Sustainable Energy Grids & Networks最新文献

{"title":"Electric vehicle charging infrastructure planning with integrated energy management and parking behavior analysis","authors":"Fareed Ahmad ,&nbsp;Tousif Khan Nizami ,&nbsp;Atif Iqbal","doi":"10.1016/j.segan.2025.101990","DOIUrl":"10.1016/j.segan.2025.101990","url":null,"abstract":"<div><div>The rapid adoption of electric vehicles (EVs) offers ecological and economic benefits but also introduces challenges to power distribution networks, including increased energy losses, voltage fluctuations, reduced reliability, and higher peak demand. Uncoordinated deployment of charging stations (EVCSs) may further deteriorate grid performance. While existing studies have examined EVCS siting or renewable energy integration separately, few provide a holistic framework that simultaneously considers EVCS planning, renewable generation, storage-based energy management, and user behavior under uncertainty. The objective of this study is to develop an integrated planning model that determines the optimal locations and sizes of EVCSs, aiming to minimize energy losses, investment costs, and driver travel costs, while reducing peak demand and maximizing renewable energy utilization. To achieve this, a hybrid Gray Wolf Optimization–Particle Swarm Optimization (GWO–PSO) algorithm is applied for multi-objective optimization, chosen for its effective balance of global exploration and local exploitation. Photovoltaic (PV) systems are incorporated at selected distribution nodes, and energy management strategies (EMSs) are designed to coordinate energy storage system (ESS) operations. Uncertainties in PV generation and EV charging demand are addressed using Monte Carlo Simulation (MCS). The methodology is validated on the IEEE 33-bus distribution system under a 24-hour simulation. Results show that integrating EMS with optimally located EVCSs reduces average energy losses by up to 15 % and lowers peak power demand by 20 %. These findings demonstrate that the proposed approach provides a robust, cost-effective, and sustainable pathway for EVCS infrastructure planning.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101990"},"PeriodicalIF":5.6,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266128","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-faceted strategy for scalable, efficient, and grid-integrated electric vehicle systems using solid-state batteries and AI technologies","authors":"S. Poorani ,&nbsp;P. Josephin Shermila ,&nbsp;R. Niruban ,&nbsp;T. Maris Murugan","doi":"10.1016/j.segan.2025.101994","DOIUrl":"10.1016/j.segan.2025.101994","url":null,"abstract":"<div><div>The widespread adoption of electric vehicles (EVs) is a pivotal step toward achieving sustainable transportation and energy systems. However, several technological and infrastructural challenges hinder their scalability and efficiency. Despite advancements, current EV technologies are constrained by battery energy density, charging rates, and thermal management, limiting vehicle range and performance. Manufacturing limitations, supply chain issues, and charging infrastructure prevent large-scale implementation. The increasing demand for EVs also poses challenges to power system stability, particularly with the integration of intermittent renewable energy sources. This paper proposes a Multi-Faceted Method (M-FM) to address these challenges by integrating next-generation solid-state batteries with 40 % higher energy density, advanced battery management systems for optimal performance, and AI-driven predictive maintenance. This study aims to develop and assess a scalable, AI-augmented EV infrastructure model leveraging solid-state battery technologies for enhanced grid integration and sustainability. The proposed solutions include modular battery designs, automated gigafactories, and circular economy strategies for battery recycling to enable scalability. A smart grid integration architecture with bidirectional charging, dynamic load balancing algorithms, and blockchain-enabled energy trading platforms is introduced to transform EVs into grid-stabilizing assets. Experimental results show that the solid-state battery design achieves 500 Wh/kg energy density and 99.8 % faster charging. Vehicle-to-grid (V2G) integration can potentially fulfill to 96.3 % of a city's frequency control needs. Economic analyses indicate that these innovations could reduce the overall cost of EV ownership by 28 % compared to technologies. The study also emphasizes the need for legislative interventions, standardized billing, tariff reforms, and public-private partnerships, to accelerate implementation.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101994"},"PeriodicalIF":5.