Energy Storage and Saving最新文献

{"title":"Communication-efficient vertical federated k-means for enterprise energy consumption profiling","authors":"Xin Chen,&nbsp;Xuejiao Zhong,&nbsp;Shaohui Xu,&nbsp;Linwei Liu,&nbsp;Xinyuan Guo","doi":"10.1016/j.enss.2025.04.005","DOIUrl":"10.1016/j.enss.2025.04.005","url":null,"abstract":"<div><div>With the continuous growth of global energy consumption and big data, the research on energy consumption profiles has grown in both academia and industry. While many studies have explored power data for user profiling, research on applying it to enterprise profiling is still limited. Federated learning offers a way to mine power data while protecting the privacy of both grids and enterprises. However, the classic vertical federated (VF) k-means algorithm suffers from inefficiency and high communication overhead. To address these issues, we propose the communication-efficient vertical federated (CeVF) k-means algorithm. In this approach, each participant conducts multiple local iterations to reduce communication with the coordinator and uploads only the clustering results rather than distance matrices. The coordinator then merges these results using a maximum intersection method. Experiments on real power grid and enterprise datasets show that our algorithm maintains clustering performance while reducing communication by approximately 75%.</div></div>","PeriodicalId":100472,"journal":{"name":"Energy Storage and Saving","volume":"5 2","pages":"Article 100137"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148572695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exhaust heat utilization of a household generator engine for refrigeration purposes: case study","authors":"Maral M. Hussein ,&nbsp;Khalaf I. Hamada ,&nbsp;Omer K. Ahmed","doi":"10.1016/j.enss.2025.05.009","DOIUrl":"10.1016/j.enss.2025.05.009","url":null,"abstract":"<div><div>The exhaust gas heat of an internal combustion engine (ICE) can potentially yield the cooling effect of a diffusion absorption refrigerator (DAR). One of the electricity generators in rural areas is a significant source of exhaust waste heat. The exhaust waste heat recuperation of the ICE of a domestic electricity generator was experimentally assessed to operate a DAR system. The experimental assembly consists of a set of household generator engines and a waste gas heat exchanger to operate the absorption test system, which utilizes an Electrolux refrigerator (RM212, F). The system was tested under various operating conditions, based on the engine loading ratios (i.e., 0%, 30%, 60%, and 90% of the total load of the test engine), as well as the operating condition of the electrical resistance (electric heater). The steady-state results of the exhaust gases’ temperatures ranged between 400 °C and 500 °C at load conditions of 0% to 60%, which are suitable for running the DAR system. The evaporator surface temperatures decreased in all operating cases except for the case (90%), where a negative effect appeared owing to the high thermal energy in the engine exhaust gas. Accordingly, an optimised heat source was adopted, bypassing part of the exhaust stream under this loading condition (90% with improvement) to avoid overheating of the refrigerant fluid. Furthermore, the lowest surface temperature of the evaporator in the absorption system was recorded as −1.1 °C at a load of 30%. On the other hand, the highest value of the system performance coefficient was recorded as 0.0953 under the load-high condition with an optimized heat source (90% with improvement). Based on the positive cooling effect of the tested system, it can be adopted as supplementary equipment for refrigeration purposes in rural areas with energy-saving prospects.</div></div>","PeriodicalId":100472,"journal":{"name":"Energy Storage and Saving","volume":"5 1","pages":"Pages 27-38"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147703792","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":"Advanced optimization strategies for resilient and cost-efficient hydrogen-based AC/DC microgrids with integrated RES and BESS","authors":"Radharaman Shaha ,&nbsp;Chetan Jambhulkar ,&nbsp;Umesh Hiwase ,&nbsp;Lata Gidwani","doi":"10.1016/j.enss.2025.05.010","DOIUrl":"10.1016/j.enss.2025.05.010","url":null,"abstract":"<div><div>Currently, renewable energy sources (RESs) are crucial for advancing sustainable and resilient energy systems owing to their integration into alternating current and direct current microgrids. However, the current approaches face limitations when it comes to suboptimal topology designs, inefficient resource sizing, and limited adaptability under dynamic conditions. These constraints limit cost efficiency, energy efficiency, and resiliency, particularly when coupled with emerging sectors such as hydrogen-powered transportation. To address these challenges, this framework develops an innovative, multifaceted approach based on advanced artificial intelligence and optimization techniques. The first is the use of a graph convolutional neural network (GCNN) with stochastic optimization to obtain robust and efficient