutrality. This article presents a case study of nine coastal provinces in China and designs four scenarios to analyze the economic benefits and potential hydrogen production capacity of OW-EHS. A multi-stage stochastic dual dynamic programming based approach is proposed for integrating OW-EHS, exploring offshore wind energy as both a primary electricity source for coastal regions and a clean, sustainable energy source for electrolyzers. It accounts for variations in hydrogen demand, wind uncertainty, load profiles, and electricity price dynamics, offering a holistic perspective on the feasibility and benefits of collaborative offshore wind and electricity–hydrogen systems. A comparative analysis highlights the effectiveness of the proposed planning approach, demonstrating its capacity to maximize the overall system benefits, encompassing both the optimal levelized cost of energy and hydrogen production. The new discoveries elucidate the unique attributes and advantages of each coastal province in OW-EHS. This research provides a tailored blueprint for coastal provinces in China to harness their offshore wind potential and accelerate progress towards carbon neutrality.

Performance evaluation and multi-objective optimization of hydrogen-based integrated energy systems driven by renewable energy sources

This study proposes an integrated energy system using hydrogen storage to realize the efficient utilization of renewable energy sources and reduce the fluctuation when renewable energy is connected to grid. The system utilizes solar and wind energy to realize hydrogen production, desalination, and CCHP. First, the energy, exergy, and economic evaluations for the proposed system are carried out, and then an in-depth analysis of the key operating parameters is performed. The system achieves energy efficiency, exergy efficiency, and cost rate of 48.49%, 19.98%, and 7.969$/h, respectively. And the exergy analysis shows that the main exergy destructions are caused by the parabolic trough solar collector and the transcritical CO2 power cycle. The parametric analysis demonstrates when solar radiation flux and wind speed increase, the exergy efficiency and hydrogen production are increased, but the cost rate is increased accordingly. Finally, two sets of multi-objective optimization schemes are performed combining the artificial neural network with the non-dominant genetic algorithm-Ⅱ(NSGA-II). For the optimized fresh water output, cost rate, and exergy efficiency, it is achieving improvements of 51.73%, 8.4%, and 3.6%, and for the optimized hydrogen production, cost rate, and exergy efficiency, it is increased by 12.53%, 0.564%, and 0.75%, respectively.

Assessing the Damage to Environmental Pollution: Discerning the Impact of Environmental Technology, Energy Efficiency, Green Energy and Natural Resources

The existing literature covers the topic of environmental pollution, but there is a scarcity of research that specifically examines the factors contributing to financial losses caused by carbon emissions. In this perspective, this ongoing analysis provides an understanding of the impact of environmental technology, energy efficiency, renewable energy consumption, natural resources, and economic growth on carbon dioxide damage in Organization for Economic Cooperation and Development (OECD) countries from 2000 to 2021 using the “Method of Moments Quantile Regression (MMQR)”, and “Dumitrescu–Hurlin (D-H)” causality test. The findings from the MMQR revealed that environmental control technology, renewable energy consumption, and energy efficiency contribute to reducing carbon dioxide damage at different quantiles. It was also found that economic growth and natural resources contribute to the increase in carbon dioxide damage in various quantities. Additionally, a one-way causality result was obtained from environmental technology, energy efficiency, renewable energy consumption, natural resources, and economic growth towards carbon dioxide damage. These results indicate that policymakers in OECD nations should provide suggestions on the efficient utilization of renewable energy sources and environmentally friendly technologies to minimize carbon dioxide damage.

PV- Battery Based AC/DC Hybrid Multiport Power Routing System

The utilization of renewable energy sources has significantly increased as a result of growing environmental concerns and the depletion of traditional energy sources. One clean and sustainable energy source that has gained popularity among them is photovoltaic (PV) systems. Batteries and other energy storage devices are necessary to offer a steady and dependable power source, though, because PV energy is sporadic. Effective power flow control and a dependable power supply are demonstrated by the test results. By reducing dependency on traditional sources and supporting clean and sustainable energy, this adaptable and scalable solution is suitable for residential, commercial, and industrial environments. An AC/DC hybrid multiport power routing system based on PV batteries is shown in this project to solve this problem. The battery bank, power routing unit, and PV array that make up the suggested system. keywords: PV panels, batteries, inverters, and a microcontroller-based power routing unit.

