Sustainable Energy Grids & Networks最新文献

{"title":"Data-driven probabilistic evaluation of electric-vehicle integration in distribution systems: charging behavior, hosting capacity, and grid impact","authors":"Priscila Costa Nascimento ,&nbsp;Silvia Trevisan ,&nbsp;Monika Topel ,&nbsp;Björn Laumert","doi":"10.1016/j.segan.2025.101770","DOIUrl":"10.1016/j.segan.2025.101770","url":null,"abstract":"<div><div>Decarbonization policies have significantly increased the adoption of plug-in electric vehicles (PEVs) worldwide. This paper develops and applies a probabilistic method for assessing large-scale integration of PEVs in a real low voltage distribution system (DS) in Stockholm County, Sweden. The framework employs Monte Carlo simulations to capture uncertainties in driver behaviors, daily distances, charging start times, and vehicle allocation. Its key contributions are: (i) a replicable data-driven Monte Carlo framework that merges DS operator (DSO) load data with travel habit statistics, (ii) realistic charging-profile generation, (iii) demonstrate that adding price signals plus a network constraint almost doubles hosting capacity and cuts user costs, and (iv) a systematic comparison of uncontrolled versus controlled charging that clarifies technical-economic trade-offs. The analysis considers PEV penetration levels (<span><math><mi>P</mi><mi>l</mi></math></span>s)—defined as the percentage of customer units (CUs) with a PEV among all CUs with permanent access to a passenger vehicle—up to 100 %. Key performance indicators, analyzed at the <span><math><msup><mn>95</mn><mrow><mi>t</mi><mi>h</mi></mrow></msup></math></span> percentile to represent near-worst-case outcomes, include voltage profiles, transformer and line loading, aggregated peak power, technical losses, and hosting capacity. Uncontrolled charging raises peak demand, causing voltage and overload violations that cap hosting capacity at <span><math><mi>P</mi><mi>l</mi></math></span> 27 %. Adding price signals with a peak demand cap lifts capacity to <span><math><mi>P</mi><mi>l</mi></math></span> 49 %, halves overloads, and lowers charging costs by about 10 %. Night-time charging suffices up to <span><math><mi>P</mi><mi>l</mi></math></span> 49 %; above <span><math><mi>P</mi><mi>l</mi></math></span> 75 %, morning charging is needed to keep power quality within limits. The method is broadly replicable and offers actionable guidance for municipalities, DSOs, and policymakers seeking to ensure a sustainable and cost-effective transition toward electrified transportation while maintaining reliable DS operation.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"43 ","pages":"Article 101770"},"PeriodicalIF":4.8,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient charging coordination of electric vehicles: A consensus tracking control approach","authors":"Hany A. Abdelsalam ,&nbsp;Ehab M. Attia ,&nbsp;Ali Arzani ,&nbsp;Satish M. Mahajan","doi":"10.1016/j.segan.2025.101771","DOIUrl":"10.1016/j.segan.2025.101771","url":null,"abstract":"<div><div>Participating in climate change mitigation requires addressing the escalating use of electric vehicles (EVs) and their integration with the power grid. Inefficiencies in energy utilization during the EV charging process occur due to power losses. To foster an energy-efficient system and support sustainable energy resource utilization, this paper introduces a consensus tracking control method for effective EVs charging coordination in a charging station. The primary aim is to reduce charging power losses and efficiently use the available power at the charging stations. The approach involves formulating power deviations of EVs and designing control gains. Graph theory is employed to create the communication network between EVs and the charging station. The consensus tracking algorithm facilitates the updating of local information, sharing of external information among neighboring EVs and the charging station, and ensures the convergence of the consensus goals. To demonstrate the proposed method, the consensus tracking controller is applied to an EV system based on both random parameters and commercial models’ parameters. The proposed method is evaluated for its fundamental performance using a system with three EVs, and for its scalability with a system comprising ten EVs, all within the Matlab/Simulink environment. Simulation results indicate that with commercial EV models, charging coordination is effectively managed while achieving the target EVs’ state of charge (SoC). In addition, the approach reduces power losses and maximizes the charging efficiency maintaining power losses in AC charging within 0.18 %–0.66 % and below 6.73 % in DC fast charging. Furthermore, the proposed consensus tracking method consistently converges regardless of varying EVs’ arrival and departure times.