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219718","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-scale optimization framework for energy transition planning in urban areas: Insights from a university campus case study","authors":"J. Vergara-Zambrano,&nbsp;Parth Brahmbhatt,&nbsp;Styliani Avraamidou","doi":"10.1016/j.segan.2025.101996","DOIUrl":"10.1016/j.segan.2025.101996","url":null,"abstract":"<div><div>The transition to low-carbon energy systems is crucial for mitigating climate change. However, it remains challenging due to the intermittency of renewable energy sources and increasing energy demands. This study introduces a multi-scale optimization framework for the infrastructure planning of urban energy systems, considering the complex interplay between heating and electricity systems, and, unlike existing approaches, simulating a planning horizon of multiple years at an hourly resolution, without relying on representative-day approaches. It links short-term operational decisions with long-term sustainability goals, providing a realistic representation of energy system performance. It is applied to a case study considering the energy transition of a university campus, with the model solved at an hourly resolution over a 25-year horizon. The proposed framework includes weather data forecasting and preprocessing to generate hourly energy production profiles and reduce computational complexity. The results show that by 2030, 50 %–95 % of electricity can be supplied from low-carbon sources, achieving a 50 %–88 % reduction in annual CO₂ emissions compared to 2025, though this requires high upfront investments, highlighting the trade-offs between emissions reduction and costs. Energy storage will be crucial for mitigating renewable intermittency, potentially accounting for 40 % of the system costs. The electrical grid decarbonization pathway strongly influences infrastructure requirements but is insufficient alone to achieve net-zero targets, as heating and cooling systems must also be decarbonized. Overall, the analysis highlights the importance of temporal granularity: hourly modeling captures peak loads, seasonal mismatches, and variability across timescales, enabling more accurate technology sizing and assessment of operational flexibility.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101996"},"PeriodicalIF":5.6,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266125","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 novel planning and operation strategy of solar rooftop EV parking lots in a coupled transportation-distribution network considering uncertainties","authors":"Kutikuppala Nareshkumar,&nbsp;Debapriya Das","doi":"10.1016/j.segan.2025.101988","DOIUrl":"10.1016/j.segan.2025.101988","url":null,"abstract":"<div><div>Electric vehicles (EVs) offer a sustainable path for decarbonizing transportation, and solar rooftop parking lots (SRPLs) enable their integration with solar energy. The growing adoption of EVs poses challenges for power grid integration, including peak demand spikes, voltage instability, network congestion, and uncertain charging behaviour. Addressing these issues requires coordinated planning and operation that meet both transportation and distribution network goals. A multi-stage approach effectively handles multiple objectives. This study introduces strategic two-stage planning and operation of SRPLs in a coupled transportation (TN)-distribution network (DN). In the first stage, a sensitivity analysis is conducted to identify the ideal locations and sizes of SRPLs by integrating a novel EV user satisfaction cost index. The objectives in this stage focus on enhancing the operational performance of both the transportation and distribution networks. In the second stage, the identified locations and sizes are used to determine the optimal operation of SRPLs, taking into account seasonal variations in solar generation and load demand. The objectives aim to maximize SRPL operator profit while minimizing EV user payments, additional DN operator costs, and grid emissions. Fuzzy max-min composition is used to determine the optimal solution by simultaneously satisfying all objectives to the highest possible extent. The proposed technique, validated on real (28-node TN, 69-bus DN) and test (35-node TN, 85-bus DN) systems, effectively identifies SRPL locations, ratings, and operation strategies. Vehicle-to-grid mode of EVs at SRPLs increases profit by 26.19 %, reduces EV user costs by 6.55 %, and cuts grid emissions by 4.86 %.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101988"},"PeriodicalIF":5.6,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266130","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":"Dynamic cluster and wind-storage collaborative frequency regulation control strategy for large scale wind farms","authors":"Guangxue WANG ,&nbsp;Hongchun SHU ,&nbsp;Botao SHI ,&nbsp;Haixin MA ,&nbsp;Liuqing ZHU","doi":"10.1016/j.segan.2025.101991","DOIUrl":"10.1016/j.segan.2025.101991","url":null,"abstract":"<div><div>When wind farms participate in primary frequency regulation (PFR) of power grids, most existing methods adopt single-machine multiplication approaches, making wind power frequency regulation struggle to meet practical requirements. To enable more accurate system frequency dynamic analysis and research, it is imperative to establish equivalent models for wind power frequency regulation and optimize wind turbine control strategies. From the perspective of \"wind turbine clusters\", this paper proposes a Principal Component Analysis (PCA) based clustering criteria selection method, employs an improved Kernel Fuzzy C-Means (Kernel-FCM) clustering algorithm to classify wind turbine clusters, and achieves dynamic aggregation equivalence for large-scale wind farms. Based on aggregation results, a wind-storage coordinated frequency regulation control strategy for full wind speed scenarios is developed: the Energy Storage Systems (ESSs) adopts adaptive virtual droop control; turbines implement pitch angle de-loading control in constant power zones and adaptive virtual inertia control in maximum power point tracking (MPPT) zones. A determination mechanism is established upon the conclusion of inertial support and the initiation of rotor speed recovery, accompanied by corresponding power compensation schemes. The three-machine, nine-node model with a wind-storage system was established using RT-LAB, validating the advantages of the proposed frequency regulation control strategy.