layouts that integrate RES, battery energy storage system (BESS), and hydrogen electrolyzers, allowing transportation sector coupling. It ensures the optimal sizing of hydrogen electrolyzers and BESS, based on the criteria of balancing capital expenditure, operational expenditure, and energy efficiency, using a multi-objective mixed integer linear programming approach. Second, it proposes a hierarchical model predictive control strategy that utilizes hybrid long short-term memory–Gaussian process forecasts for real-time, multi-time-scale operational optimization of energy dispatch, along with hydrogen production. Finally, a deep reinforcement learning model with proximal policy optimization ensures adaptive and fault-tolerant energy management by incorporating resiliency metrics into the learning process. The proposed framework brings about the following significant advancements: an approximately 25% reduction in power losses, around 30% faster outage recovery, hydrogen production costs of 2–3 $·kg⁻¹, and renewable utilization rates near 95%. It further supports cost and energy efficiency and creates a more resilient blueprint that sets up a promising integration of RES, BESS, and hydrogen technologies into the future towards sustainability.</div></div>","PeriodicalId":100472,"journal":{"name":"Energy Storage and Saving","volume":"5 1","pages":"Pages 3-15"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147658250","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":"Improvement in energy conservation and thermal stability of a refrigerator through integration of phase change materials: a CFD analysis","authors":"Mahbubul Muttakin,&nbsp;Farzana Akter,&nbsp;Salim Subah,&nbsp;Md Nurul Momen Chowdhury,&nbsp;Mohammad Rejaul Haque","doi":"10.1016/j.enss.2025.04.004","DOIUrl":"10.1016/j.enss.2025.04.004","url":null,"abstract":"<div><div>Reducing the energy consumption of domestic refrigeration appliances has been a key research focus over the past decade. This study presents an innovative approach that integrates phase change materials (PCMs) in household refrigerators to enhance energy efficiency while optimizing storage space utilization. Unlike prior studies, this study comprehensively analyzes the combined effects of PCM placement, five types of PCM, and distribution, filling a critical gap in the literature. A combined vertical and horizontal PCM configuration was implemented, and its impact on the temperature distribution inside the compartments was analyzed using computational fluid dynamics (CFD) simulations in ANSYS Fluent. This study evaluated multiple PCM materials, including water, eutectic solutions and commercial PCMs such as caproic acid, E3, and puretemp-2, to identify the most effective materials for thermal stability. This study further explored the impact of various PCMs, revealing diverse cooling patterns and identifying caproic acid and E3 as superior choices for stabilizing temperatures within compartments. E3 displayed a slightly better result than caproic acid. Conversely, water as a PCM performed poorly. A constant PCM volume was considered for each case, taking only 6.6% of the compartment’s volume. Notably, the results demonstrate a maximum temperature reduction of 8.1% in the top compartment and 5.1% in the bottom compartment during compressor off-cycles, resulting in a 42% reduction in refrigeration load. These findings highlight the potential of PCM integration as a viable approach for enhancing refrigerator energy efficiency while maintaining optimal cooling performance.</div></div>","PeriodicalId":100472,"journal":{"name":"Energy Storage and Saving","volume":"5 1","pages":"Pages 57-71"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147749625","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":"Impact of OMEx e-fuels for CO2 emissions abatement in light-duty commercial vehicles","authors":"Antonio Foglia ,&nbsp;Pierpaolo Polverino ,&nbsp;Ivan Arsie ,&nbsp;Cesare Pianese","doi":"10.1016/j.enss.2025.06.001","DOIUrl":"10.1016/j.enss.2025.06.001","url":null,"abstract":"<div><div>E-fuels, such as oxymethylene dimethyl ethers (OME_x), represent a promising alternative to conventional fuels to reduce engine emissions and extend their durability. They are highly oxygenated fuels that allow emissions reduction because of smoke-free combustion. Recent studies have highlighted that adopting scaled injection systems can overcome their lower energy content. The aim of this work is to provide a preliminary well-to-wheel analysis by merging the most recent literature findings concerning well-to-tank with a detailed tank-to-wheel assessment. The analysis considers different OME_x types and diesel/OME_x blends to compute energy demand, fuel consumption, and carbon dioxide emissions of a delivery van subject to a given driving cycle. From literature evidence, the average improvement in engine efficiency attained by using pure OME_x fuels has been estimated to be approximately 4.5% compared to pure diesel, with greater values for short-chain OME_x (e.g., up to 10%). From the obtained results, a reduction in tank energy requirement was observed, with a minimum of −10% for pure OME₂. However, fuel consumption increases because of OME_x lower energy content, showing a growth ranging from 74% to 140% in mass and from 65% to 86% in volume with increasing chain length of pure OME_x. The variation in CO₂ emissions is not univocal, since a reduction of almost −4% for pure OME₁ and an increase above 20% for pure OME₄ were achieved. The obtained results highlight how OME_x with shorter chains can lead to greater efficiencies compared to pure diesel, with a slight reduction in CO₂ emissions.