Sustainable Energy Grids & Networks最新文献

{"title":"Sizing of Fast Frequency Response Reserves for improving frequency security in low-inertia power systems","authors":"Savvas Panagi ,&nbsp;Petros Aristidou","doi":"10.1016/j.segan.2025.101699","DOIUrl":"10.1016/j.segan.2025.101699","url":null,"abstract":"<div><div>The increasing penetration of Renewable Energy Sources (RES) in electricity grids has led to the gradual decommissioning of conventional generators and, thus, to a decrease in the available inertia and other frequency support reserves. Consequently, the frequency security of power systems, in particular islanded low-inertia ones, is compromised, leading to faster and more extreme frequency deviations following disturbances. There is an urgent need to incorporate faster frequency reserves that can stabilize the system and enhance its resilience and reliability. This paper first investigates the impact of various frequency support mechanisms on the system frequency security in low-inertia grids. Then, we propose a novel, data-driven, gradient-descent-based method, that combines Dynamic Security Assessment (DSA) with linear predictions to optimize Fast Frequency Response (FFR) sizing for low-inertia grids. The performance of the proposed approach is evaluated using the dynamic model of Cyprus across 500 selected historical operating scenarios. The results demonstrate fast convergence, achieving the target frequency Nadir with minimal computational effort.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101699"},"PeriodicalIF":4.8,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143784049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of the influence of climate change on power grid transmission capacity","authors":"Montaña-Salas Sergio,&nbsp;Michiorri Andrea","doi":"10.1016/j.segan.2025.101695","DOIUrl":"10.1016/j.segan.2025.101695","url":null,"abstract":"<div><div>In order to propose effective solutions to mitigate the effects of climate change on the electrical power system, it is essential to have a comprehensive understanding and quantification of the relevant issues. This article explores the impact of climate on transmission network capacity, employing established thermal models and a regional expansion plan, fed by historical and climatic projections on a 0.25° grid resolution over the European continent. The results indicate that, under the high greenhouse gas emissions scenario (RCP 8.5), the area studied will experience average reductions of 1.53%, 2.1%, and 0.2% capacity by 2070, for overhead lines, power transformers, and underground cables, respectively. We propose a quasi-dynamic thermal rating method to estimate maximum capacity. This results in a capacity improvement of up to 22% for power transformers in winter and up to 17% for overhead lines during nighttime hours. This solution represents a viable alternative for electricity operators seeking to solve the dilemma of temperature-driven capacity reduction in the context of challenging network reinforcements.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101695"},"PeriodicalIF":4.8,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143776333","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":"Symbiotic fitness assessment for the \"Resource-Project-Demand\" chain of integrated energy system in industrial park","authors":"Zhenyu Zhao,&nbsp;Kun Yang","doi":"10.1016/j.segan.2025.101702","DOIUrl":"10.1016/j.segan.2025.101702","url":null,"abstract":"<div><div>The emergence of Integrated Energy Systems (IES) offers a promising solution for low-carbon transformation and enhancing energy efficiency in industrial park energy systems. Grounded in symbiosis theory, this study evaluates the symbiotic fitness status of three functional components (\"resource-project-demand\" chain) within IES in industrial parks (IES-IP). We establish a symbiotic fitness evaluation framework centered on key symbiotic parameters and develop a corresponding model to analyze five case studies. Comparative analysis of cumulative absolute relative deviations reveals that the weighting method incorporating moment estimation theory demonstrates superior performance among combined weighting approaches. The most influential criteria are found to be renewable energy resource potential (resource side), effective clean energy utilization rate (project side), and industrial-commercial electricity price peak-valley differentials (demand side). All five cases exhibit symbiotic fitness degrees surpassing the baseline threshold (point symbiosis, extremely unfit), though remaining below the optimal level (integrated symbiosis, perfectly fit). The obstacle degree model combined with symbiotic fitness coefficient analysis provides actionable insights for system optimization.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101702"},"PeriodicalIF":4.8,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748615","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 multi-objective strategic planning of smart energy hubs and hydrogen refueling stations toward net-zero emissions","authors":"Ghada Abdulnasser ,&nbsp;Essam E.M. Mohamed ,&nbsp;Mostafa F. Shaaban ,&nbsp;Abdelfatah Ali","doi":"10.1016/j.segan.2025.101690","DOIUrl":"10.1016/j.segan.2025.101690","url":null,"abstract":"<div><div>Hydrogen-based generation and storage technologies have been increasingly emerging as an appealing candidate for decarbonizing different sectors, including microgrids and transportation. Practically, energy hubs (EH) and Hydrogen refueling stations (HRS) could provide an ideal environment for integrating such technologies. However, the management process involves several conflicting objectives that must be met to a satisfactory extent. In this regard, this paper proposes a stochastic bi-level tri-objective optimization framework for the planning and operation of EHs and on-site green/blue HRSs. The multiple objectives involve total cost (i.e., capital, operation and maintenance, Hydrogen, and emissions), load profile deviation resulting from engaging the demand response program (DRP), and the dissatisfaction