Testing the Stability of NASICON Solid Electrolyte in Seawater Batteries

Rechargeable batteries play a crucial role in the utilization of renewable energy sources. Energy storage systems (ESSs) are designed to store renewable energy efficiently for immediate use. The market for energy storage systems heavily relies on lithium-ion batteries due to their high energy density, capacity, and competitiveness. However, the increasing cost and limited availability of lithium make long-term use challenging. As an alternative to Li-ion batteries, rechargeable seawater batteries are gaining attention due to their abundant and complementary sodium ion active materials. This study focuses on the preparation and characterization of Na3.0Zr2Si2PO12- and Na3.15Zr2Si2PO12-type ceramic membranes and testing their stability in seawater batteries used as solid electrolyte. From the surface analysis, it was observed that the Na3.15Zr2Si2PO12 powder showed a specific surface area of 2.94 m2/g compared to 2.69 m2/g for the Na3.0Zr2Si2PO12 powder. The measured NASICON samples achieved ionic conductivities between 7.42 × 10−5 and 4.4 × 10−4 S/cm compared to the NASICON commercial membrane with an ionic conductivity of 3.9 × 10−4 S/cm. Battery testing involved charging/discharging at various constant current values (0.6–2.0 mA), using Pt/C as the catalyst and seawater as the catholyte.

Application of a GIS-Based Multi-Criteria Decision-Making Approach to the Siting of Ocean Thermal Energy Conversion Power Plants: A Case Study of the Xisha Sea Area, China

In order to achieve the goals of carbon neutrality and reduced carbon emissions, China is increasingly focusing on the development and utilization of renewable energy sources. Among these, ocean thermal energy conversion (OTEC) has the advantages of small periodic fluctuations and large potential reserves, making it an important research field. With the development of the “Maritime Silk Road”, the Xisha Islands in the South China Sea will see a growing demand for electricity, providing the potential for OTEC development in this region. Optimal site selection of OTEC power plants is a prerequisite for developing thermal energy provision, affecting both the construction costs and future benefits of the power plants. This study establishes a scientific evaluation model based on the decision-making frameworks of geographic information systems (GISs) and multi-criteria decision-making (MCDM) methods, specifically the analytic hierarchy process (AHP) for assigning weights, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to reclassify the factors, and weighted linear combination (WLC) to compute the suitability index. In addition to commonly considered factors such as temperature difference and marine usage status, this study innovatively incorporates geological conditions and maximum offshore distances of cold seawater based on cost control. The final evaluation identifies three suitable areas for OTEC development near the Xuande Atoll and the Yongle Atoll in the Xisha Sea Area, providing valuable insights for energy developers and policymakers.

Optimal control algorithms for Gabon’s Smart Grid: Enhancing efficiency and sustainability

The adoption of smart grid technologies offers Gabon several opportunities to enhance energy efficiency and sustainability. This study investigates the use of optimum control algorithms to increase grid stability and enhance the utilization of renewable energy sources. Mathematical models and simulations are used to assess genetic algorithms in light of Gabon's unique energy situation. Systems known as electrical smart grids supply electricity to users directly from power plants in an effort to reduce costs, cut down on blackouts, and improve energy efficiency. Smart grids, or SGs, are well-known pieces of equipment because of their amazing capabilities, which include bi-directional communication, stability, power failure detection, and interconnectivity with appliances for monitoring. Many modern data and security management systems, including modeling, monitoring, optimization, and/or artificial intelligence, lead to SGs. Energy was once cheap, easily managed, and dependent only on fundamental elements. Demand has significantly increased as a result of the current circumstances, which has also increased Gabon's electricity expenses, the likelihood of a contingency, and the complexity of the electrical network. In Gabon, smart grids have shown to be the most practical and clever way to lessen these problems in recent years. An electrical network with information technology integrated is called a smart grid. According to this study, using genetic algorithms in a smart grid could lower Gabon's overall electricity prices. In order to meet demand, the proposed approach makes use of off-grid battery banks and renewable energy sources. The goal of GA optimization is the short-term time averaged power cost; factors that affect this include grid energy to satisfy load, battery discharge, and other factors. MATLAB software is used to solve the optimization problem over a 24-hour period of renewable input, real-time energy pricing, and load. The results are presented.