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101991"},"PeriodicalIF":5.6,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266126","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":"Optimal microgrid sizing of offshore renewable energy sources for offshore platforms and coastal communities","authors":"Ann Mary Toms,&nbsp;Xingpeng Li,&nbsp;Kaushik Rajashekara","doi":"10.1016/j.segan.2025.101989","DOIUrl":"10.1016/j.segan.2025.101989","url":null,"abstract":"<div><div>The global energy landscape is undergoing a transformative shift towards renewable energy and advanced storage solutions, driven by the urgent need for sustainable and resilient power systems. Isolated offshore communities, such as islands and offshore platforms, which traditionally rely on mainland grids or diesel generators, stand to gain significantly from renewable energy integration. Promising offshore renewable technologies include wind turbines, wave and tidal energy converters, and floating photovoltaic systems, paired with a storage solution like battery energy storage systems. This paper introduces a renewable energy microgrid optimizer (REMO), a tool designed to identify the optimal sizes of renewable generation and storage resources for offshore microgrids. A key challenge in such models is accurately accounting for battery degradation costs. To address this, the REMO model integrates a deep neural network-based battery degradation (DNN-BD) module, which factors in variables like ambient temperature, charge/discharge rates, state of charge, depth of discharge and battery health. Simulations on six test regions demonstrate that the REMO-DNN-BD approach minimizes lifetime energy costs while maintaining high reliability and sustainability, making it a viable design solution for offshore microgrid systems.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101989"},"PeriodicalIF":5.6,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266123","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":"Improving transmission line resilience against hurricane-induced cascading outages by preventing maloperation of vulnerable zone 3 distance relays","authors":"Seyyed Alireza Modaberi ,&nbsp;Sajjad Tohidi ,&nbsp;Saeid Ghassem Zadeh ,&nbsp;Tohid Ghanizadeh Bolandi","doi":"10.1016/j.segan.2025.101993","DOIUrl":"10.1016/j.segan.2025.101993","url":null,"abstract":"<div><div>High-impact, low-probability events such as hurricanes can severely affect transmission networks, causing cascading outages of transmission lines and leading to power system instability. When transmission lines are disconnected, power flow transfer phenomena may occur, resulting in the redistribution of power to healthy lines and potentially causing overloading. This condition often triggers load encroachment into Zone 3 of distance relays on unaffected lines, leading to their unintended operation. Such maloperation can propagate cascading outages and accelerate system instability. This paper aims to enhance the resilience of transmission networks against cascading outages induced by hurricanes. The primary objective is to identify and monitor the performance of vulnerable Zone 3 distance relays to prevent their contribution to the spread of outages. By mitigating relay maloperations, the number of line outages due to incorrect tripping is reduced. A new hurricane model based on the Rankine vortex is proposed to improve the prediction of transmission line outages associated with hurricanes. The model estimates wind speeds over time, tracks the spatial and temporal evolution of hurricanes, and enables more accurate modeling of hurricane-induced cascading outages. Additionally, two novel indices are introduced: one for evaluating the vulnerability of Zone 3 distance relays and another for distinguishing between three-phase symmetrical faults and load encroachment conditions. The proposed methodology is implemented on the 39-bus New England test system. Simulation results confirm the method’s effectiveness and efficiency.