Cleaner Energy Systems最新文献

{"title":"Assessment of the impact of electric vehicle charging on low voltage distribution system in Takoradi","authors":"Richard Arthur ,&nbsp;Albert Kotawoke Awopone ,&nbsp;Samuel Gyapong","doi":"10.1016/j.cles.2025.100226","DOIUrl":"10.1016/j.cles.2025.100226","url":null,"abstract":"<div><div>This study assessed the impact of electric vehicle (EV) charging on low voltage (LV) distribution systems at different penetration levels. The existing electric power distribution system of Takoradi, the Western Regional capital city of Ghana was modelled using the power analysis software <span><span>Electrical Transient and Analysis Program 2019</span></span>. Load flow analysis was then performed on the low voltage distribution system to further assess the total amount of EVs the distribution system can handle. EV charging impacts on the current LV distribution system was assessed under three different scenarios; current state, minimum and maximum uptakes penetration levels of EVs. Two different EV charger models were employed to represent home charging (HC)-7.4 kW level-2 and fast charging (FC)-50 kW level-3. Voltage variations and transformer loading at twelve substations were meticulously noted in all simulations. The load flow simulation did not show any significant impact on the distribution system at the current state and minimum uptake penetration levels. However, at a maximum penetration level of 1.88 % for HC and 1.11 % for FC, under voltage conditions were observed at most buses with the condition deteriorating to the highest penetration level of 11.63 % and 6.87 % for HC and FC respectively where the system tend to fail. Domestic loads significantly increased along with the increment of EV penetration levels over the years which contributed to total instability of Takoradi Distribution System (TDS). The study revealed that, the impact of EV charging on low voltage networks vary by factors such as vehicle density, power demand and network architecture. In effect, EV charging types, significantly impact load and voltage variables, contributing to network instability in TDS. To address these challenges, integrating renewable energy sources like solar and wind with EV charging infrastructure is recommended to promote grid stability and sustainable energy practices. The findings of this study will assist policy-makers take the appropriate actions needed to manage EV loads.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"13 ","pages":"Article 100226"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modular ammonia-based hydrogen propulsion for drones and ground vehicles","authors":"Jaroslav Pavelka","doi":"10.1016/j.cles.2025.100228","DOIUrl":"10.1016/j.cles.2025.100228","url":null,"abstract":"<div><div>Hydrogen logistics remain a bottleneck for sustainable mobility, particularly in off-grid applications where compressed hydrogen and batteries face limits in energy density and infrastructure. This study proposes the first modular ammonia-to-hydrogen propulsion unit engineered for UAVs and ground vehicles. The compact system integrates catalytic cracking of liquid NH₃ at 650–750 °C (Ru/SiO₂ for UAVs, Ni/Al₂O₃ for vehicles), hydrogen purification via Pd/Ag or ceramic membranes, and power conversion through either PEM fuel cells or hydrogen-adapted internal combustion engines. A cartridge-based modular design enables easy replacement of catalysts and membranes, direct adaptation to platform needs, and scalable performance. Modeling indicates that a 200 kg UAV can achieve ranges up to 150 km with 1.2–1.5 kg NH₃ per 100 km. The novelty lies not in the individual processes, but in their first integration into a deployable system architecture optimized for mobility. Coupled with nuclear-sourced ammonia production, this approach outlines a fossil-free pathway that combines high energy density with improved logistics and lifecycle sustainability.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"13 ","pages":"Article 100228"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hydrogen impact on gas turbine operating flexibility in simple and combined cycle mode","authors":"Matteo Cappellini,&nbsp;Chiara Castagna,&nbsp;Silvia Ravelli","doi":"10.1016/j.cles.2025.100229","DOIUrl":"10.1016/j.cles.2025.100229","url":null,"abstract":"<div><div>This study addresses two topical issues: carbon-free power production, on the one hand, and secure and reliable energy supply on the other hand. Undeniably, to integrate increasing shares of renewables into sustainable and competitive electricity systems, “capacity mechanisms”, i.e., a range of solutions aimed at ensuring adequate power capacity, are needed. Clean, dispatchable power generation is one such solution. Specifically, gas turbines fed by green fuels such as hydrogen can be scheduled to provide power when the contribution from solar and wind sources is not enough to meet the demand or in challenging situations, even for a few hours per year. With the idea of retrofitting existing gas turbine (GT) plants to hydrogen combustion, a thermodynamic model was developed by means of Thermoflex® software in a dual context: peaking, with a small, simple-cycle (SC) GT or “load-following”, with a large size combined cycle (CC) with 1 × 1 configuration. In both cases, <em>ad hoc</em> control strategies were implemented to increase thermal efficiency (<em>η</em>) at partial load. Simulations were run on an hourly basis to meet the prescribed load profiles at representative locations, for two typical hot and cold days: computations were carried out assuming 100% hydrogen as fuel, for comparison against conventional natural gas (NG), given the same GT output requirement and environmental condition. This study's novelty stems from these constraints.