Renewable energy-driven for sustainable off-grid desalination: A comprehensive review on technical highlights and process

Despite the traditional desalination processes have achieved tremendous progress in terms of technology and cost, it is still limited by the high energy consumption and carbon emissions brought about by expensive fossil fuels. Finding alternative energy sources is essential to meet the growing demand for desalination. Over the past few decades, plenty of efforts have focused on driving the desalination process through renewable energy sources in order to achieve the goal of environmentally friendly off-grid desalination and zero liquid discharge. However, limited by technology and economic cost, the desalination processes driven by renewable energy are difficult to carry out large-scale application, and they are more used as an auxiliary unit of the traditional desalination processes. First of all, this article reviews the basic principles of solar interface evaporation technology, and further, summarizes the sustainable desalination technologies driven by solar and geothermal energy completely. In the end, the current global environment and energy troubles are discussed, and the necessity and significance of promoting renewable energy for desalination in the future are emphasized. This review will assist to promote attention to the development and utilization of renewable energy sources, with a focus on large-scale renewable-driven off-grid desalination projects in the future.

Empowering Green Energy Storage Systems with MXene for a Sustainable Future.

Green energy storage systems play a vital role in enabling a sustainable future by facilitating the efficient integration and utilization of renewable energy sources. The main problems related to two-dimensional (2D) materials are their difficult synthesis process, high cost, and bulk production, which hamper their performance. In recent years, MXenes have emerged as highly promising materials for enhancing the performance of energy storage devices due to their unique properties, including their high surface area, excellent electrical and thermal conductivity, and exceptional chemical stability. This paper presents a comprehensive scientific approach that explores the potential of MXenes for empowering green energy storage systems. Which indicates the novelty of the article. The paper reviews the latest advances in MXene synthesis techniques. Furthermore, investigates the application of MXenes in various energy storage technologies, such as lithium-ion batteries, supercapacitors, and emerging energy storage devices. The utilization of MXenes as electrodes in flexible and transparent energy storage devices is also discussed. Moreover, the paper highlights the potential of MXenes in addressing key challenges in energy storage, including enhancing energy storage capacity, improving cycling stability, and promoting fast charging and discharging rates. Additionally, industrial application and cost estimation of MXenes are explored. As the output of the work, we analyzed that HF and modified acid (LiF and HCl) are the established methods for synthesis. Due to high electrical conductivity, MXene materials are showing extraordinary results in energy storage and related applications. Making a composite hydrothermal method is one of the established methods. This scientific paper underscores the significant contributions of MXenes in advancing green energy storage systems, paving the way for a sustainable future driven by renewable energy sources. To facilitate the research, this article includes technical challenges and future recommendations for further research gaps in the topic.

Cylindrical Composite Structural Design for Underwater Compressed Air Energy Storage

Abstract The utilization of renewable energy sources is pivotal for future energy sustainability. However, the effective utilization of this energy in marine environments necessitates the implementation of energy storage systems to compensate for energy losses induced by intermittent power usage. Underwater compressed air energy storage(UWCAES) is a cost-effective and emission-free method for storing energy underwater. This technology has proven to be effective and viable, and it offers significant benefits in terms of energy efficiency and sustainability. In this paper, a cylindrical composite structure UWCAES tank is designed. At first, the materials and shapes of the different forms of air containers were evaluated, and the relationship between container diameter, length, and number of air containers was analyzed on the basis of the range of energy storage densities for different kinds of systems. Subsequently, the materials to be applied in the tanks were investigated and selected according to the actual working conditions of the system. Eventually, finite element models and prediction formula were developed and the influence of changes in the thicknesses of the steel reinforcement were discussed. The results demonstrated that the storage tank possesses adequate environmental resistance and load-bearing capacity, which can provide a reference for its practical engineering implementation.

