Sustainable Energy Grids & Networks最新文献

{"title":"Enhancing resilience and cost efficiency in multi-microgrids through peer-to-peer energy trading and decentralized energy management systems","authors":"Wei Yu,&nbsp;Wenjian Wang","doi":"10.1016/j.segan.2025.102116","DOIUrl":"10.1016/j.segan.2025.102116","url":null,"abstract":"<div><div>The study proposes a two-stage decentralized energy management system for multi-microgrids, includes peer-to-peer trading and resilient network reconfiguration. Physical constraints such as radiality, voltage stability, and line capacity are implemented in first stage to ensure safe operation in both normal and faulty conditions. In second stage, market-clearing mechanism enables supply-demand bidding and zonal cost allocation for multi-bilateral trades while maintains grid and marginal pricing. Renewable uncertainty is modeled using an upper-quantile approach from historical data to balance robustness and economic efficiency without probability distributions. Financial incentives are used in incentive-based demand response following faults to shift or curtailment loads. The model is implemented on a modified IEEE-33 bus system with three microgrids and 36 residential households equipped with photovoltaic panels, wind turbines, battery energy storage, and electric vehicles. Simulation results show that under normal conditions, grid reliance for residential loads is reduced by 43.66 %. During grid and line outages, demand falls by 16.33 % while grid usage increases modestly to 53.16 %. When distributed generators fail, peer to peer energy sharing within the faulty zone rises by a factor of 2.5, supported by battery and electrical vehicle discharges. The proposed framework thus enhances resilience, lowers operating costs, and strengthens local energy self-sufficiency through coordinated P2P trading, flexible storage, and fault-tolerant scheduling.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"45 ","pages":"Article 102116"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926049","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":"Supervised learning-driven dead band control of occupant thermostats for energy-efficient residential HVAC","authors":"Alper Savasci ,&nbsp;Oguzhan Ceylan ,&nbsp;Sumit Paudyal","doi":"10.1016/j.segan.2025.102110","DOIUrl":"10.1016/j.segan.2025.102110","url":null,"abstract":"<div><div>Heating, ventilation, and air conditioning (HVAC) systems play a crucial role in demand-side management (DSM) by shaping residential electricity consumption and enabling flexible, grid-responsive operation. Thermostats in HVAC systems regulate indoor temperature as part of a closed-loop control framework, typically incorporating a fixed temperature dead band–a range around the setpoint where no action is taken–to reduce energy use and prevent frequent cycling of the HVAC system. Although essential for efficiency and equipment longevity, fixed dead bands limit adaptability, as dynamically adjusting them under varying environmental conditions remains challenging for occupants. To address this limitation, we propose a machine learning (ML)-based dead band tuning framework that optimally adjusts thermostat settings in real time. The method integrates conventional optimization with data-driven modeling: a mixed-integer linear programming (MILP) model is first used to generate optimal dead band values under measured outdoor temperature records (diverse seasonal weather scenarios) which are then employed to train the ML-based predictor to learn a real-time discrete dead band decision policy that approximates the MILP-optimal hysteresis-aware decisions. Among the evaluated models, Random Forest demonstrates superior predictive performance, achieving a mean squared error (MSE) of 0.0399 and a coefficient of determination (<span><math><msup><mi>R</mi><mn>2</mn></msup></math></span>) of 95.75 %.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"45 ","pages":"Article 102110"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884022","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":"Two-Stage Coordinated Scheduling for Enhanced Economic Capability in User-Side Integrated Energy Systems","authors":"Can Chen","doi":"10.1016/j.segan.2025.101956","DOIUrl":"10.1016/j.segan.2025.101956","url":null,"abstract":"<div><div>This study presents a distributionally robust coordinated scheduling framework for user-side integrated energy systems (IES), which incorporates power, thermal, and cooling energy interactions. The core innovation lies in a multi-timescale optimization model that synergistically links monthly-scale strategic planning with day-ahead operational dispatch under uncertainty. A vectorized energy balance formulation captures bidirectional multi-energy flows, while a multi-service energy storage system (ESS) is designed to support arbitrage, peak shaving, and spinning reserve provisioning. To address renewables and demand variability, a distributionally robust chance-constrained programming (DRCCP) model is introduced, accounting for forecast uncertainty via ambiguity sets, which are characterized by moment statistics. The optimization trackable convex is available through a Mahalanobis-norm-based risk bounds. Furthermore, the framework incorporates a degradation-aware ESS cost model based on SOC-dependent wear, which is approximated via a piecewise linear surrogate for integration into MILP solvers. The day-ahead layer dynamically adjusts generator and ESS decisions in response to real-time deviations, constrained by dual-reserve and DR flexibility requirements. To solve this high-dimensional, non-convex problem space efficiently, an enhanced Particle Swarm Optimization (PSO) algorithm is proposed. This includes adaptive inertia weighting, chaotic learning dynamics, and elite-guided perturbation, significantly improving convergence and diversity in multimodal landscapes.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101956"},"PeriodicalIF":5.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145026455","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":"Context-aware state estimation in battery management systems: Leveraging nonlinear dynamics with physics-guided parameter identification","authors":"M.B. Rasheed,&nbsp;Á. Llamazares,&nbsp;R. Gutiérrez-Moreno,&nbsp;M. Ocaña,&nbsp;P. Revenga","doi":"10.1016/j.segan.2025.101979","DOIUrl":"10.1016/j.segan.2025.101979","url":null,"abstract":"<div><div>Black accurate remaining range estimation remains a critical issue to promote plug-in and electric vehicle adoption, primarily due to underlying uncertainties in voltage and current-dependent state estimation. To overcome these challenges, the proposed work introduces a novel framework for range estimation while integrating an enhanced equivalent circuit model with a physics-guided temperature-compensated Extended Kalman Filter algorithm. Firstly, comprehensive mathematical models are developed and validated that integrate: (i) proposed enhanced 3rd-order equivalent circuit modeling (p-eTECM) with control parameter optimization, (ii) data-and-model-driven parameter identification using Coulomb counting