</div><div>The results show that replacing NG with hydrogen combines obvious decarbonization with increases in net power (P_n) and net efficiency (η_n), the magnitude of which depends on the off-design control strategy, which in turn is a function of the GT operating environment. Overall, the largest increase in η_n was quantified at about 0.6 percentage points (pp). Furthermore, the combustor shifted towards leaner conditions so that the maximum cycle temperature does not exceed that with the conventional fuel.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"13 ","pages":"Article 100229"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Random forest based wind power prediction method for sustainable energy system","authors":"Zuriani Mustaffa ,&nbsp;Mohd Herwan Sulaiman","doi":"10.1016/j.cles.2025.100210","DOIUrl":"10.1016/j.cles.2025.100210","url":null,"abstract":"<div><div>Wind power generation prediction is critical for the effective integration of renewable energy into the power grid, supporting stability, reliability, and sustainability in electricity supply. However, the inherent variability and non-linear characteristics of wind patterns present substantial challenges to accurate prediction. This study tackles these challenges by utilizing the Random Forest (RF) algorithm, an ensemble learning approach renowned for its ability to capture complex, non-linear relationships in data. The RF model’s performance is compared with three commonly used prediction techniques: Neural Networks (NN), Extreme Gradient Boosting (XGBoost), and Linear Regression (LR). The models were evaluated using historical wind power data and key meteorological variables, with performance assessed through multiple metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Maximum Error (MAX), Standard Deviation (STD DEV), and R-squared (R²). The results indicate that the RF model achieved the best performance, with an RMSE of 55.11 and an R² of 0.9882, outperforming the NN, XGBoost, and LR models. Specifically, the NN model achieved an RMSE of 95.5 with an R² of 0.9651, XGBoost had an RMSE of 93.32 and an R² of 0.9666, and the LR model exhibited an RMSE of 144.45 with an R² of 0.9084. These findings demonstrate RF's superior predictive accuracy and robustness, making it a powerful tool for wind power forecasting, providing valuable insights for grid management and renewable energy planning.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"12 ","pages":"Article 100210"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144913657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysing emissions in compression ignition engines powered by diesel blend with bio diesel and nano particles","authors":"Hasanain A. Abdul Wahhab ,&nbsp;Miqdam T. Chaichan","doi":"10.1016/j.cles.2025.100221","DOIUrl":"10.1016/j.cles.2025.100221","url":null,"abstract":"<div><div>This research involves both experimental and numerical studies that explore how different amounts of iron oxide nanoparticles (Fe₃O₄) in biodiesel blends influence combustion in a single cylinder diesel engine. To simulate the engine dynamics and combustion processes, Diesel Engine Fluent, a specialized numerical tool from ANSYS 19.0 software, was employed. Biodiesel blends were tested with three levels of Fe₃O₄ concentration: 50, 100, and 150 ppm. The samples used in these tests were labelled as D100, D80B20, D80B20N50, D80B20N100, and D80B20N150 (D80 stands for 80 % diesel, B20 for 20 % biodiesel, and N for nanoparticle). Various engine loads, ranging from 20 % to 90 %, were examined at speeds between 1100 and 2200 rpm. The presence of nano additives led to a reduction in emissions, attributed to their catalytic effects and enhanced surface area. The findings indicated that the addition of nanoparticles effectively lowered emissions. At a 90 % load, CO₂ emissions decreased by 3 %, 5 %, and 8 % for the D80B20, D80B20N50, D80B20N100, and D80B20N150 blends, respectively. Additionally, the presence of nano-additives also contributed to a decline in CO emissions from these blends. Furthermore, the combustion of the nanoparticle mixtures produced lower NOx emissions compared to the D80B20 blend.