Sustainable Energy Grids & Networks最新文献

{"title":"Power distribution system resilience: A perspective of the power system operator","authors":"Sawan Vijay ,&nbsp;Mala De","doi":"10.1016/j.segan.2025.101950","DOIUrl":"10.1016/j.segan.2025.101950","url":null,"abstract":"<div><div>The increasing global temperature has led to heightened climate uncertainties, resulting in high-impact low-probability extreme weather events that pose significant challenges to critical power system infrastructure. Resilience of power systems is the capability to minimize the impact of these events while maintaining essential services during the events. Resilience can be broadly categorised into operational resilience and infrastructural resilience. Although existing research focuses extensively on operational part as it ensures continuity of power supply to the customers, but lacks comprehensive methodologies and metrics to effectively quantify infrastructural resilience. However, infrastructural resilience is vital for power system operators as it measures the quality of infrastructural components. This paper introduces a systematic approach to define and quantify both operational and infrastructural resilience through a stepwise metric-based framework, while focusing on the improvement of infrastructural resilience. To validate the proposed model, multiple simulation scenarios under varying extreme wind speed conditions are analysed. The results demonstrate the effectiveness of the developed metrics in assessing power system resilience and offer valuable insights into potential enhancement strategies. This paper provides a foundational methodology for improving resilience assessment and decision-making in power system operations.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101950"},"PeriodicalIF":5.6,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144912309","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":"Evaluating the advantages of compressed air energy storage, demand response program and dynamic rating technologies in the risk-constrained optimal scheduling of wind-based power system","authors":"Yonghui Cui ,&nbsp;Kai Jin ,&nbsp;Jianyong Yu","doi":"10.1016/j.segan.2025.101948","DOIUrl":"10.1016/j.segan.2025.101948","url":null,"abstract":"<div><div>This paper provides a risk-constrained stochastic day-ahead scheduling model for a power system integrated with smart technologies. Multiple uncertainties including wind speed, ambient temperature, sun irradiation and load demand are covered by the stochastic method. On the other hand, the downside risk constraint (DRC) is used to involve risks associated with uncertainties. Smart technologies including dynamic line/transformer rating (DLR and DTR), compressed air energy storage (CAES) unit and demand response program (DRP) are integrated into the power system to create a flexible system to decrease total cost, emissions, wind curtailment and load shedding as main aims of this work. AC power flow framework is used in this study to show the impact of smart technologies on the voltage profile of the test system. The benefits of the proposed model are surveyed through different case studies implemented on the IEEE 24-bus system. The results demonstrate that with smart technologies 100 % of wind power is absorbed which is in line with the goals of this work. Also, the voltage profile is smoother with smart technologies. Moreover, results show that the operator can properly handle the risk of uncertainties with the DRC approach.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101948"},"PeriodicalIF":5.6,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144989159","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":"Distributed optimal resource control strategy under correlated uncertainties in isolated microgrid","authors":"Dapeng Wu ,&nbsp;Zixuan Li ,&nbsp;Yan Zhen ,&nbsp;Xinqi Lin ,&nbsp;Songnong Li ,&nbsp;Yaping Cui ,&nbsp;Peng He","doi":"10.1016/j.segan.2025.101934","DOIUrl":"10.1016/j.segan.2025.101934","url":null,"abstract":"<div><div>The output uncertainties caused by the excessive dependence of renewable energy sources (RES) on meteorological factors affect the optimal operation of isolated microgrids. In addition, the superposition of demand fluctuations due to their power usage patterns and different scales severely affects the optimal control of distributed energy resources (DER) in microgrids. To solve the above problems, we design a distributed architecture driven by flexible loads in dual domains to solve the demand imbalance problem in microgrids, i.e., time and power flexible domains. Specifically, we first measure the probability of PV and load prediction error, that is, the conditional probability density function (PDF) by the Copula function. Based on these PDFs, we propose a copula-based correlated discrete convolutional (CopCDC) algorithm to calculate the uncertainty range of netload. Finally, a distributed optimal resources control strategy under correlated uncertainties algorithm(DOC-CU) is proposed, for different DERs to achieve game strategy exchange and individual optimum, guaranteeing the overall optimal consistency of the microgrid. The results show that the DOC-CU algorithm proposed has an 18.4 % reduction in user expenditures, a 15.4 % increase in microgrid benefits, a 13.5 % reduction in microgrid costs, a 65.5 % reduction in penalty expenditures, and finally a 24.5 % increase in user satisfaction.