Energy Reports最新文献

{"title":"Application of novel chemical regimen in a thermal power plants’ cooling tower","authors":"Maryam Chinisaz ,&nbsp;Majid Soleimani ,&nbsp;Mohsen Esmaeilpour ,&nbsp;Majid Ghahraman Afshar","doi":"10.1016/j.egyr.2025.109010","DOIUrl":"10.1016/j.egyr.2025.109010","url":null,"abstract":"<div><div>The effect of chemical regimen on the corrosion and sedimentation rate of cooling tower of thermal power plant is investigated here. The subjected power plant has four wet cooling towers and the quality of raw water (well water) decreases drastically in recent years. Therefore, the clarifier process is incapable to provide suitable soft water. As a result, controlling corrosion and sedimentation with the applied chemical regimen is impossible due to the high alkalinity and hardness level of clarified water. Considering the lack of clarifier efficiency, a chemical regimen is proposed based on the elimination of clarifier processes and use of anti-corrosions, antiscalants and organic biocides. For this purpose, corrosion and scale coupon, microbial monitoring, chemical and physicochemical water analysis are applied to evaluate the effect of proposed regimen on the condenser and cooling tower performance. All mentioned parameters are examined in both main (new regimen) and control (previous regimen) unit. In both regimens, the bleach injection is performed continuously. However, TBC level is obtained 10³-10⁴ and 0–100 cfu/ml for previous and new regimen, respectively. The corrosion rate of the condenser is reduced by 6 times with application of anti-corrosion. Furthermore, destruction rate of wooden structure of cooling tower in previous regimen is 1.5 times higher than new regimen. Moreover, the sedimentation rate of routine regimen is 6 times higher than new regimen due to use of antiscalants. The overall results prove that corrosion and sedimentation rates decrease dramatically with the elimination of clarifier processes and applying new regimen.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 109010"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Disaggregation of household energy consumption with principal component analysis and sparse filtering","authors":"Stylianos Loizidis ,&nbsp;Fanourios Kourougianni ,&nbsp;Andreas V. Olympios ,&nbsp;Andreas Kyprianou ,&nbsp;George E. Georghiou","doi":"10.1016/j.egyr.2025.12.031","DOIUrl":"10.1016/j.egyr.2025.12.031","url":null,"abstract":"<div><div>A detailed understanding of household electricity consumption across heating, cooling, and baseline electrical loads is critical for effective demand-side energy management and the development of targeted energy efficiency strategies. This paper presents two methodologies—Principal Component Analysis (PCA) and Sparse Filtering—for disaggregating total daily electricity consumption into baseline electricity, heating, and cooling loads, using data collected from 68 households in Cyprus over one year. PCA, a well-established technique for dimensionality reduction, identifies two dominant components explaining 52.27% and 14.83% of the total variance, attributed primarily to cooling and heating, respectively. Sparse Filtering, in contrast, produces two distinct temporally localized features: one active in winter, the other in summer. This suggests a disentangled, seasonally aligned representation of energy use. While PCA emphasizes global variance, Sparse Filtering captures household-specific patterns – even when PCA components are near zero or negative – highlighting periods of low consumption potentially attributable to base electrical loads. These findings demonstrate the potential of Sparse Filtering to offer more interpretable and granular insights into occupant energy consumption than traditional PCA-based approaches. The complementary strengths of the two approaches improve the interpretability of disaggregated household load profiles and increase the robustness of unsupervised energy disaggregation methods.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 108894"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Probabilistic optimal power flow analysis for optimal deployment of Unified power flow controller in renewable integrated hydro power systems using marine predators algorithm","authors":"Basudeb Mondal ,&nbsp;Soumen Biswas ,&nbsp;Susanta Dutta ,&nbsp;Anagha Bhattacharya ,&nbsp;Sajjan Kumar ,&nbsp;Soham Dutta","doi":"10.1016/j.egyr.2025.12.036","DOIUrl":"10.1016/j.egyr.2025.12.036","url":null,"abstract":"<div><div>The optimal integration of renewable energy sources into power systems is a challenging but extremely important task for cost reduction to achieve environmental sustainability. To address the challenges of probabilistic optimal power flow problems, this study proposes a novel marine predators algorithm as an optimization tool. The proposed method used to solving the probabilistic optimal power flow problem on conjunction with a unified power flow controller, incorporating the behavior of wind, PV, and small hydro generation. The proposed framework