Heating performance of PCM radiant floor coupled with horizontal ground source heat pump for single-family house in cold zones

As the energy demand for space heating increases, the necessity for innovative and energy-efficient heating technologies has become more urgent. This study proposes and evaluates a heating system that integrates a phase change material radiant floor with a horizontal ground source heat pump (PCM floor-HGSHP, Case 1), intending to maintain indoor comfort temperatures and enhance energy efficiency through the utilization of renewable energy sources. Despite the considerable potential of PCM floors, their combination with HGSHP requires further research to fully unlock their benefits. The study involved simulating the PCM floor using a validated TRNSYS component and developing an advanced rule-based control strategy with time-of-use tariffs as a key input to optimize system operation. To elucidate and quantify the advantages of the PCM floor-HGSHP system and verify its applicability in cold regions of China, a single-family house was used as a case study building, four representative cities were selected, and two comparative simulation cases were defined, along with three key performance indicators. The results indicate that, compared with the radiant floor heating system with HGSHP (RFHS-HGSHP, Case 2), the incorporation of PCM in the floor of Case 1 improves the indoor temperature stability by 8.6 %, enhances the load flexibility by 18.1 %, and reduces the total operating costs by 13.8 %. Furthermore, relative to the PCM floor with air-to-water heat pump system (PCM floor-AWHP, Case 3), the proposed system which substitutes the AWHP with the HGSHP, extends the duration of indoor comfort temperatures by 9.6 % and decreases the system electricity consumption by 33.7 %. The proposed system demonstrates excellent thermal and energy performance across all selected cities, proving to be both technically feasible and significantly beneficial for maintaining indoor comfort temperatures and reducing operational costs. This study offers valuable insights and technical support for designing and optimizing residential heating systems in cold regions, thereby promoting the development and application of renewable energy sources.

Promoting food security and sustainability with a transportable indirect evaporative solar pre-cooler

Perhaps one of the most important goals of sustainable development for developing countries is to enhance the utilization of renewable energy sources in all sectors in general and the agricultural sector in particular. The pre-cooling process helps to maintain quality and extend the shelf life of the fruits and vegetables. The aim of this study was to fabricate an indirect evaporative solar precooler (IESP) to reduce post-harvest loss for agricultural products. Tomato crop was chosen to be pre-cooled as an example of agricultural value chains. Experimental variables included three temperatures of cooling water (15, 10 and 5 °C) and two air velocities that passed through the cooling cabinet (1.5 and 2.5 m s-1). The results showed that at 1.5 m s-1 air velocity, the actual coefficient of performance was 19.4, 25.5 and 34.7% at cooling water temperatures 15, 10 and 5 °C, respectively. At 2.5 m s-1 air velocity, the actual coefficient of performance was 23.1, 28.8 and 37.2% at cooling water temperatures 15, 10 and 5 °C, respectively. The performance of the IESP under these conditions was 15.4 °C, 91.5% RH, 0.338 TR refrigeration load and 37.2% COPcyc. Total energy consumption was 6.4 kWh day-1. The solar pre-cooler performance proved a very feasible solution to the demands of small and medium horticultural holdings, especially in cities with a very hot climate to keep vegetables and fruits from deteriorating after harvest.

An Analysis of the most Promising Strategies for Controlling the MPPT of Photovoltaic Systems

There has been a tremendous increase in the utilization of renewable energy sources, particularly, solar energy. In terms of potential energy sources, photovoltaics are among the most promising. This article provides a concise assessment of the prospective approaches of maximum power point tracking (MPPT) control for the photovoltaic system. To begin, a discussion is held regarding the fundamentals of the photovoltaic system, including the concept and fundamental theory behind the P&O MPPT control method, which is frequently utilized. After that, three of the most promising techniques for enhanced MPPT control are selected. There is also coverage of research that is meaningful on the fundamental theory and the primary concept of the three techniques. Several of the research findings from different approaches are compiled in this document for the purpose of comparison. The tracking time, the complexity of implementation, and the accuracy are the three main indexes that are examined in this final comparison of these three systems. The efficiency, accuracy, and applicability of these methodologies are indicated by these indexes, which can serve as a meaningful reference for future applications and additional research.