</div></div>","PeriodicalId":100472,"journal":{"name":"Energy Storage and Saving","volume":"5 1","pages":"Pages 72-83"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147750802","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":"Monetization of life-cycle based environmental impacts: a case study on external environmental costs of steel-based wear parts for biogas plants","authors":"Gerald Feichtinger,&nbsp;Wolfgang Posch","doi":"10.1016/j.enss.2025.05.003","DOIUrl":"10.1016/j.enss.2025.05.003","url":null,"abstract":"<div><div>This study investigates the external environmental costs (EEC) of steel-based wear-parts used in biogas plants by monetizing the life-cycle-based environmental impacts caused throughout the entire life cycle. A simplified cradle-to-grave based environmental life cycle assessment was conducted using the commonly applied ReCiPe assessment method. Two monetization approaches were used to comparatively calculate the EEC: recently published TruePrice factors and traditional ReCiPe-based factors. The analysis revealed that EEC account for 8% to 14% of total production costs, indicating significant additional expenses. These costs are primarily driven by the steel used in the components. Sensitivity analysis identified climate change, fossil depletion, and fine particulate matter formation as the most influential impact categories. A 5% increase in the monetization factors resulted in a 1.6% rise in overall EEC. The findings demonstrate that the choice of monetization method significantly influences the estimated EEC. Furthermore, the use of more sustainable steel offers the greatest potential for reducing both environmental and economic impacts associated with steel-based wear parts.</div></div>","PeriodicalId":100472,"journal":{"name":"Energy Storage and Saving","volume":"5 1","pages":"Pages 39-48"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147750804","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":"Battery sizing and performance evaluation of battery electric vehicles","authors":"Hari Maghfiroh ,&nbsp;Oyas Wahyunggoro ,&nbsp;Adha Imam Cahyadi","doi":"10.1016/j.enss.2025.05.008","DOIUrl":"10.1016/j.enss.2025.05.008","url":null,"abstract":"<div><div>The adoption of electric vehicles (EVs) is a promising solution to reduce air pollution. Batteries serve as the primary energy storage system (ESS) in EVs, and their sizing significantly affects their overall performance. A straightforward method to determine the battery energy size is to integrate the power requirements over a given drive cycle. However, a key challenge arises because of the interdependence between battery energy size and battery mass, which requires an optimization approach. To address this issue, this study proposes an optimized battery sizing method using particle swarm optimization (PSO), selected for its simplicity and computational efficiency. A key novelty lies in the incorporation of traction motor dynamics via a transfer function model, which enables realistic performance estimation during optimization. The proposed method is validated against a conventional iterative sizing approach using both the worldwide harmonized light vehicle test cycle (WLTC) and a real driving cycle (RDC). The results show that PSO reduces the battery size by 4.67%, battery mass by 4.76%, and total cost by 4.75%. While the iterative method achieves a higher driving performance owing to a larger battery, the PSO-sized battery still achieves 224.5 km of autonomy and 10.37 years of estimated battery life under WLTC conditions. In RDC testing, the PSO method yields 270.2 km of autonomy and a 22.38-year battery life. These results demonstrate that PSO enables lightweight and cost-effective battery sizing while maintaining acceptable EVs performance. Future work will focus on multi-objective optimization to better balance costs, energy, and endurance.