Sustainable Energy Grids & Networks最新文献

{"title":"Multi-agent double time scale two critic deep reinforcement learning for voltage control in active distribution systems","authors":"Hafiz Mehboob Riaz,&nbsp;Malik Intisar Ali Sajjad","doi":"10.1016/j.segan.2025.102077","DOIUrl":"10.1016/j.segan.2025.102077","url":null,"abstract":"<div><div>Active distribution systems (ADS) encounter significant challenges from severe voltage violations and increased power losses, driven by load variations and the intermittency of distributed and renewable energy sources (DRES). Such voltage violations can be mitigated by coordinating slow and fast voltage regulating devices on their respective time scales, considering their operational characteristics and response time. To address this, a multi-agent double time scale two-critic deep reinforcement learning (MA-DTTC-DRL) approach is proposed in this paper to meet the two objectives of volt/VAR control (VVC)—minimizing voltage violations and reducing power losses in ADS. The proposed method employs a multi-agent distributed control scheme by dividing the distribution network into sub-areas. Rather than combining two VVC objectives into a single critic per agent, this approach uses two centralized critics shared among all the agents, thereby reducing the learning complexity of DRL. The optimal set points of continuous agents including inverter-based distributed generators (IBDGs), and static VAR compensators (SVCs) are adjusted using the deep deterministic policy gradient (DDPG) method, while discrete actions of the capacitor agents are generated using reparameterization with Gumbel SoftMax distribution. The proposed method leverages centralized learning with decentralized execution to jointly manage continuous and discrete actions, enabling the coordinated control of various devices on the double time scale. The proposed method is validated on the modified IEEE 33-bus, 69-bus and 118-bus systems against two DRL methods, namely DDPG and soft actor-critic (SAC). Simulation results demonstrate that the proposed approach not only achieves enhanced voltage regulation and lower power losses but also exhibits faster convergence and improved learning stability compared to baseline DRL methods. Moreover, the centralized critic architecture offers substantial computational advantages, making it suitable for practical implementation in ADS.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"45 ","pages":"Article 102077"},"PeriodicalIF":5.6,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738306","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":"Analysis of the iberian intraday market: Price dynamics, market participation, and balancing challenges","authors":"Santiago Maiz ,&nbsp;Raquel García-Bertrand ,&nbsp;Luis Baringo ,&nbsp;Tarek Alskaif","doi":"10.1016/j.segan.2025.102072","DOIUrl":"10.1016/j.segan.2025.102072","url":null,"abstract":"<div><div>This paper presents an in-depth analysis of the intraday (ID) market within the Iberian electricity market. The study examines price dynamics and the participation of market agents across multiple trading sessions, including both the auction-based intraday (IDA) sessions and the continuous intraday (IDC) market. Additionally, it explores the intricacies of the balancing market, particularly in terms of managing untraded energy from various stages, including the day-ahead (DA) market, the IDA sessions, and the IDC market. Special attention is given to the recent reform of the discrete ID market, which transitioned from six daily sessions to three, as part of its integration into the European single intraday coupling (SIDC) framework. The work also investigates the evolution of price volatility as the delivery hour approaches, and studies market liquidity through two key indicators: the number of matched agents and the traded energy volume in each session. Overall, this research highlights the evolving structure and challenges of the Iberian ID electricity market, offering valuable insights for market participants and policymakers. The results contribute to a better understanding of how the ID market supports vRES integration and short-term system flexibility under increasing uncertainty.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"45 ","pages":"Article 102072"},"PeriodicalIF":5.6,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738364","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":"Integrated optimization and game theory framework for fair cost allocation in community microgrids","authors":"K. Victor Sam Moses Babu ,&nbsp;Pratyush Chakraborty ,&nbsp;Mayukha Pal","doi":"10.1016/j.segan.2025.102076","DOIUrl":"10.1016/j.segan.2025.102076","url":null,"abstract":"<div><div>Fair cost allocation in community microgrids remains a significant challenge due to the complex interactions between multiple participants with varying load profiles, distributed energy resources, and storage systems. Traditional cost allocation methods often fail to adequately address the dynamic nature of participant contributions and benefits, leading to inequitable distribution of costs and reduced participant satisfaction. This paper presents a novel framework integrating multi-objective optimization with cooperative game theory for fair and efficient microgrid operation and cost allocation. The proposed approach combines mixed-integer linear programming (MILP) for optimal resource dispatch with Shapley value analysis for equitable benefit distribution, ensuring both system efficiency and participant satisfaction. The framework was validated using real-world data across six distinct operational scenarios, demonstrating significant improvements in both technical and economic performance. Results show peak demand reductions ranging from 7.8 % to 62.6 %, solar utilization rates reaching 114.8 % through effective storage integration, and cooperative gains of up to $1,801.01 per day. The Shapley value-based allocation achieved balanced benefit-cost distributions, with net positions ranging from −16.0 % to +14.2 % across different load categories, ensuring sustainable participant cooperation.