of fuel cell electric vehicles (FCVs) owners. The proposed model forms a bi-level optimization strategy. The upper-level optimization (i.e., planning level) optimizes the sizes and locations of renewable energy sources (RESs) along with the capacities, rates, and locations of the other resources (i.e., photovoltaic, wind turbine, Hydrogen storage system, thermal storage system) integrated into both EHs and HRSs incorporated into the IEEE-69 system. On the other hand, the lower level (i.e., operation level) precisely optimizes the charging and discharging profiles of the different resources incorporated in EHs and HRSs along with FCVs. The Pareto optimal solution is employed to find the best-compromised solution among the conflicting tri-objective solutions. The simulation results demonstrate that the green-controlled approach has validated its superiority for net-zero emissions transition with a 10.74 % reduction in emissions costs at almost the same total cost compared to the other approaches (i.e., blue-uncontrolled, blue-controlled, green-uncontrolled).</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101690"},"PeriodicalIF":4.8,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143776332","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":"Physics-data hybrid driven based topology and line parameter identification for AC/DC distribution network","authors":"Wang He,&nbsp;Hu Zhenning,&nbsp;Li Shiqiang,&nbsp;Yu Huanan,&nbsp;Bian Jing","doi":"10.1016/j.segan.2025.101698","DOIUrl":"10.1016/j.segan.2025.101698","url":null,"abstract":"<div><div>To achieve the accurate identification in AC/DC distribution network, A physics-data hybrid driven method is proposed to predict the real-time topology and line parameters of AC/DC distribution network. Firstly, a framework based on physics-data hybrid-driven approach is proposed, which enables the rapid online identification of real-time topology and line parameters. Secondly, a pseudo-measurement model of the AC/DC distribution network is proposed considering the control mode of voltage source converter (VSC). Then, the adaptive spectral clustering (ASC) algorithm is proposed to estimate the number of historical topology categories, and the label-free topology discrimination (LTD) model is trained by machine learning methods according to the clustered data. Then, a two-stage physics-driven model is proposed to deal with the topology and line parameters identification problem using only a small amount of historical data with the same topology label. By leveraging the relationship between the data with topology labels and the results of the physics-driven identification, a label-to-topology and line parameters mapping model is built using the graph convolutional neural network (GCN), enabling rapid prediction of the topology and line parameters. Finally, the effectiveness of the proposed method is verified by case study.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101698"},"PeriodicalIF":4.8,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748616","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":"Renewable energy communities virtual islanding: A decentralized service to improve distribution grid security","authors":"Angelo Velini ,&nbsp;Manuela Minetti ,&nbsp;Sergio Bruno ,&nbsp;Andrea Bonfiglio ,&nbsp;Renato Procopio ,&nbsp;Massimo La Scala","doi":"10.1016/j.segan.2025.101700","DOIUrl":"10.1016/j.segan.2025.101700","url":null,"abstract":"<div><div>This article presents a novel service provided by Renewable Energy Communities (RECs) for the distribution network. This service, called Virtual Islanding (VI) and ideally requested to the RECs by the Distribution System Operator (DSO), basically consists in requiring that the net active power exchange between the involved REC and the main grid is zero for a specified time frame. The effectiveness of RECs VI operation is assessed using an Optimal Power Flow (OPF) that considers distribution network operational limits. Simulations, performed in a test case distribution system with massive penetration of RES, highlight the VI benefits both from a technical and sustainability viewpoint.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101700"},"PeriodicalIF":4.8,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel MILP formulation for optimal allocation and coordination of protective and switching devices in active distribution networks","authors":"Wandry Rodrigues Faria ,&nbsp;Gregorio Muñoz-Delgado ,&nbsp;Javier Contreras ,&nbsp;Benvindo Rodrigues Pereira Jr.","doi":"10.1016/j.segan.2025.101692","DOIUrl":"10.1016/j.segan.2025.101692","url":null,"abstract":"<div><div>In the last decade, the increasing penetration of distributed generation has prompted the proposal of new formulations for distribution protection system planning, as the typical indications of coordination may not be reliable for active networks. In this context, a few papers that explicitly enforce coordination constraints have been published. However, these papers are mostly based on heuristics and metaheuristics; therefore, although the solutions are feasible, there is no guarantee of optimality. This paper presents a mixed-integer linear formulation for the allocation and coordination of control and protective devices in distribution systems with distributed generators. Thus, the proposed approach guarantees both the optimal investment plan and feasibility of the protection system operation. The proposed formulation is tested for a 69-node system considering load restoration possibilities via island operation, using protective devices, and load transfer to neighboring feeders and fault permanent isolation, using switching devices. The results attest to the cost-effectiveness of the protection system and its operational feasibility, as well as the superiority of the proposed model over simpler existing ones.