</div></div>","PeriodicalId":100472,"journal":{"name":"Energy Storage and Saving","volume":"5 1","pages":"Pages 49-56"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147750803","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":"Smart grid cybersecurity: anomaly detection in solar power systems using deep learning","authors":"Jagbir Singh ,&nbsp;Owais Ahmad Shah ,&nbsp;Sujata Arora","doi":"10.1016/j.enss.2025.04.001","DOIUrl":"10.1016/j.enss.2025.04.001","url":null,"abstract":"<div><div>This study explores the application of deep learning techniques, specifically long short-term memory (LSTM) networks, to detect anomalies in solar power production within a smart grid framework. Given the increasing integration of renewable energy sources such as solar power into electrical grids, ensuring cybersecurity and operational stability has become a critical challenge. The LSTM model was developed to capture the temporal dependencies in energy production data, demonstrating its ability to accurately predict production values and identify significant deviations. The model achieved a mean squared error (MSE) of 0.846 with a precision of 79%, although it showed a relatively lower recall of 33%, indicating the need for further optimization to enhance sensitivity. In a comparative analysis, the LSTM model was evaluated against a feedforward neural network (FFNN) and traditional isolation forest model. The FFNN outperformed the LSTM in key metrics, including accuracy, precision, recall, F1-score, and area under the curve (AUC)-receiver operating characteristic (ROC), highlighting its robustness in anomaly detection tasks. Conversely, the isolation forest model demonstrated a limited effectiveness. Despite the potential of the LSTM model, these results suggest that FFNNs currently provide a more reliable solution for detecting anomalies in solar power systems. The study also emphasized the importance of real-time anomaly detection and its implications for grid stability and reliability. By integrating advanced deep learning models into monitoring systems, operators can proactively manage and mitigate the impact of anomalies, thereby ensuring a stable power supply.</div></div>","PeriodicalId":100472,"journal":{"name":"Energy Storage and Saving","volume":"5 1","pages":"Pages 16-26"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147658248","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":"Energy storage and saving technologies: a review on SDEWES 2023 special issue","authors":"Wenxiao Chu ,&nbsp;Neven Duić ,&nbsp;Qiuwang Wang","doi":"10.1016/j.enss.2025.08.001","DOIUrl":"10.1016/j.enss.2025.08.001","url":null,"abstract":"<div><div>The 18th Conference on Sustainable Energy, Water and Environmental Systems (SDEWES) served as a global meeting point for scientists, engineers, and decision-makers to exchange ideas on integrated resource management. This editorial introduces the curated special-issue articles, distilling their contributions and mapping their intersections across energy, water, and environmental systems. Rather than offering an exhaustive survey, it foregrounds the questions and debates that emerge from the collected work, situating them within current scholarly and policy conversations. By highlighting recurring motifs, including technological innovation, life-cycle assessment, and governance frameworks, the piece clarifies the journal’s perspective on pressing sustainability dilemmas and points readers toward actionable insights.</div></div>","PeriodicalId":100472,"journal":{"name":"Energy Storage and Saving","volume":"5 1","pages":"Pages 1-2"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147551827","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":"Thermal management of electric vehicle batteries using multiple PCMs","authors":"Esra Ahmed Khodadad,&nbsp;Bashir Eskander Kareem,&nbsp;Darawan Bazyan Dhahir,&nbsp;Ahmed Mohammed Adham","doi":"10.1016/j.enss.2025.03.004","DOIUrl":"10.1016/j.enss.2025.03.004","url":null,"abstract":"<div><div>Electric car batteries experience high discharge rates that can result in elevated temperatures. To prevent thermal runaway and ensure safe operation, it is crucial to effectively manage and maintain the battery temperature within its optimal operating range. The use of paraffin-based phase change materials (PCMs) for cooling lithium-ion batteries is an effective passive cooling method. PCMs are highly effective for thermal energy storage owing to their high heat-storage capacity. However, their limited thermal conductivity restricts their effectiveness in thermal-management applications. This study conducted a numerical investigation into the thermal management of 18650 lithium-ion batteries using multiple PCMs, employing the commercial software ANSYS Fluent. The analysis involved uniform heat dissipation from the center of a cylindrical battery, which was submerged in a paraffin-based PCM and enclosed within a metal housing. A 2D model was used to reduce the computational costs by leveraging the geometric symmetry of the system. During battery discharge, individual PCMs with melting points of 35, 44, and 54 °C fully melt at 1000, 4000, and 7000 s, respectively. In contrast, when using multiple PCMs with melting points of 38, 42, 44, 47 °C, and 50 °C, complete melting occurred at 1,900, 2,000, 2,750, 3,050, and 3,500 s, respectively, for the same total mass. The temperature difference between the top and bottom of the battery was up to 6 °C for a single PCM, but this difference was significantly reduced by using multiple PCMs.</div></div>","PeriodicalId":100472,"journal":{"name":"Energy Storage and Saving","volume":"4 4","pages":"Pages 404-412"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841268","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}