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101934"},"PeriodicalIF":5.6,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144989157","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":"Joint optimization of fleet sizing, charging station planning, and operation for autonomous electric vehicle fleets in urban transportation networks","authors":"Huayu Zhang ,&nbsp;Ding Jin ,&nbsp;Bing Han ,&nbsp;Fei Xue ,&nbsp;Shaofeng Lu ,&nbsp;Lin Jiang","doi":"10.1016/j.segan.2025.101946","DOIUrl":"10.1016/j.segan.2025.101946","url":null,"abstract":"<div><div>The rapid advancement of autonomous electric vehicles (AEVs) is reshaping urban transportation, presenting new opportunities for ride-hailing fleets. Under a vertically integrated structure and a unified economic objective, this study develops a mixed-integer linear programming model that jointly optimizes vehicle loading/rebalancing, order acceptance/abandonment, and charging/discharging operations, while accounting for AEV and charging station investments, charging and maintenance costs, and discharging and passenger revenues to maximize the operator’s net present value. One year of New York City Yellow Taxi trip data is processed by sampling from empirical distributions and introducing distributional noise using maximum likelihood estimation and the Akaike Information Criterion to capture travel demand characteristics and uncertainties. The model is tested across six operational modes within a 24-node transportation network that aggregates New York City’s taxi pick-up and drop-off zones and integrates real-world travel distances, speeds, and electricity prices. Results show that rebalancing reduces investment costs by approximately 50 %, while a flexible order acceptance strategy strategically abandons extreme congestion orders during peak hours, resulting in a 3.5 % cost reduction. Discharging operations improve charging pile utilization by 7 %. Sensitivity analysis reveals that higher driving speed and vehicle-to-grid incentives enhance profitability, a charging power of charging piles with 80 kW achieves a favorable cost–benefit trade-off, while the marginal benefits of increasing AEV battery capacity and charging/discharging power gradually decline as operational benefits approach saturation. These findings offer a practical framework for operators and planners to deploy cost-effective AEV fleets in urban transportation networks.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101946"},"PeriodicalIF":5.6,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144933101","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":"Photovoltaic hosting capacity evaluation of distribution grid integrated with two modes of battery swap stations","authors":"Hossein Farzin,&nbsp;Mohammad Reza Zahmati,&nbsp;Mahmood Joorabian","doi":"10.1016/j.segan.2025.101951","DOIUrl":"10.1016/j.segan.2025.101951","url":null,"abstract":"<div><div>This paper evaluates the photovoltaic (PV) hosting capacity (HC) of a distribution grid integrated with electric vehicle (EV) battery swap stations. Two modes of battery swapping are addressed: centralized and decentralized. In the centralized mode, swapped batteries are collected from various locations and transported to the centralized charging stations (CCSs) for recharging. In decentralized mode, however, batteries are both swapped and recharged at battery swapping stations (BSSs). To explore these modes, various scenarios are modeled to simulate the charging of batteries, considering initial charge states, battery capacities, and different charging schemes. The percentage of PV penetration is incrementally increased in different scenarios, and technical indicators—including feeder current, energy losses, node voltage, and transformer loading—are analyzed to calculate the PV hosting capacity. Monte Carlo simulation method is employed to generate and analyze scenarios associated with load patterns, PV power generation, and charging station power in different seasons of the year. The proposed framework is implemented on the IEEE 34-bus network using MATLAB and MATPOWER for load flow analysis. The results suggest that in decentralized mode, HC strongly depends on BSS location; when PV units are located at the same bus as BSSs, the HC increases by over 132 % compared to the base case. Moreover, increasing the number of EVs from 50 to 150 reduces the HC by more than 9 %. On the other hand, in centralized mode, increasing the frequency of battery collection cycles improves the hosting capacity by more than 39 %.