The impact of AI on boosting renewable energy utilization and visual power plant efficiency in contemporary construction

The integration of Artificial Intelligence (AI) in the renewable energy sector has revolutionized the efficiency and utilization of renewable energy sources in contemporary construction, significantly impacting visual power plant operations. AI technologies, including machine learning and predictive analytics, optimize energy production, enhance system performance, and streamline maintenance processes, thereby driving the adoption and efficiency of renewable energy systems. AI-powered predictive maintenance tools analyze vast amounts of data from renewable energy installations, such as solar panels and wind turbines, to predict and prevent equipment failures. This proactive approach reduces downtime and maintenance costs, ensuring continuous and efficient energy generation. Additionally, AI algorithms optimize energy storage and distribution by accurately forecasting energy production and demand. This optimization helps balance supply and demand, reducing reliance on non-renewable energy sources and enhancing grid stability. In visual power plants, AI enhances operational efficiency through advanced monitoring and control systems. Real-time data from sensors and IoT devices are processed by AI to provide actionable insights, enabling operators to make informed decisions promptly. AI-driven automation in visual power plants ensures optimal performance by adjusting parameters in real-time, based on environmental conditions and energy demand, thus maximizing energy output. Moreover, AI facilitates the design and construction of smart buildings and infrastructure, integrating renewable energy systems seamlessly. AI-driven energy management systems in buildings analyze consumption patterns, optimize energy use, and promote energy-saving practices, contributing to overall energy efficiency. This integration not only reduces the carbon footprint of construction projects but also aligns with global sustainability goals. The impact of AI on boosting renewable energy utilization and visual power plant efficiency is profound. By leveraging AI technologies, contemporary construction can achieve higher energy efficiency, lower operational costs, and increased sustainability. As AI continues to evolve, its role in the renewable energy sector will likely expand, further enhancing the capabilities of renewable energy systems and paving the way for a more sustainable future in construction and beyond. This paper underscores the transformative potential of AI in renewable energy and its critical role in shaping the future of sustainable construction practices.

Optimization of multi-vehicle charging and discharging efficiency under time constraints based on reinforcement learning

In the Vehicle-to-Grid (V2G) scenario, a multitude of coordinated electric vehicles (EVs) equipped with high-capacity batteries actively participate in power grid dispatching as energy carriers, aiming to achieve a tripartite objective encompassing peak load reduction and valley filling, enhanced utilization of renewable energy sources, and added benefits for electric vehicle owners. To address the existing limitations in the charging–discharging decision-making process for electric vehicles based on V2G, such as the lack of consideration for charging pile constraints, EV profitability, EV transportation timeliness, and high costs associated with central servers, we proposed a reinforcement learning-based Multi-vehicle Joint Routing and Charging–Discharging Decision algorithm (MJRCDD). Firstly, the Markov decision process (MDP) was established to describe the problem, and the route selection and charging–discharging behavior of the vehicle were innovatively integrated in the vehicle action space. Secondly, the multi-vehicle joint route planning and charging–discharging decision problem was solved by multi-agent reinforcement learning. Finally, the effectiveness of MJRCDD was verified by simulation and comparison experiments based on PeMS.