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"43 ","pages":"Article 101771"},"PeriodicalIF":4.8,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust stackelberg game strategies for managing demand uncertainty in energy trading problems","authors":"Biji Varghese,&nbsp;D.N. Gaonkar","doi":"10.1016/j.segan.2025.101764","DOIUrl":"10.1016/j.segan.2025.101764","url":null,"abstract":"<div><div>Peer-to-peer (P2P) energy trading is emerging as a promising approach for facilitating energy sharing among microgrids. This paper addresses the P2P energy trading problem using a robust Stackelberg game (RSG) approach, where producers and consumers are modeled as multiple leaders and followers, respectively, within a power system, while accounting for demand uncertainty. The robust noncooperative framework treats the energy trading problem as a competitive game among self-interested prosumers, each selecting a demand strategy to maximize their own benefit. The study also incorporates the impact of carbon emissions and transmission costs. To solve the social welfare function, a dual decomposition method is employed. Simulation results demonstrate the convergence performance, fairness, and scalability of the proposed decentralized approach for market clearing in P2P energy trading scenarios. The findings reveal that the method not only reduces trading costs for consumers and enhances utility for producers but also increases producer profits by <span><math><mn>34.56</mn><mspace></mspace><mi>%</mi></math></span> compared to conventional methods.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"43 ","pages":"Article 101764"},"PeriodicalIF":4.8,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144298121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 synthetic Texas power system with time-series weather-dependent spatiotemporal profiles","authors":"Jin Lu ,&nbsp;Xingpeng Li ,&nbsp;Hongyi Li ,&nbsp;Taher Chegini ,&nbsp;Carlos Gamarra ,&nbsp;Y.C. Ethan Yang ,&nbsp;Margaret Cook ,&nbsp;Gavin Dillingham","doi":"10.1016/j.segan.2025.101774","DOIUrl":"10.1016/j.segan.2025.101774","url":null,"abstract":"<div><div>We developed a synthetic Texas 123-bus backbone transmission system (TX-123BT) with spatio-temporally correlated grid profiles of solar power, wind power, dynamic line ratings and loads at one-hour resolution for five continuous years, which demonstrates unique advantages compared to conventional test cases that offer single static system profile snapshots. Three weather-dependent models are used to create the hourly wind power productions, solar power productions, and dynamic line ratings respectively. The actual historical weather information is also provided along with this dataset, which is suitable for machine learning models. Security-constrained unit commitment is conducted on TX-123BT daily grid profiles and numerical results are compared with the actual Texas system for validation. The created hourly DLR profiles can cut operating cost from $8.09 M to $7.95 M (-1.7 %), raises renewable dispatch by 1.3 %, and lowers average LMPs from $18.66 to $17.98 /MWh (-3.6 %). Two hydrogen options—a 200 MW dual hub and a 500 MW hydrogen-energy transmission and conversion system—reduce high-load Q3 daily costs by 13.9 % and 14.1 %, respectively. Sensitivity tests show that suppressing the high-resolution weather-driven profiles can push system cost up by as much as 15 %, demonstrating the economic weight of temporal detail.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"43 ","pages":"Article 101774"},"PeriodicalIF":4.8,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144365037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fast decomposition of energy flow for integrated electricity and gas systems","authors":"Yeong Geon Son,&nbsp;Sung Yul Kim","doi":"10.1016/j.segan.2025.101758","DOIUrl":"10.1016/j.segan.2025.101758","url":null,"abstract":"<div><div>This paper introduces a novel mathematical approach for analyzing energy flow in Integrated Electricity and Gas Systems (IEGS) within distribution networks. Although the non-convex nature of natural gas flow has traditionally been handled using second-order cone programming (SOCP), SOCP-based formulations suffer from reduced computational efficiency and solver compatibility issues as system scale increases. To address these challenges, this paper proposes a Taylor series-based first-order linear approximation method that maintains linearity, thereby enabling faster computation and better compatibility with standard optimization solvers. Despite its iterative nature, the proposed method exhibits rapid and accurate convergence. Validation was conducted on several test systems, including the radial IEEE 33-bus/33-node system, a meshed IEEE 8-bus/8-node gas network, and the large-scale IEEE 118-bus/118-node system. Simulation results demonstrate that the proposed approach achieves higher approximation accuracy and faster computation compared to conventional SOCP-based methods, confirming its effectiveness for practical IEGS operation analysis.