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101951"},"PeriodicalIF":5.6,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144903318","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 methodology for the analysis and selection of weather station location for dynamic line rating using the estimation of effective wind","authors":"R. Minguez ,&nbsp;R. Martinez ,&nbsp;M. Manana ,&nbsp;A. Arroyo ,&nbsp;E. Sainz-Ortiz","doi":"10.1016/j.segan.2025.101888","DOIUrl":"10.1016/j.segan.2025.101888","url":null,"abstract":"<div><div>The new outlook regarding energy and climate change encourages electricity companies to increase the renewable power capacity and improve the infrastructure to manage and transport renewable energy. The increase in renewable energy, especially wind generation, together with the growth of distributed generation, creates the need to provide the flexibility to operate a grid. The economic, environmental and administrative barriers to creating new infrastructure or modifying existing infrastructure encourage the development of alternatives such as Dynamic Line Rating (DLR) systems. This study solves one of the problems that appear in the practical application of DLR systems. The aim of this study is to create a new methodology that allows the analysis of the error caused by an existing configuration of a DLR system and to determine the most appropriate number and location of measurement points during the design phase. These approaches are based on the Simulated Wind Distributed Estimation (SWDE) methodology, which obtains a cooling model along the line using wind propagation software and Digital Elevation Models.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101888"},"PeriodicalIF":5.6,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144895562","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-driven hidden solar PV and energy storage capacity estimation from the net-load of active distribution systems","authors":"Wutthipum Kanchana,&nbsp;Jai Govind Singh,&nbsp;Weerakorn Ongsakul","doi":"10.1016/j.segan.2025.101940","DOIUrl":"10.1016/j.segan.2025.101940","url":null,"abstract":"<div><div>The proliferation of distributed energy resources (DER), particularly solar photovoltaic (PV) systems, has introduced challenges in managing active distribution systems. Due to their behind-the-meter installation, network operators often lack visibility in PV generation. Accurate net-load forecasting, which considers both load demand and DG output, is essential for ensuring grid stability and reliability. This research presents a data-driven approach to address these challenges. A novel method is proposed for estimating the capacity of DER, including PV and energy storage systems (ESS). Furthermore, a reinforcement learning-based ESS control strategy is devised to maximize the economic benefits of PV-battery integrated systems. A deep learning-based long short-term memory and Gated Recurrent Unit model is developed for net-load forecasting. Finally, to enhance model performance and reduce computational complexity, feature selection is implemented using the Shapley value technique. Simulation results demonstrate that the proposed approach achieves absolute percentage errors of 4.72 % and 47.87 % in PV and ESS capacity estimation, respectively. The proposed charging strategy increases the annual return of the PV-battery integrated system by an average of 1304 THB/kWp. Additionally, annual ESS utilization is reduced by an average of 2.91 % with the proposed strategy.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101940"},"PeriodicalIF":5.6,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144895563","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 imputation of missing offshore wind speed based on conditional diffusion models","authors":"Junhao Zhao ,&nbsp;Xiaodong Shen ,&nbsp;Tingjian Liu ,&nbsp;Junyong Liu ,&nbsp;Xisheng Tang","doi":"10.1016/j.segan.2025.101949","DOIUrl":"10.1016/j.segan.2025.101949","url":null,"abstract":"<div><div>The growing demand for reliable renewable energy underscores the pivotal role of offshore wind power, renowned for its consistent and robust wind speeds. However, harsh weather conditions often lead to sensor failures and communication disruptions at sea, resulting in missing wind speed data. Such data gaps significantly hinder the accuracy of wind power forecasting, power curve modeling, and energy assessments of wind turbines—critical tasks for efficient operation and maintenance. To address these challenges, this work introduces an innovative imputation framework for missing wind speed data in offshore wind farms, leveraging a conditional diffusion model. By framing the imputation as a conditional generation problem, the approach employs multi-head attention mechanisms and graph convolutional networks to effectively capture spatiotemporal correlations and generate context-aware information. A denoising network then transforms random noise into accurate estimates for the missing values, while adaptive bandwidth kernel density estimation (ABKDE) is used to estimate the distribution of missing wind speed, providing probabilistic intervals for