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"45 ","pages":"Article 102076"},"PeriodicalIF":5.6,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738367","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":"EnergyFlow: Predictive trading platform for decentralized energy exchange","authors":"Vidya Krishnan Mololoth,&nbsp;Christer Åhlund,&nbsp;Saguna Saguna","doi":"10.1016/j.segan.2025.102074","DOIUrl":"10.1016/j.segan.2025.102074","url":null,"abstract":"<div><div>The integration of renewable energy sources (RES) into modern power grids has enabled decentralized energy generation at the community level, fostering peer-to-peer (P2P) energy trading among prosumers and microgrids. Accurate forecasting of household energy consumption and photovoltaic (PV) generation is critical for optimizing energy flows, enhancing grid reliability, and enabling cost-effective trading decisions. This paper presents an intelligent energy trading platform that integrates machine learning-based forecasting, battery-aware decision-making, and blockchain-enabled transactions to facilitate secure and efficient local energy exchange. Using historical smart meter and weather data from London households, multiple forecasting models including GRU, LSTM, Random Forest, and XGBoost were trained and evaluated. The GRU model achieved superior performance in predicting energy consumption, while Random Forest produced the most accurate PV generation forecasts. These predictions were combined with household battery levels to dynamically determine next-day operational roles: Buyer, Seller, Store, or Use Battery. Unlike conventional fixed-threshold approaches, the framework supports user-defined variable battery thresholds, allowing personalized energy management strategies. The proposed decision-making model achieved an accuracy of 90.72 % for one random block, and extended simulations across 29 different random household blocks confirmed its robustness with an average accuracy of 88.69 % (95 % CI: 87.9–89.6 %). In the trading phase, households participate in a decentralized energy trading platform powered by blockchain and smart contracts. Based on the next-day forecasts, a linear programming-based optimization algorithm matches buyer requests and seller offers to minimize the total system cost while ensuring fairness and efficient energy allocation. To assess its performance, the proposed optimization approach was compared against a greedy matching algorithm where sequential matching is done without a cost optimization and a grid baseline scenario where no storage/sharing of energy takes place. The optimized matching consistently achieved substantially lower trading costs across all households demonstrating superior efficiency, fairness, and scalability compared to the benchmark methods. All transactions are executed securely and transparently on the blockchain through Ethereum-based smart contracts, which automate energy trading, pricing, and settlement. A user-friendly web interface was developed to allow participants to monitor and interact seamlessly with the platform. Overall, this battery-aware, community-driven trading framework showcases how intelligent energy forecasting, cost-optimized decision-making, and blockchain-enabled trading can collectively enhance energy autonomy, cost savings, and renewable energy utilization at both the household and community levels.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"45 ","pages":"Article 102074"},"PeriodicalIF":5.6,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685700","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 dynamic event-triggered predefined-time secondary control for islanded microgrids with disturbance rejection","authors":"Junjie Guan ,&nbsp;Shiming Chen ,&nbsp;Huijun Xu ,&nbsp;Zheng Zhang ,&nbsp;Xin Huang ,&nbsp;Yang Zhang","doi":"10.1016/j.segan.2025.102068","DOIUrl":"10.1016/j.segan.2025.102068","url":null,"abstract":"<div><div>Conventional distributed secondary control schemes for islanded microgrids suffer from limitations such as the inability to preset convergence time offline, high computational resource consumption, and vulnerability to external disturbances. To overcome these limitations, this paper proposes a novel distributed dynamic event-triggered predefined-time secondary control scheme by combining consensus algorithm, predefined-time control method, and dynamic event-triggered mechanism. This scheme can achieve secondary control objectives within a predefined time while effectively rejecting bounded external disturbances. The minimum upper bound of the convergence time for the proposed scheme can be directly preset via tunable controller parameters and is decoupled from the initial system states. Meanwhile, internal dynamic variables are designed to offer more relaxed triggering conditions, which significantly reduce the number of controller updates. Rigorous Lyapunov-based proofs demonstrate that the proposed scheme guarantees predefined-time convergence and precludes Zeno behavior. Numerical simulations across several scenarios illustrate the effectiveness and superiority of the proposed scheme.