Sustainable Energy Grids & Networks最新文献

{"title":"A fully decentralized spatiotemporal decomposition method for real-time peer-to-peer trading in distribution network","authors":"Jianquan Zhu,&nbsp;Wenhao Liu,&nbsp;Langsen Fang,&nbsp;Ruibing Wu,&nbsp;Jiajun Chen","doi":"10.1016/j.segan.2025.101729","DOIUrl":"10.1016/j.segan.2025.101729","url":null,"abstract":"<div><div>High penetration of distributed renewable energy (DRE) promotes the development of peer-to-peer (P2P) trading. In this study, P2P trading is extended from a deterministic single-period model to a stochastic multi-period model, which considers the interaction of both spatial and temporal dimensions. A stochastic dual dynamic programming (SDDP)-based decentralized method is proposed to coordinate this spatiotemporal effect. In the spatial dimension, the impact of bilateral transactions on power flow is considered by prosumers independently based on the cumulative effect of branch capacity and bus voltage shift, which protects the privacy of P2P trading while preventing power flow violation. In the temporal dimension, the influence between periods is coordinated by prosumers based on the state of charge (SOC), which gives them overview abilities to handle future uncertainties. Besides, the dynamic cut-set selecting strategy is presented to improve the solving efficiency of SDDP. Numerical simulations demonstrate the effectiveness of the proposed method, which can reduce the computational time by 52 %.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"43 ","pages":"Article 101729"},"PeriodicalIF":4.8,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144070579","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 scalable and flexible solution to evaluate the effects of the integration of photovoltaic distributed generation systems within the electrical grid","authors":"Marco Massano ,&nbsp;Carlos Mateo Domingo ,&nbsp;Enrico Macii ,&nbsp;Edoardo Patti ,&nbsp;Lorenzo Bottaccioli","doi":"10.1016/j.segan.2025.101732","DOIUrl":"10.1016/j.segan.2025.101732","url":null,"abstract":"<div><div>This study introduces a novel methodological approach for evaluating the impacts of distributed photovoltaic (PV) generation systems within Urban Energy Systems (UES) on the distribution grid at an infrastructural level by generating synthetic electricity networks. The methodology integrates Geographic Information System (GIS)-based procedures, simulation techniques, and energy models to provide a comprehensive tool for analyzing electricity power flows at a high spatio-temporal resolution.</div><div>The study emphasizes the potential for localized energy sharing and the formation of Energy Communities. The adaptable platform supports operational and planning activities, offering detailed analyses for various urban settings. The methodology provides a valuable tool for identifying and mitigating the challenges posed by distributed PV systems, such as reverse power flow, line congestion, and over-voltage problems.</div><div>A case study focusing on the city of Turin was conducted, wherein a synthetic network of a specific urban area was created and analyzed. This detailed examination revealed critical network vulnerabilities triggered by the simulated integration of photovoltaic (PV) power, highlighting specific points that require attention to be effectively addressed. Furthermore, the study explores potential interventions to enhance the network’s resilience and efficiency in accommodating distributed renewable energy sources.</div><div>The proposed methodology can be used by Energy Communities, Distribution System Operators, and other stakeholders to evaluate different scenarios, test different aggregations, and design effective control strategies to ensure the stability and reliability of the distribution grid.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"43 ","pages":"Article 101732"},"PeriodicalIF":4.8,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935652","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":"Conductor size selection in radial distribution networks using robust optimization","authors":"Vasko Zdraveski,&nbsp;Mirko Todorovski","doi":"10.1016/j.segan.2025.101730","DOIUrl":"10.1016/j.segan.2025.101730","url":null,"abstract":"<div><div>This paper presents a robust optimization method for solving the conductor size selection problem in radial distribution networks with predefined topologies, considering load uncertainty. The method addresses the increasing unpredictability of power demand driven by electric vehicles and renewable energy sources. We employ the column and constraint generation algorithm, known for its efficiency in robust optimization, ensuring that all technical constraints are met, even under worst-case demand scenarios. The objective function incorporates annual energy losses and construction costs as annualized expenses, offering a balanced evaluation of economic and operational efficiency. Numerical results from case studies demonstrate the method’s effectiveness, showing optimal conductor sizes for various load scenarios and validating its application for reconductoring in medium voltage distribution networks. The results highlight the model’s ability to balance cost and reliability under uncertainty, providing a robust solution for real-world distribution network planning.