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"12 ","pages":"Article 100221"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing grid stability and renewable energy: Policy evolution of battery energy storage systems in China, Japan, and South Korea","authors":"Michael Osezua,&nbsp;Olusegun S. Tomomewo","doi":"10.1016/j.cles.2025.100199","DOIUrl":"10.1016/j.cles.2025.100199","url":null,"abstract":"<div><div>The evolution of policies and regulations supporting battery energy storage system (BESS) development, utilization, and sustainability to enhance resource adequacy was investigated. The study examined the role of BESS in mitigating renewable energy intermittency, using China, Japan, and South Korea as case studies. The review finds that environmental, economic, political, technological, and regulatory factors significantly influence BESS applications' viability, growth, and sustainability. BESS offers environmental and social benefits but faces challenges like raw material price volatility and supply chain disruptions. The study concludes that integrating renewable energy sources and the growing demand for grid stability will continue to drive BESS adoption. However, supply chain challenges, international green trade barriers, and evolving technologies will shape the next phase of BESS growth. Collaboration among stakeholders, strategic partnerships, technological innovation, and supportive policies are required to advance the global adoption of BESS. The study highlights critical policy frameworks facilitating BESS deployment while ensuring grid stability and sustainability.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"12 ","pages":"Article 100199"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144595780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analyzing energy consumption trends and environmental influences: A time-series study on temperature, renewables, and demand correlations","authors":"Hasanur Zaman Anonto ,&nbsp;Md Ismail Hossain ,&nbsp;Abu Shufian ,&nbsp;Protik Parvez Sheikh ,&nbsp;Sadman Shahriar Alam ,&nbsp;Md. Shaoran Sayem ,&nbsp;S M Tanvir Hassan Shovon","doi":"10.1016/j.cles.2025.100209","DOIUrl":"10.1016/j.cles.2025.100209","url":null,"abstract":"<div><div>This study investigates energy consumption trends and environmental influences by analyzing time-series data to explore the correlation between temperature, humidity, renewable energy contributions, and energy demand. The research focuses on developing an advanced hybrid machine learning model using <em>LightGBM, XGBoost</em>, LSTM, and SHAP to enhance the accuracy and interpretability of energy consumption predictions. Using data from January 2022 to January 2025 across residential, commercial, and industrial buildings, the study examines the impact of temperature fluctuations, humidity, and renewable energy integration on energy consumption. Temperature dependency is further explored in the study, where it is shown that energy consumption is directly influenced by temperature, with energy use at 20 °C being 2000 kWh, increasing to 3200 kWh at 30 °C (on an annual basis), further confirming the shaped dependency with increased cooling demands during warmer months. Additionally, energy consumption varies significantly across building types, with industrial buildings showing higher and more stable energy demands than residential and commercial buildings. Results indicate that <em>XGBoost</em> provides the best predictive performance, with an RMSE of 118.24 and an R² score of 0.9871, followed by LSTM with an RMSE of 135.86 and an R² score of 0.9752, and Linear Regression with RMSE of 187.76 and an R² score of 0.9672. The hybrid model effectively predicts energy consumption and offers valuable insights into how environmental factors influence energy demands across different building types. The findings contribute to optimizing energy management strategies, improving innovative grid systems, and promoting sustainable building operations while highlighting the role of renewable energy in shaping energy consumption patterns.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"12 ","pages":"Article 100209"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Providing electricity price information to households and reducing electricity consumption: Results from a field experiment in Japan","authors":"Kazuma Murakami ,&nbsp;Ikuho Kochi","doi":"10.1016/j.cles.2025.100195","DOIUrl":"10.1016/j.cles.2025.100195","url":null,"abstract":"<div><div>Electricity accounts for 65.3 % of household CO₂ emissions in Japan; therefore, more household energy conservation is needed. This study examines the effects of information provision on various household energy-saving behaviors using randomized controlled trials (RCT). For Japanese consumers who have recently become free to choose their electricity provider, we examine two types of information provision with the same economic incentives but different framing: <em>information on the Past -</em> information about historical changes in electricity bills for the average household of their electricity provider–and <em>information on Others -</em> information about differences in electricity bills for the average household of different electricity providers. We collected objective measures of household electricity consumption levels through meter readings and subjective measures of behavioral changes through a questionnaire. Our results show that <em>information on the Past</em> has more impact on reducing electricity consumption for households with a higher volume of electricity consumption than others. The channels for this reduction are the behaviors of “not leaving the air conditioner on,” a constant time-consuming behavior, and “lowering the refrigerator's internal temperature,” a hassle-free one-time behavior. <em>Information on the Past</em> can be a low-cost and proactive information-provision measure for non-profit organizations and local governments.