Predictive modelling of drag, lift and torque coefficients of cylindrical biomass particles under artificial intelligence

The escalating threats of climate change and fossil fuel depletion have greatly spurred the widespread utilization of renewable energy sources. Biomass energy stands out as the most abundant renewable energy resource. Among all biomass conversion technologies, direct combustion of biomass for heating and power generation represents the most common and cost-effective approach. Suspended combustion technology is widely employed in existing biomass combustion power plants worldwide. However, conventional modeling procedures often fail to adequately address the differences between coal and biomass combustion, particularly the significant impact of large-sized and highly non-spherical cylindrical straw biomass particles on their trajectories. This study utilizes OpenFOAM-body-fitted mesh direct numerical simulation (DNS) to generate a Computational Fluid Dynamics (CFD) dataset consisting of 300 data points for cylindrical particles, aimed at constructing a prediction model based on machine learning (N+1) for key aerodynamic parameters (drag coefficient, lift coefficient, and torque coefficient). The proposed model enables precise prediction of aerodynamic parameters for a wider range of different cylindrical particles and flow conditions, thus extending the existing Euler-Lagrangian model for non-spherical biomass multiphase flow.

Machine learning-based energy management and power forecasting in grid-connected microgrids with multiple distributed energy sources

The growing integration of renewable energy sources into grid-connected microgrids has created new challenges in power generation forecasting and energy management. This paper explores the use of advanced machine learning algorithms, specifically Support Vector Regression (SVR), to enhance the efficiency and reliability of these systems. The proposed SVR algorithm leverages comprehensive historical energy production data, detailed weather patterns, and dynamic grid conditions to accurately forecast power generation. Our model demonstrated significantly lower error metrics compared to traditional linear regression models, achieving a Mean Squared Error of 2.002 for solar PV and 3.059 for wind power forecasting. The Mean Absolute Error was reduced to 0.547 for solar PV and 0.825 for wind scenarios, and the Root Mean Squared Error (RMSE) was 1.415 for solar PV and 1.749 for wind power, showcasing the model’s superior accuracy. Enhanced predictive accuracy directly contributes to optimized resource allocation, enabling more precise control of energy generation schedules and reducing the reliance on external power sources. The application of our SVR model resulted in an 8.4% reduction in overall operating costs, highlighting its effectiveness in improving energy management efficiency. Furthermore, the system’s ability to predict fluctuations in energy output allowed for adaptive real-time energy management, reducing grid stress and enhancing system stability. This approach led to a 10% improvement in the balance between supply and demand, a 15% reduction in peak load demand, and a 12% increase in the utilization of renewable energy sources. Our approach enhances grid stability by better balancing supply and demand, mitigating the variability and intermittency of renewable energy sources. These advancements promote a more sustainable integration of renewable energy into the microgrid, contributing to a cleaner, more resilient, and efficient energy infrastructure. The findings of this research provide valuable insights into the development of intelligent energy systems capable of adapting to changing conditions, paving the way for future innovations in energy management. Additionally, this work underscores the potential of machine learning to revolutionize energy management practices by providing more accurate, reliable, and cost-effective solutions for integrating renewable energy into existing grid infrastructures.

Simultaneous desulfurization and dearomatization of simulated straight-run diesel with novel green DBN-based ionic liquids

Given the surplus of diesel fuel in China and the extensive utilization of renewable energy sources, there is a growing inclination to eliminate organic sulphur and aromatic hydrocarbons from diesel fuel for its application as high-quality ethylene cracking feedstock. In this study, a series of 1,5-diazabicyclo[4.3.0]non-5-ene (DBN)-based ionic liquids with [DBNH]+ as the cation and imidazole as the anion were designed and synthesized for the simultaneous desulfurization and dearomatization of straight-run diesel fuel. Considering factors such as extractant stability, residue content in oil, and separation performance, [DBNH][Tr], exhibiting excellent performance characteristics, was screened as the optimal extractant. Following three-stage cross-flow extraction with an extractant/oil ratio of 3:1 at 30 °C, complete removals of sulfides (benzothiophene) and polycyclic aromatic hydrocarbons (1-methylnaphthalene) were achieved while saturated alkanes reached up to 95.8 wt%, making it suitable for ethylene cracking applications. The interaction mechanisms between ionic liquids and oil components were revealed by atoms in molecules (AIM) topology analysis, independent gradient model based on Hirshfeld partition (IGMH) analysis, and symmetric adaptive perturbation theory energy decomposition techniques, revealing that dispersion-dominated van der Waals (VDW) forces primarily govern interactions due to π-π stacking.