imputation. Extensive experiments on real-world datasets across a variety of missing data scenarios demonstrate that the proposed method outperforms existing benchmarks. Not only does it yield precise deterministic imputation results, but it also quantifies uncertainty by providing probabilistic intervals for the imputed values. This significantly enhances the reliability and accuracy of wind speed imputation. Experiments have demonstrated that the proposed method is particularly effective in handling complex missing data patterns, such as those caused by long-term sensor failures or extreme weather events, and it can improve the performance of downstream prediction tasks. This work provides a novel and robust solution for missing data imputation in offshore wind farms, offering more reliable and interpretable results for downstream tasks, including risk management and wind power optimization.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101949"},"PeriodicalIF":5.6,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926179","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":"User-centered decentralized P2P energy trading model for managing line congestion in energy communities","authors":"Sebastián San Martín ,&nbsp;Fernando García-Muñoz ,&nbsp;Franco Quezada ,&nbsp;Sebastián Dávila","doi":"10.1016/j.segan.2025.101931","DOIUrl":"10.1016/j.segan.2025.101931","url":null,"abstract":"<div><div>This paper presents a user-centered, fully decentralized framework to allow an energy community (EC) to self-manage line congestion issues through peer-to-peer (P2P) energy trading and a flexibility market using the users’ distributed energy resources (DERs) assets to take an energy seller (buyer) role when they have a surplus (deficit). A three-stage optimization-based model is introduced to consider the users’ preferences and identify line congestion issues using the Distflow model to evaluate the distribution network (DN) limitations. In this regard, users maximize their benefits in the first optimization stage by optimizing their DER operation. In the second stage, the distribution system operator (DSO) solves an optimal power flow model to identify potential congestion given the users’ preferences. If congestion occurs, the third stage activates a P2P energy and flexibility market designed to resolve the issue by minimizing deviations from the users’ initial preferences. To achieve full decentralization, a two-step alternating direction method of multipliers (ADMM) algorithm is employed: the first step addresses optimal power flow, while the second manages the P2P and flexibility market. Tests were conducted on a 33-bus DN for different DER penetration levels, showing that the methodology efficiently meets energy requirements while respecting the network’s physical constraints and improving information security.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101931"},"PeriodicalIF":5.6,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144907287","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 pricing strategy based on day-ahead charging demand forecasts","authors":"Daria Matkovic,&nbsp;Terezija Matijasevic Pilski,&nbsp;Tomislav Capuder","doi":"10.1016/j.segan.2025.101897","DOIUrl":"10.1016/j.segan.2025.101897","url":null,"abstract":"<div><div>This paper introduces a dynamic pricing model to distribute traffic across electric vehicle charging stations using demand forecasts. Analysis of real-world charging station data collected in Croatia from June 2019 to October 2022 shows that some stations experience heavy usage and long wait times, while others remain underutilized. To address this imbalance, the proposed strategy adjusts prices based on day-ahead demand predictions.</div><div>In this study, various time-series forecasting models were compared, and the Seasonal Autoregressive Integrated Moving Average (SARIMA) model demonstrated the best performance based on mean squared error (MSE) and mean absolute error (MAE). Consequently, SARIMA was selected for charging demand forecasting throughout this study.</div><div>The proposed dynamic pricing model aims to distribute charging demand more evenly across all stations, improving overall network efficiency. This model enhances station utilization, increases profitability, improves user satisfaction, and reduces waiting times. Compared to the three alternative models, the proposed approach achieves over a 27 % increase in profitability. Additionally, it enables more than 80 % of EVs to charge at their preferred stations, significantly outperforming other models in meeting user preferences. The model also reduces waiting times across the network by over 90 % compared to the second-best approach. Finally, it demonstrates superior load balancing, achieving more than 8 % improvement in mean load distribution variance over the next best method.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101897"},"PeriodicalIF":5.6,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144907286","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}