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"43 ","pages":"Article 101730"},"PeriodicalIF":4.8,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935654","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":"Hybrid game-theoretic security assessment of cyber-physical power systems using partial-information multi-agent reinforcement learning","authors":"Zahra Azimi,&nbsp;Ahmad Afshar","doi":"10.1016/j.segan.2025.101727","DOIUrl":"10.1016/j.segan.2025.101727","url":null,"abstract":"<div><div>In this paper, we develop a Hybrid Non-zero-sum Multi-stage Partial-information Stochastic (HNMPS) game theoretic model for assessing the security of interdependent cyber-physical power systems (CPPS). In the cyber layer, HNMPS encapsulates the discrete dynamics of attacks, with tactics and techniques defined in the ICS MITRE ATT&amp;CK framework. In the physical layer, it evaluates the consequence of Denial of Service (DoS) attacks on the transient stability of power networks through non-linear continuous dynamic analysis. Next, we propose an Imperfect-Information Multi-Agent Q-learning (IMQL) algorithm to solve the game when opposing players' actions and strategies are unknown. Unlike existing methods, IMQL doesn't require joint Nash strategy computation and therefore relaxes the strong assumption on existing global optima or saddle point. We further prove the convergence of the proposed algorithm. Based on the outcome of the HNMPS, we introduce the Cyber Security Index Level (CIL), a novel metric that quantifies the probability of physical layer intrusion following a breach in the cyber layer. To validate our model, we conduct simulations on the Western System Coordinating Council (WECC) 9-bus system coupled with a cyber network, employing an attack scenario inspired by the BlackEnergy v3 malware. Results indicate the successful convergence and robustness of the learning process under partial information settings compared to existing algorithms.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"43 ","pages":"Article 101727"},"PeriodicalIF":4.8,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922175","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":"Assessing the robustness of machine learning-based voltage calculations for LV networks","authors":"Orlando Pereira ,&nbsp;Vincenzo Bassi ,&nbsp;Tansu Alpcan ,&nbsp;Luis F. Ochoa","doi":"10.1016/j.segan.2025.101716","DOIUrl":"10.1016/j.segan.2025.101716","url":null,"abstract":"<div><div>The integration of distributed energy resources (DERs) into low-voltage (LV) distribution networks requires distribution companies to assess customer voltages for new scenarios involving larger generation (from solar PVs) or increased demand (from electric vehicles [EVs]). Voltage calculations typically depend on power flow analyses, which require detailed three-phase electrical models that are often unavailable for LV networks. As an alternative, machine learning (ML) models can leverage historical smart meter data (active power [P], reactive power [Q] and voltage magnitudes [V]) to capture the underlying physics of the LV network and calculate customer voltages for new scenarios without relying on electrical models. However, their robustness in scenarios beyond their training scope is often overlooked, leading to errors in determining how much DER capacity an LV network can handle. This paper evaluates the robustness of ML-based voltage calculations using Neural Networks (NNs) and Linear Regression (LR), in scenarios that remain within (in-domain) and beyond (out-of-domain) the historical data ranges, such as having more solar PV or EVs. A voltage sensitivity analysis assesses each model’s ability to capture the network’s response to changes in P and Q. The study uses synthetic data from a realistic Australian LV network comprising 31 single-phase customers and 25 % PV penetration. Results indicate that LR models calculate voltages more accurately than NNs, especially in out-of-domain scenarios, although all models exhibit limitations in capturing the network’s sensitivity to P and Q. These findings highlight the need for improving ML models to ensure reliable voltage calculations for applications involving DER integration.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101716"},"PeriodicalIF":4.8,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143903857","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":"Predictive demand analytics and machine learning in electric power systems for enhancing resilience and efficiency","authors":"Wadim Strielkowski ,&nbsp;Andrey Vlasov ,&nbsp;Kirill Selivanov ,&nbsp;Aleksandr Rasuk ,&nbsp;Luboš Smutka","doi":"10.1016/j.segan.2025.101722","DOIUrl":"10.1016/j.segan.2025.101722","url":null,"abstract":"<div><div>Rapid advancements of the Internet of Things (IoT), Artificial Intelligence (AI), cloud computing, and Big Data have significantly accelerated the adoption of predictive analytics within electric power systems. The integration of predictive analytics offers substantial opportunities for automating control and monitoring processes, thereby enhancing both