and voltage scaling analysis, (iii) comprehensive sensitivity analysis to rank important parameters to improve accuracy, and (iv) application-specific model selection criteria based on performance trade-offs. However, unlike existing frameworks that incorporate higher-order RC models that are universally superior, the proposed work identifies that model selection should be application-dependent for different battery management functions. The novel contributions include: parameter &amp; voltage optimization from the pack-level, while systematically eliminating voltage bias through online parameter optimization, and developing a comprehensive sensitivity analysis algorithm to validate the improvements. The proposed framework demonstrates that parameter calibration is more crucial with capacity correction and voltage scaling, to eliminate systematic biases that render models impractical. This study further reveals that 3rd-order model outperforms in voltage prediction (8.3 % improvement) while the 2nd-order model provides better SOC tracking (13 % improved accuracy), establishing clear application-specific selection criteria. Key results demonstrate that both models have achieved excellent performance in terms of SOC errors (<span><math><mo>&lt;</mo></math></span>0.2 %), and range accuracy (155–170 km) with real-time computational efficiency, validating the practical applicability for diverse battery management applications while providing a systematic methodology for future battery modeling research.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101979"},"PeriodicalIF":5.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266124","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":"2025-12-01","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":"Cooperative trading in smart-grid networks","authors":"Pragya Shrivastava ,&nbsp;Neminath Hubballi ,&nbsp;Sameer G. Kulkarni","doi":"10.1016/j.segan.2025.101965","DOIUrl":"10.1016/j.segan.2025.101965","url":null,"abstract":"<div><div>Continuous Double Auction (CDA) methods provide an efficient mechanism for price matching between buyers and sellers. Hence, these techniques have been adopted in smart-grid and micro-grid scenarios for Peer-to-Peer (P2P) energy trading. However, existing CDA methods either violate the natural ordering of P2P trading or introduce considerable overhead or make unrealistic assumptions about the underlying communication setup. In this paper, we introduce a CDA method involving trading agents that are coordinated with token allocation system for participation in the smart-grid market. Our proposed trading mechanism minimizes the natural order violations in the trading, and can recover from issues like token loss arising due to node or network connectivity issues. We provide algorithms for different operations of the CDA such as token request, token allocation, trading, <em>etc</em>. The proposed CDA mechanism has been evaluated with publicly available energy demand data and compared with another CDA method to show that it reduces natural ordering violations. Subsequently, we describe a variant of the CDA method where bids/asks from different participants are queued up for potential future price matches, thereby elaborating the scope for P2P trade. Both CDA variants can scale to large number of participants and can also work with heterogeneous bidding strategies adopted by clients.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101965"},"PeriodicalIF":5.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099699","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":"Social welfare maximization in unbalanced distribution networks under dynamic pricing and power exchange limits","authors":"Afshin Najafi-Ghalelou ,&nbsp;Mohsen Khorasany ,&nbsp;M.Imran Azim ,&nbsp;Reza Razzaghi","doi":"10.1016/j.segan.2025.101923","DOIUrl":"10.1016/j.segan.2025.101923","url":null,"abstract":"<div><div>This paper presents a two-stage framework to manage bidirectional power exchange between distributed energy resources (DER) and the distribution network. In the first stage, the framework optimizes and schedules DER operations and sets dynamic power exchange prices. The second stage focuses on network constraint management. By shifting from fixed tariffs to dynamic, time-varying pricing, the framework encourages active participation in power exchange among players and the distribution network at the lowest prices. The proposed framework aims to maximize social welfare by coordinating power exchanges, dynamic consumption prices and feed-in tariffs. It seeks to optimize the participation of all involved parties, ensuring effective system management through dynamic pricing and flexible limits on power exchange levels between players and the distribution network. The new pricing scheme is tested with and without profit-seeking community batteries and public charging stations, resulting in a 15.53 %–21.51 % improvement in overall social welfare. The results show that increased player participation enhances social welfare in distribution-level markets without violating any technical constraints of the distribution network.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101923"},"PeriodicalIF":5.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144933102","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":"2025-12-01","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":"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":"2025-12-01","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":"NeuraFlux: A scalable and adaptive framework for autonomous data-driven multi-agent power optimization","authors":"Ysaël Desage ,&nbsp;François Bouffard ,&nbsp;Benoit Boulet","doi":"10.1016/j.segan.2025.101999","DOIUrl":"10.1016/j.segan.2025.101999","url":null,"abstract":"<div><div>NeuraFlux is an open-source, adaptive multi-agent reinforcement learning platform designed to optimize energy management in complex, dynamic environments. It addresses key challenges in coordinating distributed energy resources, including scalability limitations, difficulties in managing competing objectives, and lack of real-time adaptability. This paper presents two primary contributions: the theoretical foundations of NeuraFlux and its significance in modern power systems infrastructure and control, along with a novel training algorithm optimized for real-world deployment performance. Through three case studies—energy storage market arbitrage, heating, ventilation, and air conditioning (HVAC) system control, and electric vehicle grid integration—NeuraFlux’s effectiveness in managing intricate, multi-agent, and multi-objective optimization challenges is demonstrated. The modularity and scalability demonstrated in these examples, combined with the framework’s technical robustness for edge deployment, establish NeuraFlux as a powerful and practical tool for deploying advanced control systems in modern power and energy systems.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101999"},"PeriodicalIF":5.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145323698","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}