models the variability of renewable resources using appropriate probability density functions like weibull distribution for wind speeds, lognormal distribution for solar irradiance, and gumbel distribution for water flow rates. The simulation results highlight the efficiency and robustness of marine predators algorithm in solving single-objective optimal power flow problems. The findings show that 5112.5 (<span><math><mrow><mi>$</mi><mo>/</mo><mi>h</mi></mrow></math></span>) and 1.6823 (t/h) is the ideal fuel cost &amp; emission when considering thermal generators alone; when incorporating renewable energy with thermal, the total cost is 4808 (<span><math><mrow><mi>$</mi><mo>/</mo><mi>h</mi></mrow></math></span>) and the emission is 1.5131 (t/h). When RES with UPFC both are included, fuel costs 4785.3 (<span><math><mrow><mi>$</mi><mo>/</mo><mi>h</mi></mrow></math></span>) and emissions are 1.469 (t/h). For research validation, selected conventional generators in the IEEE 57-bus system are replaced with renewable sources. Furthermore, the proposed method enhances the operational efficiency of renewable sources and small hydro systems when integrated with flexible AC transmission system devices like unified power flow controller. Performance evaluation on the IEEE 57-bus test system shows that the proposed approach delivers highly competitive results compared to other optimization.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 108899"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145789019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design of a modular and heterogeneous electrolysis system to increase flexibility in the use case of direct iron reduction","authors":"Hannes Lange,&nbsp;Franziska Marie Meltzner,&nbsp;Michael Große,&nbsp;Isabell Viedt,&nbsp;Anselm Klose,&nbsp;Leon Urbas","doi":"10.1016/j.egyr.2025.11.060","DOIUrl":"10.1016/j.egyr.2025.11.060","url":null,"abstract":"<div><div>2021, Germany produced 40 million tons of crude steel, making it the largest producer in the EU. The steel industry accounted for 55 million tons of CO₂-equivalents, or 7.2% of national greenhouse gas emissions, with about 70% of production relying on conventional blast furnaces. Hydrogen offers a promising pathway to decarbonize this sector. This study investigates how electrolysis systems can be rapidly scaled while maintaining flexibility to adapt to fluctuating electricity prices. A blast furnace simulation model from the literature was extended by integrating a large-scale heterogeneous electrolysis system, and its responsiveness to dynamic electricity costs was assessed. Results show that waste heat integration is insufficient for systems based solely on HTEL. The most efficient configuration was a combination of 40% HTEL and 60% PEMEL, achieving 3.46 MWh per ton of crude steel. A price-driven control principle for electrolysis was successfully implemented and validated with literature data. While Germany remains a key steel producer, countries such as Brazil already achieve more efficient production due to higher renewable shares and lower electricity prices.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 108801"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An XAI-driven CNN-Transformer model for transmission line fault detection and classification","authors":"Tilak Giri ,&nbsp;Bipul Bikram Thapa ,&nbsp;Biplov Paneru ,&nbsp;Bishwash Paneru","doi":"10.1016/j.egyr.2025.108929","DOIUrl":"10.1016/j.egyr.2025.108929","url":null,"abstract":"<div><div>Fault detection in power transmission lines is vital for maintaining the electrical grid stability and reliability. Conventional methods for identifying and classifying faults generally suffer from inefficiencies, inaccuracies, and poor interpretability. This study introduces an Explainable Artificial Intelligence (XAI)-driven approach to enhance fault identification and classification in transmission lines. The study uses MATLAB Simulink simulated data to evaluate and compare the performance of various Machine Learning (ML) models, including eXtreme Gradient Boosting (XGBoost), Extreme Learning Machine (ELM), Long-Short Term Memory (LSTM), Long Short-Term Memory-Gated Recurrent Unit (LSTM-GRU), and Vision Transformer (ViT) against the proposed CNN-Transformer hybrid model. Results demonstrate that the CNN-Transformer hybrid model outperforms alternative models, achieving superior fault detection accuracy across diverse resistance values. The CNN-Transformer model achieved the highest accuracy (0.97), followed by XGBoost (0.93), LSTM-GRU (0.92), LSTM (0.88), ViT (0.93), and ELM (0.87). Additionally, we implement SHAP, an explainable AI framework, to enhance transparency and interpretability in fault diagnosis. The developed methodology supports a mobile application enabling utility operators to monitor and identify faults in real time. This work illustrates ML along with XAI’s potential in power system fault detection, paving the way for intelligent and reliable grid management solutions.