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"45 ","pages":"Article 102068"},"PeriodicalIF":5.6,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791161","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":"JuliaGrid: An open-source julia-based framework for power system state estimation","authors":"Mirsad Cosovic ,&nbsp;Ognjen Kundacina ,&nbsp;Muhamed Delalic ,&nbsp;Armin Teskeredzic ,&nbsp;Darijo Raca ,&nbsp;Amer Mesanovic ,&nbsp;Dragisa Miskovic ,&nbsp;Dejan Vukobratovic ,&nbsp;Antonello Monti","doi":"10.1016/j.segan.2025.102073","DOIUrl":"10.1016/j.segan.2025.102073","url":null,"abstract":"<div><div>Modern electric power systems have an increasingly complex structure due to rise in power demand and integration of diverse energy sources. Monitoring these large-scale systems, which relies on efficient state estimation, represents a challenging computational task and requires efficient simulation tools for power system steady-state analyses. Motivated by this observation, we propose JuliaGrid, an open-source framework written in the Julia programming language, designed for high-performance execution across multiple platforms. The framework implements observability analysis, weighted least-squares and least-absolute value estimators, bad data analysis, and various algorithms related to phasor measurements. To complete power system analysis, the framework includes power flow and optimal power flow, enabling measurement generation for the state estimation routines. Leveraging computationally efficient algorithms, JuliaGrid solves large-scale systems across all methods, offering competitive performance compared to other open-source tools. It is specifically designed for quasi-steady-state analysis, with automatic detection and reuse of computed data to boost performance. These capabilities are validated on systems with 10 000, 25 000 and 70 000 buses.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"45 ","pages":"Article 102073"},"PeriodicalIF":5.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685698","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":"Analysis on improvement of photovoltaic hosting capacity through the flexible connection policy","authors":"Jae Hyeon Shin ,&nbsp;Jin Hyeok Kim ,&nbsp;Seung Wan Kim ,&nbsp;Dam Kim","doi":"10.1016/j.segan.2025.102071","DOIUrl":"10.1016/j.segan.2025.102071","url":null,"abstract":"<div><div>The rapid integration of renewable energy sources, including photovoltaics (PV), presents operational challenges for distribution networks, such as reverse power flow, voltage fluctuations, and network congestion. In industrial parks, growing demand for on-site and shared renewables has spurred interest in deploying microgrids, where the concentration of variable generation creates hosting capacity constraints at feeder and substation. Conventional firm connection policies impose strict capacity limits based on worst-case scenarios, delaying interconnection and underutilization of the grid. To address these limitations, this study introduces a time-series bi-level optimization framework for evaluating flexible connection policies that allow controlled PV curtailment. A linearized power flow-based hosting capacity optimization model is developed and applied to evaluate maximum hosting capacity and optimize the siting of PV systems under firm and flexible connection cases. A case study on an IEEE 40-bus networked microgrid system demonstrates that allowing modest annual PV curtailment (1–11 %) can significantly enhance the hosting capacity of the network—up to 45 % greater than that achieved under firm connection approaches—while maintaining or even increasing the total annual renewable generation. Furthermore, an economic analysis reveals that although curtailment may slightly reduce developer profitability, significant savings from deferred grid upgrades provide substantial benefits to both microgrid and distribution system operators. Therefore, we establish a cost-effective pathway for large-scale renewable energy integration by proposing practical incentive mechanisms, such as net present value and benefit-cost ratio-based compensation. These findings emphasize the importance of strategically flexible connection policies in enabling efficient, economical, and high-capacity renewable energy integration into future power grids.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"45 ","pages":"Article 102071"},"PeriodicalIF":5.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685697","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":"Raw measurement supervised learning transformer for anomaly detection of power system digital twin updates","authors":"Zhiwei Shen,&nbsp;Felipe Arraño-Vargas,&nbsp;Georgios Konstantinou","doi":"10.1016/j.segan.2025.102069","DOIUrl":"10.1016/j.segan.2025.102069","url":null,"abstract":"<div><div>Continuous updates are essential to ensure that a digital twin (DT) remains an accurate representation of its physical counterpart. The performance of DT applications heavily relies on how accurately the DT reflects its physical counterpart. DT updates, however, can be compromised by anomalous PT data stemming from physical twin (PT) measurements, communication malfunctions, and/or external attacks. Detecting such anomalies in PT data is crucial to ensuring the accuracy and reliability of DT, thereby generating only valid outcomes for associated applications. This paper proposes a detection method to identify anomalous PT data before its integration into the DT. The proposed raw measurement supervised learning Transformer (RM-SL-TF) facilitates a straightforward identification of PT data using raw measurements, eliminating the dependency on data preprocessing. The feasibility and effectiveness of the RM-SL-TF are demonstrated by using a power system digital twin (PSDT) that requires frequent updates. The resulting detection accuracy of anomalous PT data is comparable to, or even surpasses, that of other artificial intelligence (AI) algorithms that rely on input feature normalisation. By directly analysing raw measurements without normalising input features, the proposed approach is simpler, more flexible, and expandable, making it suitable for establishing and advancing the development and implementation of DTs for power systems and other industries.