Energy Storage and Saving最新文献

{"title":"Optimization of renewable energy management using machine learning and genetic algorithms","authors":"Afeez Showole,&nbsp;Muhammad Shahwaiz Afaqui","doi":"10.1016/j.enss.2025.06.007","DOIUrl":"10.1016/j.enss.2025.06.007","url":null,"abstract":"<div><div>This study aims to develop an optimized energy management system (EMS) that effectively integrates photovoltaic (PV) energy and load forecasting with intelligent control strategies to reduce energy costs and grid dependency. The increasing integration of intermittent renewable energy sources, particularly solar power, into energy systems poses challenges in maintaining the supply demand balance and minimizing energy waste. Without advanced control, substantial portions of the generated energy are lost and the energy demand during peak hours remains unmet. This study employs machine learning models namely random forest for PV forecasting and linear regression for load prediction, to enable accurate energy forecasting. These forecasts are embedded into a genetic algorithm (GA)-optimized EMS framework that dynamically manages battery usage, load shifting, and grid interaction. Simulations were conducted to evaluate the system performance under real-world conditions. The optimized EMS reduced the grid dependency by 39.29% and decreased the energy costs by 35.14%. It effectively utilized surplus PV energy, minimized energy waste, and improved the battery and grid coordination. Without the EMS, 40% of the energy is wasted, and 45.45% of the demand remains unmet during peak periods. The results demonstrated the potential of intelligent EMS frameworks to enhance energy efficiency, ensure supply reliability, and support sustainability goals. The model is scalable and adaptable to various energy environments, including smart grids and decentralized systems. By integrating accurate forecasting with GA-based control, the proposed EMS offers a practical and cost-effective solution for optimizing renewable energy management, reducing reliance on non-renewable sources, and promoting energy self-sufficiency.</div></div>","PeriodicalId":100472,"journal":{"name":"Energy Storage and Saving","volume":"5 2","pages":"Article 100162"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148573036","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":"An optimal sizing framework for renewable energy microgrids considering stationary batteries & electric vehicles as mobile energy storage systems: Case study in Morocco","authors":"Oumaima Mahir,&nbsp;Oussama Ziadi,&nbsp;Abdelilah Rochd,&nbsp;Hicham Ghennioui,&nbsp;Bouthaina El barkouki,&nbsp;Mohamed Laamim","doi":"10.1016/j.enss.2025.07.007","DOIUrl":"10.1016/j.enss.2025.07.007","url":null,"abstract":"<div><div>The intermittent nature of renewable energy sources poses a significant challenge to the stability of energy systems. This challenge is exacerbated by the increasing global demand and low-carbon transition requirements. This paper introduces the development of a novel framework for the optimal sizing and dispatch of energy in a grid-connected microgrid. The proposed model incorporates solar photovoltaic (PV), stationary energy storage systems, and mobile storage systems utilizing vehicle-to-grid technology. A mixed integer linear programming algorithm was implemented using the open energy modeling framework to minimize system costs and emissions while maintaining reliability and improving efficiency. The framework was validated through a case study applied to a building of the green energy park research platform. The results demonstrate a competitive levelized cost of energy (LCOE) of approximately 0.85 MAD·kWh⁻¹, with a renewable energy share of approximately 96%. Hybrid storage inclusion significantly decreases dependence on the grid, enhances energy self-sufficiency, and minimizes the total annual cost (TAC) to 441,403.81 MAD. The inclusion of hybrid storage significantly reduced grid dependence, enhanced energy self-sufficiency, and lowered TAC to 441,403.81 MAD. It also contributes to system flexibility, with electric vehicle batteries supplying 6.6% of total energy production. Furthermore, the sensitivity analysis indicates that projected cost declines in PV systems and batteries could further reduce the LCOE to 0.5 MAD·kWh⁻¹ by 2030 and 0.3 MAD·kWh⁻¹ by 2050. These results underline the economic viability and environmental durability of the proposed microgrid design. This study supports the development of hybrid energy storage systems. This also contributes to the development of more efficient and sustainable energy solutions.</div></div>","PeriodicalId":100472,"journal":{"name":"Energy Storage and Saving","volume":"5 2","pages":"Article 100165"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148573037","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":"Publisher's Note: Introducing article numbering to Energy Storage and Saving","authors":"","doi":"10.1016/S2772-6835(26)00060-9","DOIUrl":"10.1016/S2772-6835(26)00060-9","url":null,"abstract":"","PeriodicalId":100472,"journal":{"name":"Energy Storage and Saving","volume":"5 2","pages":"Article 100183"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149153503","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":"Study of the optimal model and parameters of the Th–U cycle nuclei","authors":"Abel B. Olorunsola,&nbsp;Olumide O. Ige,&nbsp;Mathew O. Ayemowa,&nbsp;Emmanuel J. Adoyi,&nbsp;Tajudeen O. Adeeko,&nbsp;Damilola S. Samson","doi":"10.1016/j.enss.2025.09.001","DOIUrl":"10.1016/j.enss.2025.09.001","url":null,"abstract":"<div><div>Recent developments in the advanced reactor concept have renewed interest in the Th–U fuel cycle. This interest stems from its potential advantages over the traditional U–Pu fuel cycle, particularly in mitigating the major health concerns associated with long-lived nuclear waste. Unfortunately, thorium fuel has been suffering from the quality of nuclear data for the relevant isotopes as a result of inadequate attention previously given to the thorium fuel cycle. In some cases, the nuclear reaction data uncertainties are larger than the target accuracies required by the nuclear plant designer. This study provides an improved and accurate cross-section of ²³²Th, ^231,232Pa, and ^233,235U, which play vital roles in the ²³²Th–²³³U fuel cycles. Statistical modeling code (EMPIRE 3.2.3) was used with a combination of phenomenological models to obtain cross-sections to fit the available experimental data and the existing evaluated library. If reliable predictions of the reaction cross-sections are to be realized, the fission cross-section at lower energies remains a region for improvement. However, the cross-sections were predicted at a lower energy, where the experiments are difficult to conduct. This supports the need for an improved cross-section of isotopes that are relevant to the Th–U fuel cycle.