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101993"},"PeriodicalIF":5.6,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219717","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":"Three-phase power flow calculations for unbalanced radial distribution networks: recDQNR method","authors":"Nien-Che Yang,&nbsp;Hsing-Chih Chen","doi":"10.1016/j.segan.2025.101995","DOIUrl":"10.1016/j.segan.2025.101995","url":null,"abstract":"<div><div>Unbalanced radial distribution networks (DNs) present unique challenges in power system analysis, particularly in three-phase power flow calculations. This study introduces an innovative computational framework to address these complexities. The proposed method combines matrix decomposition, sparse matrix techniques, and injected power techniques. Specifically, a rectangular (Cartesian) coordinate-based decomposed quasi-Newton–Raphson (recDQNR) method is used to solve the set of nonlinear power equations. By incorporating the injected power technique, coupling-free component models can be seamlessly integrated into the recDQNR method. Four three-phase IEEE test DNs were used for a comparative analysis to assess the accuracy and performance of the proposed method. The proposed recDQNR method demonstrated high accuracy, with mismatches of 0.000146 pu (0.015 %) for the voltage magnitude and 0.047° (0.187 %) for the voltage angle in the IEEE 13-node network. The efficiency improved significantly: up to 156 % faster than decomposed quasi-Newton Raphson with 57 %–175 % fewer iterations, 104 %–249 % faster than decomposed Newton–Raphson, and up to 800 times faster than traditional Newton–Raphson (NR). The proposed method exhibits superior performance compared with the current injection-based NR method, reducing the execution time by up to 91 %. These results highlight the potential of the proposed recDQNR method for DN optimisation applications.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101995"},"PeriodicalIF":5.6,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266127","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":"Enhanced maintenance prioritization strategies for medium voltage distribution substations through precise health index assessment","authors":"Rahman A. Prasojo ,&nbsp;Muhammad R. Zainal ,&nbsp;Galuh P.C. Handani ,&nbsp;Muhammad F. Hakim ,&nbsp;Bustani H. Wijaya ,&nbsp;Sherif S.M. Ghoneim ,&nbsp;Karar Mahmoud ,&nbsp;Matti Lehtonen ,&nbsp;Mohamed M.F. Darwish","doi":"10.1016/j.segan.2025.101987","DOIUrl":"10.1016/j.segan.2025.101987","url":null,"abstract":"<div><div>Medium Voltage (MV) distribution substations are vital infrastructure in electrical power networks, yet condition-based maintenance practices for these systems remain underdeveloped compared to transmission-level assets. This study introduces a structured diagnostic framework to support maintenance prioritization in MV substations by aggregating diverse technical indicators into a unified condition score. The proposed model integrates data from field inspections, online monitoring, and laboratory testing across multiple substation components—such as MV fuses, lightning arresters, distribution transformers, and LV panels. Diagnostic parameters are individually scored based on operational standards, while the relative importance of each parameter is established using the Analytic Hierarchy Process (AHP), informed by expert input. The framework was applied to 695 MV substations within an Indonesian utility, producing health index scores that guided condition-based categorization. Results show that 567 substations were in good condition (HI &gt; 80), while 128 were classified as requiring closer monitoring. Case studies of representative substations demonstrated a strong alignment between the health index outcomes and actual field conditions. The multi-parameter approach was particularly effective in identifying issues that would be overlooked in single-variable assessments. Compared to conventional methods, this model supports more holistic decision-making for asset maintenance and replacement planning. Its scalable structure and adaptability to different datasets make it a valuable tool for utility operators aiming to improve reliability and optimize resource allocation in distribution networks.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101987"},"PeriodicalIF":5.6,"publicationDate":"2025-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219719","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":"Temporal attention for photovoltaic power forecasting using all-sky imagery","authors":"Chien-Liang Liu ,&nbsp;Shu-Rong Lu ,&nbsp;Yong-Tai Chen ,&nbsp;Ching-Hsien Lee","doi":"10.1016/j.segan.2025.101985","DOIUrl":"10.1016/j.segan.2025.101985","url":null,"abstract":"<div><div>Accurate solar power forecasting plays a pivotal role in sustainable energy management. We propose a temporal attention (TA) module that leverages sequential all-sky imagery to enhance solar radiation and power generation predictions. This pluggable module distinctly captures temporal dynamics in sky images, surpassing traditional methods. Comprehensive experiments verify its adaptability and universality, showing marked forecasting improvements across state-of-the-art deep-learning models. Notably, integrating TA with the Video Swin Transformer, forming ViSiT-TA, further boosts predictive accuracy by extracting spatio-temporal features. This research underscores the importance of innovative deep-learning techniques for advancing solar energy forecasting and promoting environmentally responsible solutions.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101985"},"PeriodicalIF":5.6,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219859","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}