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 108929"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-objective optimization of energy management of a series hybrid vehicle on a highway with solar energy and regenerative braking via NSGA-II","authors":"Christiano Hypolithe Ngi Tonye,&nbsp;Romain Kengne Signe,&nbsp;Pierre Gérard Darel Kond Ngue,&nbsp;Frédéric Biya Motto","doi":"10.1016/j.egyr.2025.108995","DOIUrl":"10.1016/j.egyr.2025.108995","url":null,"abstract":"<div><div>This study presents an optimized multi-source energy management strategy for a series hybrid vehicle integrating an internal combustion engine (ICE), battery storage, photovoltaic panels, and regenerative braking. A multi-objective NSGA-II approach is applied to minimize fuel consumption and CO₂ emissions while enhancing system stability. Under optimization, solar production and regenerative braking curves are smoothed and regularized, indicating improved power integration and energy recovery. The battery state of charge (SOC) remains within safe limits, avoiding deep discharges. Fuel consumption drops 20 % (from 6.5 to 5.2 L/100 km), and CO₂ emissions decrease (160→135 g/km). These gains stem from optimal energy distribution among ICE, photovoltaic (PV) generation, regenerative braking, and battery usage. An optimized proportional–integral (PI) controller and dynamic maximum power point tracking (MPPT) contribute to enhanced stability, especially under highway-like Federal Test Procedure 75 (FTP-75) cycle conditions. Thermal and irradiance analyses underscore the need for adaptive MPPT to manage environmental fluctuations. Overall, the framework yields a 20 % efficiency gain over an urban cycle, meeting propulsion constraints.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 108995"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A short-term wind power hybrid forecasting Research based on two-step decomposition and BBO optimized FNETformer-OSRELM deep Learning","authors":"Dan Luo ,&nbsp;Siti Amely Jumaat","doi":"10.1016/j.egyr.2025.108958","DOIUrl":"10.1016/j.egyr.2025.108958","url":null,"abstract":"<div><div>Accurate short-term wind power forecasting is essential for the reliable operation of modern power systems. We propose a hybrid forecasting framework that combines multi-scale decomposition, frequency-domain enhancement, and intelligent optimization. First, a two-step decomposition method combining ICEEMDAN and VMD is applied to extract multi-scale frequency features and alleviate mode mixing. Then, the Pearson correlation coefficient is employed to select features strongly correlated with wind power. Subsequently, a frequency-domain enhanced mechanism (FNet module) replaces the attention mechanism in iTransformer, forming the improved FNETformer model that better captures periodic characteristics. The lightweight OS-RELM model is applied to predict high-frequency components, while FNETformer handles medium- and low-frequency components. Furthermore, BBO algorithm is used to adaptively optimize the hyperparameters of the hybrid model. Taking May as a representative example, comparative experiments from five perspectives show that the proposed model achieves a reduction in RMSE by 4.61%–35.27%, a decrease in MAE by 6.26%–35.07%, and an improvement in <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> by 0.43%–6.01% compared with other benchmark models. Overall, the results demonstrate that the proposed method effectively enhances the model’s adaptability, robustness, and forecasting accuracy.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 108958"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Balancing energy security and carbon emissions in China: The roles of renewable energy and environmental regulation","authors":"Huiquan Huang ,&nbsp;Wenjun Wang ,&nbsp;Guotian Cai","doi":"10.1016/j.egyr.2025.109007","DOIUrl":"10.1016/j.egyr.2025.109007","url":null,"abstract":"<div><div>Although research on energy security (ENS) and carbon emissions (CE) is extensive, few studies have addressed their nonlinear dynamics and spatial spillover effects, or systematically explored the roles of renewable energy development and environmental regulation in shaping this relationship. Using panel data for 30 Chinese provinces from 2008 to 2022, this study adopts a spatial Durbin model to investigate the ENS-CE nexus and employs a multinomial logit model to assess the roles of renewable energy and environmental regulation in such trade-offs. The results indicate significant spatial agglomeration of ENS and CE. ENS exerts a U-shaped direct effect and an inverted U-shaped indirect effect on CE. Renewable energy development helps prevent the simultaneous deterioration of ENS and CE, but does not significantly promote their synergistic improvement. Instead, it increases the likelihood of a trade-off scenario in which CE is reduced at the expense of ENS. Environmental regulation fosters synergy and mitigates the risk of emission reductions due to declining ENS. These findings provide new insights into the complex interplay between energy security and carbon mitigation and offer policy guidance for balancing stability and decarbonisation in China’s energy transition.