</div></div>","PeriodicalId":100472,"journal":{"name":"Energy Storage and Saving","volume":"5 2","pages":"Article 100140"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148573033","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":"Green innovations and clean energy: a regional exploration of their dual impact on global ecological sustainability","authors":"Abdul Salam Maftoon,&nbsp;Kiran Afzal,&nbsp;Abdul Razzaq,&nbsp;Wasim Abbas Shaheen","doi":"10.1016/j.enss.2025.05.011","DOIUrl":"10.1016/j.enss.2025.05.011","url":null,"abstract":"<div><div>This study investigates the impact of green technology innovation (GTI) and renewable energy consumption (REC) on the ecological footprint (EFP) of 126 countries over the period 2001–2022, employing a dynamic panel data approach using the two-step system generalized method of moments (GMM) estimation. By conducting a cross-regional analysis covering seven global regions, this study revealed significant heterogeneity in the environmental outcomes of green technological efforts. The findings reveal that, while GTI fosters technological advancement, it may paradoxically increase EFP through the rebound effect, particularly in rapidly industrializing economies. Conversely, REC consistently contributes to reducing EFP by promoting a transition to cleaner energy sources. These results underscore the need for region-specific policy strategies to maximize the ecological benefits of green technologies. Achieving sustainable development requires a balanced integration of technological innovation and renewable energy adoption tailored to the regional context. This study distinguishes itself by integrating a dynamic panel approach with cross-regional perspectives, offering nuanced policy insights to advance global environmental sustainability.</div></div>","PeriodicalId":100472,"journal":{"name":"Energy Storage and Saving","volume":"5 2","pages":"Article 100139"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148573031","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":"Multi-objective optimal sizing of EV charging stations and PV systems in residential buildings","authors":"Abdelhak Borhani,&nbsp;Hamid Ouadi","doi":"10.1016/j.enss.2025.07.002","DOIUrl":"10.1016/j.enss.2025.07.002","url":null,"abstract":"<div><div>Rapid growth of the electric vehicle (EV) market has made the widespread development of residential charging stations inevitable. Simultaneously, integrating a photovoltaic (PV) system into a residential parking facility not only reduces charging-related electricity costs but also alleviates stress on the main power grid. In this study, a new sizing strategy was developed to optimize both EV charging stations and PV systems for residential parking, addressing the critical challenge of aligning the infrastructure supply with the evolving demand for EV charging. A multi-objective optimization approach was employed: (1) to minimize the combined energy bill and investment costs; (2) to quantify and reduce the mismatch between EV power demand and charging station supply, which was a novel metric introduced as part of this work; (3) to enhance the quality of service (QoS) for EV users. Specific residential constraints, including limited parking space and contracted power capacity, are also considered. To solve the complex multi-objective problem, a metaheuristic method was used, and clustering analysis was applied to support stakeholder decision making. The proposed strategy was evaluated using a QoS-constrained sizing optimization method. It achieves approximately 27% lower energy and infrastructure costs. In addition, significant reductions in global greenhouse gas emissions were obtained owing to PV integration, amounting to a 31% decrease compared to the reference method. Furthermore, a more strategic charger allocation was enabled by combining slow and fast chargers to improve charging efficiency and user convenience.</div></div>","PeriodicalId":100472,"journal":{"name":"Energy Storage and Saving","volume":"5 2","pages":"Article 100141"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148573034","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":"Effect of cool and thermal paints on the cooling and heating loads in various climates","authors":"Saeed Golkar,&nbsp;Mehdi Baneshi,&nbsp;Amirhossein Fathi","doi":"10.1016/j.enss.2025.05.007","DOIUrl":"10.1016/j.enss.2025.05.007","url":null,"abstract":"<div><div>The influence of cool and thermal paints on the cooling and heating loads of an office building was investigated under three distinct four-season climatic conditions at different geographic latitudes. It has been observed that reductions in one type of thermal load may be accompanied by compensatory increases in another. Cool paints have been found to be particularly effective in decreasing cooling energy demands in warmer climates, whereas the application of thermal paints to interior surfaces has been shown to enhance the heating efficiency in colder regions. The combined utilization of both types of paint is proposed as an adaptable and climate-responsive strategy for improving overall building energy performance, with considerable implications for sustainable architectural practices. To assess the effectiveness of these strategies, an engineering-based simulation model was developed and implemented using EnergyPlus software. The model was applied to an eight-story ABFA administrative building in Shiraz, Iran. Furthermore, the same building configuration was modeled for Tabriz (cold and dry climate) and Ahvaz (extremely hot climate). Multiple scenarios involving various applications of cool and thermal paints were examined. In Tabriz, the optimal scenario resulted in a 3.56% reduction in the total annual energy load, corresponding to 3.22 kWh·m⁻²·yr⁻¹. For Shiraz, the most effective configuration achieved a 5.0% reduction (4.15 kWh·m⁻²·yr⁻¹). In Ahvaz, a 3.6% decrease in total energy demand (3.21 kWh·m⁻²·yr⁻¹) was attained through the implementation of cool paint on the building’s exterior envelope. These findings underscore the context-dependent performance of paint-based passive strategies in enhancing energy efficiency across different climatic zones.</div></div>","PeriodicalId":100472,"journal":{"name":"Energy Storage and Saving","volume":"5 2","pages":"Article 100132"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148573038","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":"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}