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"43 ","pages":"Article 101758"},"PeriodicalIF":4.8,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144298122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data anomaly detection in photovoltaic power time-series via unsupervised deep learning with insufficient information","authors":"Seyed Mahdi Miraftabzadeh,&nbsp;Michela Longo,&nbsp;Sonia Leva,&nbsp;Nicoletta Matera","doi":"10.1016/j.segan.2025.101769","DOIUrl":"10.1016/j.segan.2025.101769","url":null,"abstract":"<div><div>Anomaly detection in photovoltaic (PV) systems is essential to improving reliability, ensuring electricity production and equipment safety, and decreasing their negative impact on the economy of the operation system. In many real-world scenarios—such as limited historical data, incomplete documentation, varying conditions, data corruption, or privacy issues—insufficient and unlabelled data challenge traditional anomaly detection and supervised learning methods for PV systems. Therefore, this paper proposes an effective unsupervised data anomaly detection model based on a deep neural network autoencoder. This model does not require prior knowledge about the system and accurately identifies PV system anomalies with limited information. The proposed model only uses measured PV power production as input and does not need additional information on PV system parameters or measurement data. Additionally, we derived an optimal threshold to detect anomalies based on the mean and standard deviation of the reconstruction error, resulting in a significant improvement in the F1-score from 0.9123 with the traditional approach to 0.9993. Lastly, a novel locally adaptive mechanism based on Dynamic Time Warping (DTW) error analysis is proposed to effectively locate anomaly segments by considering the shape of anomalous parts within the input time series data. The proposed model is validated on a real PV power plant in Genoa, Italy. The case study results demonstrate that the model outperforms other unsupervised machine learning models with a 0.9535 F1-score in testing and shows performance comparable to that of advanced supervised models, including XGBoost and deep neural networks.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"43 ","pages":"Article 101769"},"PeriodicalIF":4.8,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144262019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection and classification of concurrent attacks in substation automation systems using wavelet design and deep learning","authors":"M. Oinonen,&nbsp;W.G. Morsi","doi":"10.1016/j.segan.2025.101768","DOIUrl":"10.1016/j.segan.2025.101768","url":null,"abstract":"<div><div>This paper presents a novel approach to detect and classify cyberattacks using wavelet design and deep learning. Existing works fail to investigate concurrent cyberattacks and works that utilize time-frequency features for cyberattack detection only use the existing standard wavelet filters that have not been designed for cybersecurity applications. This work proposes a detection scheme for concurrent attacks using new wavelet filters with the Discrete Wavelet Transform (DWT) to better extract time-frequency features from substation automation system (SAS) data. A set of new wavelet filters are generated from parameterized equations. The wavelet filter that best suits SAS cyberattack detection is used to extract the salient features of cyberattacks using the DWT. Unlike existing detection approaches, the use of wavelet design allows the generation of new wavelet filters that better match the time-frequency features of SAS data. The proposed approach has been tested on a publicly available dataset as well as experimentally using OPAL-RT. The results demonstrate its effectiveness in detecting four popular cyberattack types as well as the challenging concurrent attacks, which involve two or more attacks occurring simultaneously. The use of wavelets not only enables the detection of the attacks but also their classification by type from power disturbances with an accuracy reaching 99.12 % on a synthetic dataset and 95.47 % on an experimental dataset. Furthermore, the results have shown that the use of the newly designed wavelets leads to an increase in the detection accuracy by 9.36 % and a significant reduction in the computational complexity of the feature extraction process by up to 99.16 % over the existing time-frequency transforms.