the resilience and operational efficiency of power grids. This paper introduces a novel predictive analytics framework that uniquely integrates supervised and unsupervised machine learning methods, specifically linear and logistic regression, decision trees, random forests, and clustering algorithms, to simultaneously predict short-term power demand and accurately detect early signs of short circuits and system faults. Utilizing the grid load data from the U.S. Department of Energy's Open Energy Data Initiative (OEDI), our research systematically illustrates the implementation, optimization, and integration of selected machine learning algorithms specifically tailored for power systems. Our empirical results demonstrate substantial efficiency improvements in electric power systems ranging from 14 % to 24 %, with measurable enhancements across reliability indices, economic savings, reductions in environmental impact (lower greenhouse gas emissions), and optimized infrastructure utilization. Furthermore, the paper explicitly addresses regulatory hurdles and industry adoption challenges, outlining how predictive analytics can strategically facilitate technology integration in traditionally conservative power sectors. Finally, the paper provides deeper theoretical synthesis and proposes several specific future research avenues, emphasizing scalability to diverse grid contexts, renewable energy integration, and further exploration of regulatory dynamics. Overall, this study not only highlights the practical benefits of predictive analytics but also significantly contributes to theoretical advancements, strategic planning, and informed policymaking within the energy sector.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101722"},"PeriodicalIF":4.8,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143899317","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":"Goal-oriented heuristic dynamic programming for scheduling of virtual energy hubs with management of intelligent parking lot","authors":"Qingwen Fu ,&nbsp;Yanbo Ding ,&nbsp;Yongfeng Wang ,&nbsp;Liping Yu","doi":"10.1016/j.segan.2025.101718","DOIUrl":"10.1016/j.segan.2025.101718","url":null,"abstract":"<div><div>The increasing complexity of multi-carrier energy systems (MCESs) has introduced significant challenges in the current energy systems necessitates seamless integration and optimization. To address this issue, this work proposes an effective configuration of a virtual energy hub (VEH) to schedule and make multi-energy systems active inside energy markets. In particular, the framework of VEH dynamically schedules the system in such a way that addresses the management of intelligent parking lot (IPL) alongside the MCESs context. The proposed structure of VEH not only facilitates the optimal operation of MCES but also formulates an advanced model to integrate the EV unit into the studied energy plant. This model of VEH is effectively developed to operate within both the thermal and electrical market where it brings more flexibility and efficient energy interactions. To demonstrate the real-world applicability of the proposed structure, this model considers the inherent uncertainties corresponding to the EV behavior, including stochastic changes in their arrival and departure times, and their state of charge (SOC). Furthermore, the electrical energy generated by renewable resources and energy prices have an uncertainty factor which imposes more complexity to the model of VEH. The Goal-Oriented Heuristic Dynamic Programming (Go-HDP) is adopted to solve the scheduling problem to reach the maximum profit from the integrated system. In this framework, the Go-HDP with multi-neural nets (goal, critic, and action nets) is utilized to dynamically respond to the system requirements by interacting as an agent with the environment. By maximizing a reward function that is defined based on the system characteristics, the neural nets of Go-HDP are trained in the iterative process. The comprehensive simulation examinations under typical scenarios of the virtual hub are made to ascertain the feasibility of the proposed framework.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101718"},"PeriodicalIF":4.8,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143899318","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":"Time-varying probabilistic models for incipient fault in underground cables","authors":"Zahra Hosseini ,&nbsp;Haidar Samet ,&nbsp;Masoud Jalil ,&nbsp;Teymoor Ghanbari ,&nbsp;Mehdi Allahbakhshi","doi":"10.1016/j.segan.2025.101717","DOIUrl":"10.1016/j.segan.2025.101717","url":null,"abstract":"<div><div>The incipient faults in underground cables are mainly caused by cable insulation failure, defects in splices, and water penetration. Incipient fault modeling is essential to ensure the algorithms' performance and accuracy in detecting incipient faults or generating data under various conditions. This article aims to create and develop a robust yet practical model for incipient faults by considering actual recorded data. Experimental records derived from a laboratory setup are used in the models' identification procedure. Considering that there is no arc model for the incipient fault in underground