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 109007"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated optimization of fin geometry and nanoparticle-enhanced PCMs in shell-and-tube thermal storage systems: A CFD–ML framework","authors":"Fatemeh Isania ,&nbsp;Francesca Martelletto ,&nbsp;Luca Doretti ,&nbsp;Paolo Scotton ,&nbsp;Antonio Galgaro","doi":"10.1016/j.egyr.2025.109009","DOIUrl":"10.1016/j.egyr.2025.109009","url":null,"abstract":"<div><div>This study presents a comprehensive numerical and machine learning-based investigation of a shell-and-tube latent heat thermal energy storage (LHTES) system enhanced through the integration of optimized fin geometry and nanoparticle-enriched phase change materials (NEPCMs). Using Docosane as the base PCM, the effects of various nanoparticles — graphene nanoplatelets (GNP), aluminum oxide (Al<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>O<span><math><msub><mrow></mrow><mrow><mn>3</mn></mrow></msub></math></span>), and multi-walled carbon nanotubes (MWCNT) — were evaluated in conjunction with geometric variations in fin number, thickness, length, and material. A detailed computational fluid dynamics (CFD) model was developed using the enthalpy-porosity approach to simulate the phase change process. Results show that increasing fin thickness and length significantly accelerates PCM melting, with 22<!--> <!-->mm-long, 5<!--> <!-->mm-thick copper fins achieving the best performance. Among NEPCMs, MWCNTs at 0.6<!--> <!-->wt% concentration reduced melting time by up to 44% compared to the pure PCM baseline. However, diminishing returns were observed beyond certain fin counts and nanoparticle loadings. To enhance generalizability and design insight, machine learning regression models were trained on CFD data, achieving high prediction accuracy (<span><math><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>=</mo><mn>0</mn><mo>.</mo><mn>99</mn></mrow></math></span>). Feature importance analysis revealed that fin count and thickness had the strongest influence on melting time, followed by nanoparticle type and concentration. The combined CFD–ML framework enabled the development of optimized design guidelines that balance thermal performance, material cost, and system scalability. These findings demonstrate the potential of integrated fin–nano design strategies in advancing the efficiency of compact thermal energy storage systems for applications such as smart buildings, solar thermal systems, and industrial heat recovery. The integration of ML not only improves predictive capabilities but also reveals non-obvious interactions between fin geometry and NEPCM composition, enabling synergistic thermal optimization beyond conventional parametric studies.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 109009"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An enhanced particle swarm optimizer – LSTM model with adaptive variational mode decomposition for solar radiation forecasting","authors":"Blessing Olatunde Abisoye ,&nbsp;Yanxia Sun ,&nbsp;Wang Zenghui","doi":"10.1016/j.egyr.2025.108960","DOIUrl":"10.1016/j.egyr.2025.108960","url":null,"abstract":"<div><div>Improvements in solar radiation forecasting models have several positive effects on the solar energy industry. The existing improved models exhibit shortcomings, including loss of diversity, premature convergence, and convergence to local optima. These can undermine solar energy production. To address these shortcomings, this study proposed a novel hybrid adaptive variational mode decomposition with an enhanced particle swarm optimization for long short-term memory networks (AVMD-EPSO-LSTM). The AVMD strategy eliminates the high-frequency components from the solar radiation dataset using Harris Hawk Optimization (HHO) and VMD. The EPSO comprises a bi-diversity and a tournament elite selection method. The bi-diversity involves affinity propagation clustering (APC), which groups the swarm into subswarms, and a ring topology that searches for the best neighbor in each subswarm. The tournament elite selection finds the best solution from the archived best neighbors. The EPSO algorithm obtains the best LSTM’s hyperparameters that revamp the forecasting model's capability. Several performance metrics and models are used to evaluate and benchmark the proposed model, respectively. The experimental outcomes revealed that the AVMD-EPSO-LSTM model demonstrates exceptional performance in handling loss of diversity, premature convergence, and local optima. Furthermore, the Wilcoxon signed-rank test improvement analysis revealed the model's reliability. Consequently, the AVMD-EPSO-LSTM model can serve as a valuable operational tool for solar energy stakeholders, ensuring the sustainable development of renewable energy.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 108960"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}