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"12 ","pages":"Article 100195"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144518047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Next-day average wind speed prediction in Taiwan: A comparison of neural network and hybrid wavelet-neural network models","authors":"Seemant Tiwari,&nbsp;Jeeng-Min Ling","doi":"10.1016/j.cles.2025.100214","DOIUrl":"10.1016/j.cles.2025.100214","url":null,"abstract":"<div><div>One growing renewable energy resource that is crucial to the shift to a more sustainable energy system is wind energy. One of the most significant issues affecting wind energy is variation in its production. Planning and operating a wind energy station requires the use of wind speed prediction techniques, which are challenging due to the dynamic nature of wind and the impact of regional variables. Nevertheless, current prediction techniques face substantial difficulties in achieving long-term nonlinear prediction accuracy due to the complexity of wind speed data, resulting in a deficiency of wind energy projections that may lead to erroneous energy distributions. To address the prediction issues, this research proposes a hybrid approach for average wind speed models that combines the Wavelet Transform (WT) and Neural Network (NN) techniques. The next-day forecast of time series data in Taiwan is assessed in this research using an uncertainty metric related to average wind speeds. Additionally, this study compares the deep learning-based Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Seasonal Auto-Regression Integrated Moving Average (SARIMA) approaches. A hybrid strategy that combines WT with LSTM and WT with GRU. The WT-NN model outperforms the other models according to the results of the suggested strategy. The effectiveness of the suggested WT-NN is assessed in comparison to the different models. These findings demonstrate the effectiveness of WT-NN in enhancing the accuracy of wind speed prediction. The study's suggested approach may help predict wind speed and wind energy production.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"12 ","pages":"Article 100214"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-based approach for PV energy forecasting for mono-Si, poly-Si and a-Si Grid-connected PV systems","authors":"Abdellatif Ait-Mansour,&nbsp;Amine Tilioua","doi":"10.1016/j.cles.2025.100217","DOIUrl":"10.1016/j.cles.2025.100217","url":null,"abstract":"<div><div>The growing global energy demand and the urgent need to reduce greenhouse gas emissions have intensified the search for renewable and sustainable energy sources. Among these, photovoltaic (PV) systems have emerged as a promising solution due to their long lifespan, low maintenance costs, and ability to operate under diverse climatic conditions. However, the intermittent nature of solar energy remains a major challenge for stable integration into electrical grids, especially in semi-desert regions. Despite existing research on PV performance, limited studies have focused on the comparative forecasting of different silicon-based PV technologies using advanced machine learning models in such environments. This study aims to forecast and compare the energy performance of grid-connected monocrystalline silicon, polycrystalline silicon, and amorphous silicon PV systems operating in a semi-desert region of Morocco. Using two years of real measured daily meteorological and energy production data (January 2021 to December 2022), we developed predictive models based on Random Forest and Deep Neural Networks. The models' accuracy was evaluated using multiple error metrics including mean squared error, mean absolute percentage error, mean absolute error, maximum error, and the coefficient of determination. The results demonstrate high predictive accuracy for both models, with amorphous silicon technology showing superior performance, achieving a coefficient of determination of 98.6 % for Random Forest and 98.3 %t for Deep Neural Networks. The MAPEs for amorphous silicon were 8.2 % for Random Forest and 18.7 % for Deep Neural Networks. Monocrystalline silicon achieved 98.5 % and 98.0 % for the coefficient of determination, with MAPEs of 9.3 %t and 20.4 % for Random Forest and Deep Neural Networks, respectively. For polycrystalline silicon, the coefficients of determination were 98.3 % and 98.1 %, with MAPEs of 9.1 % and 24.1 %, respectively. These findings highlight the effectiveness of machine learning models for accurate PV energy forecasting and underline the potential advantages of amorphous silicon technology in semi-desert climates</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"12 ","pages":"Article 100217"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}