cables, this article concentrates on driving effective models based on Schwarz equations for incipient fault using actual recorded data. Three modified Schwarz models for modeling the voltage and current of incipient faults in cables are presented. In the proposed models, the idea of time-varying parameters is used to show the time-varying properties of incipient faults. The models' parameters are updated using the least squares method for each cycle of power frequency. The best order of each model is determined using two error indices. Since the model parameters change in every cycle, probability distribution functions (PDFs) were used to show the stochastic behavior of the parameters. As a result, several PDFs are examined for every set of the model's parameters, and the one that best fits the actual data is selected.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101717"},"PeriodicalIF":4.8,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143881841","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":"MPC-based detection and resilience against bounded and unbounded FDI cyberattacks in isolated AC microgrid with battery-source DERs","authors":"Rohit Nandi,&nbsp;Manoj Tripathy,&nbsp;Chandra Prakash Gupta,&nbsp;Ram Singh","doi":"10.1016/j.segan.2025.101719","DOIUrl":"10.1016/j.segan.2025.101719","url":null,"abstract":"<div><div>In microgrid cybersecurity, the detection and resilience scheme against False Data Injection (FDI) cyberattacks is designed to handle either a bounded or unbounded type attack. Thus, the proposed control scheme is designed on reduced-ordered Model Predictive Control (MPC), where the collaboration of Kalman estimation and cost function can identify both kinds of FDI attacks targeted on the actuator and sensors of the Inverter-based Distributed Energy Resources (DERs). The MPC provides quick recovery and is unaffected by variable load and battery energy dissimilarity. However, after resiliency from FDI attacks, a conventional consensus with power-frequency droop characteristics (<em>P-f</em>) usually suffers from frequency fluctuation and power-sharing error. Consequently, the proposed secondary control system is designed on a three consensus coordination, where frequency coordination is only used to maintain synchronism, voltage angle coordination is designed for accurate active power sharing with State-of-Charge (SoC) balancing and as usual, the voltage coordination is for reactive power sharing among battery source voltage controlled DERs. The Lyapunov function on coordination schemes is used to analyze the stability and constraints. The performance includes MATLAB simulation of an AC microgrid built on a CIGRE European LV testbed. However, experimental validation is based on a cyber-physical system built with a Programmable Logic Controller (PLC) and Supervisory Control and Data Acquisition (SCADA) having a Common Industrial Protocol (CIP) for communication.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"43 ","pages":"Article 101719"},"PeriodicalIF":4.8,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143906335","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 coordinated charging/discharging strategy for EVs with flexible mobility in multi-temporary microgrids","authors":"Zhixian Wang ,&nbsp;Davide Falabretti ,&nbsp;Ying Wang ,&nbsp;Kaifeng Zhang","doi":"10.1016/j.segan.2025.101720","DOIUrl":"10.1016/j.segan.2025.101720","url":null,"abstract":"<div><div>The increasing occurrence of extreme weather events can lead to more major power blackouts, which attracts attention to the utilization of temporary microgrids (TMGs) in the restoration stage. However, single TMGs originating after a blackout usually have weak resilience, because of their limited available resources. Therefore, cooperation between microgrids needs urgently to be studied: to this end, this paper proposes a novel charging/discharging strategy for electric vehicles (EVs) to achieve optimal power coordination between TMGs, by taking advantage of EV’s temporal-spatial flexible mobility. First, a new user response willingness model for EVs is established considering move distance and charging compensation, and this model is combined with the EV eligibility assessment based on traveling time to evaluate the EV mobile possibility across TMGs. Then, a suitable resilience factor is proposed to measure the resilience of TMGs considering both the current and future operation conditions. Finally, a novel coordinated EV charging/discharging strategy exploiting the EVs’ mobility is developed based on a mixed integer optimization model, to enhance system resilience and reduce total regulation costs. A case study based on the realistic topology of Milan’s urban area is analyzed, comparing the results obtained with the strategy without EV spatial cooperation. As a result, the proposed strategy can enhance the resilience of TMGs by 20.93 % and reduce the total regulation cost which is paid to regulation resources for ensuring the power balance of each TMG by 8.43 %.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101720"},"PeriodicalIF":4.8,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143885979","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}