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"45 ","pages":"Article 102069"},"PeriodicalIF":5.6,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738369","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":"Pricing mechanism of localized distributed trading for household PV storage systems considering multi-agent interests","authors":"Weijun Wang,&nbsp;Xinyu Wang,&nbsp;Haifeng Wang","doi":"10.1016/j.segan.2025.102035","DOIUrl":"10.1016/j.segan.2025.102035","url":null,"abstract":"<div><div>The localized distributed trading model can effectively enhance power trading efficiency, reduce transaction costs, alleviate the operational pressure on public power grids, and facilitate the local consumption and rapid development of distributed energy. However, the core aspects of this model—such as the electricity pricing mechanism and transaction settlement—remain insufficiently defined, and the existing pricing strategies exhibit certain irrationalities. To address these issues, this study proposes a localized distributed trading for residential photovoltaic (PV)–storage systems that accounts for the interests of multiple stakeholders, coupled with a pricing mechanism incorporating demand-side response (DSR). An optimization model for the pricing mechanism is established with the dual objectives of maximizing the annual net profit of residential PV–storage systems and achieving the highest PV utilization rate. The study introduces an optimal period-partitioning method based on moving boundary techniques to segment PV generation levels into discrete time intervals, and applies a fuzzy Newton–Raphson algorithm combined with PSO to solve the model. This approach yields both the load distribution under DSR and the optimal trading price for the localized distributed trading model. Simulation results demonstrate that the proposed method increases the local PV consumption rate from 41.93 % to 78.49 %, boosts the revenue of residential PV–storage systems by 104.59 %, and reduces the overall electricity cost for residents by 29.66 %. These findings highlight the potential of the proposed model to promote the advancement of localized distributed trading and to contribute positively to China’s energy transition and green, low-carbon development.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 102035"},"PeriodicalIF":5.6,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465206","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":"An effective integrated optimal day-ahead and real-time power scheduling approach for hydrogen-based microgrid","authors":"Pasquale Vizza,&nbsp;Stanislav Fedorov,&nbsp;Anna Pinnarelli,&nbsp;Vittorio Bilotta,&nbsp;Maria Elena Bruni","doi":"10.1016/j.segan.2025.102039","DOIUrl":"10.1016/j.segan.2025.102039","url":null,"abstract":"<div><div>The increasing penetration of renewable energy sources in power systems poses significant challenges for maintaining grid reliability, mainly due to the variability and uncertainty of solar and demand profiles. Microgrids, equipped with diverse storage technologies, have emerged as a promising solution to address these issues.This paper proposes an integrated day-ahead and real-time power scheduling approach for grid-connected microgrids equipped with both conventional and hydrogen-based ESSs. While existing strategies often address day-ahead and real-time scheduling separately or rely on a single storage technology, this work introduces a unified framework that exploits the complementary characteristics of batteries and hydrogen systems. The proposed approach is based on a novel two-stage stochastic optimization model, embedded within a hierarchical optimization framework to address these two intertwined problems efficiently. For the day-ahead scheduling, a two-stage stochastic programming energy management model is solved to optimize the microgrid schedule based on forecasted load demand and PV production profiles. Building upon the day-ahead schedule, another optimization model is solved, which addresses real-time power imbalances caused by deviations in actual PV production and load demand power profiles with respect to the forecasted ones, with the aim of minimizing operational disruptions. Simulation results demonstrate the validity of the proposed approach, achieving both cost reductions and minimal power imbalances. By dynamically adjusting energy flows and using both conventional batteries and hydrogen systems, the proposed approach ensures improved reliability, reduced operational costs, and enhanced integration of RES in microgrids. These findings highlight the potential of the proposed hierarchical framework to support the large-scale deployment of RES while ensuring resilient and cost-effective microgrid operations.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 102039"},"PeriodicalIF":5.6,"publicationDate":"2025-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465739","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}