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"43 ","pages":"Article 101768"},"PeriodicalIF":4.8,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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-stage decision support system for evaluating visual energy performance certificate platforms","authors":"Hafiz Muhammad Shakeel ,&nbsp;Hafiz Muhammad Athar Farid ,&nbsp;Shamaila Iram ,&nbsp;Richard Hill ,&nbsp;Vladimir Simic","doi":"10.1016/j.segan.2025.101767","DOIUrl":"10.1016/j.segan.2025.101767","url":null,"abstract":"<div><div>The energy performance certificate (EPC) has emerged as a pivotal instrument in the United Kingdom’s drive for housing energy efficiency. This research presents a comprehensive evaluation of existing EPC platforms using a multi-stage decision support system. In response to the need for effective decision support systems, we propose a novel approach, namely the intuitionistic fuzzy criteria importance assessment (CIMAS)-alternative ranking order method accounting for two-step normalization (AROMAN) method. Through comparative analysis, the study assesses the effectiveness and functionality of various platforms in facilitating energy performance analysis for residential buildings. Among the evaluated platforms, the energy performance certificate visualization platform (EPCDescriptor) emerges as a standout performer, demonstrating superior capabilities in attribute-level analysis, real-time data insights, and strategic pathway visualization of EPC data. EPCDescriptor’s unique strengths lie in its ability to provide granular insights into factors influencing EPC ratings, offer real-time data updates for informed decision-making, and visualize strategic pathways for enhancing energy efficiency. The platform stands out among alternatives by offering enhanced capabilities for discrepancy analysis, neighbourhood exploration, and the interpretation of EPC ratings in residential buildings. Furthermore, we present a comparative analysis, managerial implications, and theoretical limitations of the proposed intuitionistic fuzzy CIMAS-AROMAN method, offering valuable insights for stakeholders in the construction and energy sectors. The findings of this research contribute to the advancement of energy performance analysis tools.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"43 ","pages":"Article 101767"},"PeriodicalIF":4.8,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144262013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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-task deep learning economic dispatch of microgrids with electric vehicles and renewables","authors":"Seyed Morteza Ghorashi,&nbsp;Javad Khazaei,&nbsp;Shalinee Kishore","doi":"10.1016/j.segan.2025.101766","DOIUrl":"10.1016/j.segan.2025.101766","url":null,"abstract":"<div><div>The increasing penetration of electric vehicles (EVs) and renewables in microgrids stimulates solving real-time economic dispatch (ED) that captures the stochastic nature of the problem. However, utilizing conventional or meta-heuristic methods to solve ED in real time is difficult and computationally costly, especially when addressing uncertainties of EVs and renewable energy sources. This paper proposes a multi-task deep learning approach to solve ED and separately learn the variability associated with EV and non-EV assets. The resulting case studies demonstrate the efficacy of the proposed data-driven model in solving real-time ED much faster and more scalable than numerical optimization and more accurately than conventional deep learning models.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"43 ","pages":"Article 101766"},"PeriodicalIF":4.8,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144279842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advanced hybrid deep learning based framework for microgrid inverter predictive maintenance","authors":"M.Y. Arafat,&nbsp;M.J. Hossain","doi":"10.1016/j.segan.2025.101765","DOIUrl":"10.1016/j.segan.2025.101765","url":null,"abstract":"<div><div>The increasing complexity of microgrids (MGs) demands sophisticated strategies for improved maintenance and reliable operation. The integration of artificial intelligence (AI) into microgrids allows for the analysis of system performance, anomaly detection, malfunction identification, and the generation of alerts through continuous monitoring in case of any unexpected drop in performance enabling reliable operations and maintenance, enhancing predictive maintenance capabilities and sustainable decision making. To improve system reliability and performance, accurate fault identification and the generation of maintenance alerts according to the performances over time are becoming crucial for enhanced predictive maintenance (PdM) of MGs. This study introduces an intelligent data-driven hybrid framework for microgrid PdM utilizing a hybrid deep learning (HDL) architecture, combined with advanced data analysis and a residual-based dynamic threshold technique. The loss function of the proposed hybrid algorithm has been optimized to enhance microgrid PdM. The results show significant accuracy in predicting the maintenance needs within MGs. This research offers valuable insights for designing and enhancing hybrid algorithms for advanced maintenance in MG systems, contributing to advancements in PdM technology and promoting a resilient and cost-effective operation of MGs.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"43 ","pages":"Article 101765"},"PeriodicalIF":4.8,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144241536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}