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101692"},"PeriodicalIF":4.8,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748617","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 hybrid stochastic-robust optimal strategy of integrated electricity and gas grids in the presence of multi-energy hubs and responsible loads","authors":"W. Abdulrazzaq Oraibi ,&nbsp;Ali Salam Al-Khayyat ,&nbsp;Ahmed K. Abbas","doi":"10.1016/j.segan.2025.101697","DOIUrl":"10.1016/j.segan.2025.101697","url":null,"abstract":"<div><div>The optimal performance of multi-carrier energy systems is becoming increasingly important with the rise of highly efficient and cost-effective utilities in gas and electricity distribution networks. However, the collaborative management of interconnected electricity and gas networks, particularly in the context of renewable energy resources and flexible demand, poses a significant challenge for system operators. To address this, a stochastic-robust optimization approach is recommended to concurrently model the operating costs for both networks integrated with the multi-energy hub system (MEH), incorporating various energy conversion technologies, renewable energy resources (RERs), and gas storage (GS) units to meet heat, cooling, and gas demands. The consequences of power pricing uncertainty are modelled to deal with market power prices using a robust optimization strategy. Other significant uncertainties, such as solar (PV), wind power, electrical demands, gas loads, cooling loads, heating loads, and gas prices, all are handled by stochastic optimization. Additionally, the impact of installed gas and electricity loads during peak hours is examined through adjustable flexible demand response (FDR). The proposed model is formulated as a mixed-integer linear programming (MILP) model and is solved using a CPLEX solver with the GAMS optimization software. The results demonstrate that interconnected MEHs can significantly impact operating costs for both the Gas Distribution Network (GDN) and Electric Distribution Network (EDN) when considering flexible demands and energy sources. Specifically, compared to the risk-neutral case, the total operating cost increased by 2.74 %, rising from $30311.739 to $31141.653 under the worst conditions. In contrast, in the case of risk-averse optimal scheduling of the proposed problem, in the presence of FDR and GS units, the total operating cost decreased to $27283.08, representing a reduction of 12.39 %. This indicates that integrating MEHs and GS units, along with responsive demands, can effectively improve the flexibility of EDN and GDN while significantly reducing overall operating costs.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101697"},"PeriodicalIF":4.8,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739843","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":"Probabilistic generator contingency assessment for power grids with high renewable penetration","authors":"Oliver Stover,&nbsp;Pranav Karve,&nbsp;Sankaran Mahadevan","doi":"10.1016/j.segan.2025.101681","DOIUrl":"10.1016/j.segan.2025.101681","url":null,"abstract":"<div><div>In modern-day power grids, increasing participation of inverter-based generation (i.e., wind/solar generation) increases supply uncertainty, reduces grid inertia, and exacerbates security-related problems. This article develops a stochastic framework to assess the grid’s ability to withstand generator failure, while explicitly considering the supply and demand uncertainty. The framework enables proactive risk quantification and management to support secure operation of the modern-day power grid. It also allows consideration of adverse event probability after a generator failure to assess the relative importance of generator failure events. We demonstrate the proposed framework using a 200-bus synthetic grid. We find that probabilistic assessment is able to identify important contingencies, which would have been missed by deterministic analyses performed using mean values. We also develop a method for identifying important generator contingencies based on the probabilistic security and reliability analyses. We find that the resulting importance ranking is not identical to the generator capacity-based ranking and depends on the uncertainty in the generator’s active power output as well as its contribution to grid inertia.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101681"},"PeriodicalIF":4.8,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715262","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":"Deep learning-driven robust model predictive control based active cell equalisation for electric vehicle battery management system","authors":"Sairaj Arandhakar,&nbsp;Jayaram Nakka","doi":"10.1016/j.segan.2025.101694","DOIUrl":"10.1016/j.segan.2025.101694","url":null,"abstract":"<div><div>Stabilizing the cells in Electric Vehicle (EV) batteries allows for optimal efficiency, longer battery life, and greater performance. This research presents a deep learning tuning based on Robust Model Predictive Control (RMPC) to address the issue of EV cell imbalance. Deep learning is used to recognize the patterns of battery operation and hence the equalization of active cells is maintained. The equilibrium is maintained through the observation of the state of charge (SoC) of the cell. Parameters, such as Mean Absolute Error (MAE) and Mean Square Error (MSE) are employed to assess the efficiency of active cell balancing through the use of RMPC. The validity of proposed technique was shown by the use of MATLAB/Simulink in modelling, training, and testing the models as well as enhancing the battery performance. To perform the assessment, Multi-Layer Neural Network (MLNN), Long Short-Term Memory (LSTM) network and Recurrent Neural Network (RNN) are used. The proposed RMPC-based balancing demonstrated better accuracy with lower MSE and MAE values for RNN (0.712, 0.34), LSTM (0.724, 0.59), and MLNN (0.73, 0.65) as compared to Adaptive Model Predictive Control (AMPC) mechanism. The simulation results prove that proposed method efficiently provides the maximum voltage during the active cell balancing process.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101694"},"PeriodicalIF":4.8,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726206","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}