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":"2026-03-01","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":"Low-carbon and robust economic scheduling of virtual power plants considering multiple uncertainties","authors":"Yongcan Zhu,&nbsp;Naying Wei,&nbsp;Junjun Kang,&nbsp;Yi Tian","doi":"10.1016/j.segan.2025.102104","DOIUrl":"10.1016/j.segan.2025.102104","url":null,"abstract":"<div><div>The uncertainties of distributed generation, power load, and electricity price forecasts pose significant challenges for optimal dispatching of load virtual power plants (VPPs). This study addresses these issues by introducing a two-stage robust economic optimization scheduling model based on information gap decision theory (IGDT). Initially, a deterministic VPP objective function is formulated to minimize operating and carbon trading costs while defining constraints for each participating element. Subsequently, a robust VPP scheduling model is developed using IGDT to quantify uncertainties in wind power, solar power, load, and electricity market price predictions. The Karush–Kuhn–Tucker conditions are applied to simplify the optimization model under both risk aversion and risk pursuit behaviors. The effectiveness of the proposed model is validated through a comparative analysis of VPP scheduling results and total costs across different scenarios using real VPP case studies. The results indicate that participation in the carbon trading market led to a reduction in carbon emissions by 17.34 %–23.57 %. The introduction of demand response averaged a 39.18 % reduction in system total costs. The risk pursuit model, considering multiple uncertainties, reduced the total costs by 25.46 %–30.00 %.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"45 ","pages":"Article 102104"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884016","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 strategies for electric vehicle charging: Enhancing cost-reflectivity and revenue stability","authors":"Toni Simolin ,&nbsp;Tim Unterluggauer ,&nbsp;Mattia Secchi ,&nbsp;Francesco Pastorelli ,&nbsp;Mattia Marinelli ,&nbsp;Pertti Järventausta","doi":"10.1016/j.segan.2025.102082","DOIUrl":"10.1016/j.segan.2025.102082","url":null,"abstract":"<div><div>Public charging infrastructure is essential for accelerating electric vehicle (EV) adoption. Currently, in Europe, customers are often offered fixed charging prices, while the costs incurred by charging site owners (CSOs) vary significantly due to factors such as electricity prices and power grid tariffs. This paper proposes alternative pricing solutions to improve cost-reflectivity based on an analysis of the current pricing landscape and related scientific literature. Simulations are carried out, using Danish and Finnish charging session data of multiple locations and electricity price databases, to assess the impact of the proposed pricing solutions on CSO revenues and their potential implications for the charging service business model. The findings indicate that dynamic cost-reflective pricing enhances the stability of CSO revenues and allows users to optimise their charging decisions by providing transparency through precise hourly charging costs. Furthermore, the results show that the proposed dynamic pricing schemes provide a competitive economic advantage for the CSO over the competitors using the present pricing schemes. Additionally, the proposed pricing schemes lead to lower charging costs for 53–64 % of the users even if they do not alter their charging behaviour.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"45 ","pages":"Article 102082"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738362","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":"2026-03-01","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":"Low voltage topology mapping through network discovery events applied to AI-based digital twins","authors":"Mesfin Fanuel ,&nbsp;Bassam Mohamed ,&nbsp;Xavier Dominguez ,&nbsp;Pablo Arboleya","doi":"10.1016/j.segan.2026.102129","DOIUrl":"10.1016/j.segan.2026.102129","url":null,"abstract":"<div><div>Accurate knowledge of Low-Voltage (LV) distribution topology is critical for reliable operation, advanced monitoring and large-scale integration of distributed energy resources (DERs). In practice, topology records in GIS/NIS are frequently incomplete or outdated, while field verification remains costly. This paper presents a smart-meter (SM)–driven methodology for LV topology mapping that combines a data-trained surrogate model with physics-inspired sensitivity analysis. A feedforward Deep Neural Network (DNN), trained on historical SM measurements spanning diverse operating conditions (including DER-driven net generation), is used as a model-free digital twin to emulate customer-to-voltage relationships. Virtual Network Discovery Events (NDEs) are then generated by applying controlled perturbations within the surrogate to obtain voltage-response signatures that support topology inference without physical intervention. Phase groups are identified through dimensionality reduction and hierarchical clustering, and customer connectivity is inferred from the similarity structure of the resulting voltage-response signatures. The method is applied independently per feeder, enabling scalable execution across multi-feeder LV networks. Validation on six real feeders from an urban Spanish network demonstrates accurate voltage emulation and high-fidelity phase and topology reconstruction using only existing SM infrastructure.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"45 ","pages":"Article 102129"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147395724","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 game-theoretic model for flexibility-constrained renewable energy communities in local energy trading with smart distribution networks","authors":"Sahar Mobasheri ,&nbsp;Masoud Rashidinejad ,&nbsp;Amir Abdollahi ,&nbsp;Mojgan MollahassaniPour ,&nbsp;Sobhan Dorahaki","doi":"10.1016/j.segan.2025.102105","DOIUrl":"10.1016/j.segan.2025.102105","url":null,"abstract":"<div><div>Renewable Energy Communities (RECs) play a critical role in advancing the energy transition towards a decentralized, distributed, and increasingly digitalized energy system. Through local energy trading within the distribution network, RECs have the potential to significantly enhance the flexibility of the energy system. This interaction, however, introduces complex challenges between REC operators and distribution network operators, necessitating robust analytical approaches. Leveraging Stackelberg game theory, this study models the hierarchical relationship between these entities, positioning the REC operator as the leader and the distribution network operator as the follower. To address the inherent uncertainties in renewable energy resources, Multi-Objective Information Gap Decision Theory (MO-IGDT) is employed, alongside flexibility constraints to ensure stability and efficiency in the system amidst fluctuations in REC output power. A bilevel optimization model, initially formulated as a mixed-integer linear program, is simplified into a single-level problem using Karush-Kuhn-Tucker (KKT) conditions. The findings underscore the benefits of integrating Community Energy Storage (CES) with renewable energy sources within an REC, demonstrating a 3.39 % increase in profits and a significant 51.23 % reduction in dependency on the upstream grid, highlighting the potential of RECs to enhance both economic and operational resilience in modern energy systems.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"45 ","pages":"Article 102105"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal capacity planning for grid‐connected power‐to‐hydrogen integrated energy system considering dynamic hydrogen production efficiency","authors":"Jizhe Dong ,&nbsp;Chongshan Xu ,&nbsp;Meng Zhu ,&nbsp;Yanbin Zhang ,&nbsp;Zehao Zhao ,&nbsp;Ziyang Hao ,&nbsp;Shunjie Han","doi":"10.1016/j.segan.2026.102120","DOIUrl":"10.1016/j.segan.2026.102120","url":null,"abstract":"<div><div>With the rapid expansion of hydrogen energy applications, the demand for high-precision modeling of hydrogen production systems has become increasingly urgent. In capacity planning for integrated energy systems (IESs), neglecting dynamic hydrogen production efficiency (DHPE) leads to planning results that deviate from the actual performance and impair the resource allocation. This paper proposes a capacity planning model for power-to-hydrogen IES that accounts for DHPE by incorporating the nonlinear relationship between the input power of an electrolyzer and its production efficiency. Additionally, a solution method is presented to address the problems of the model being unsolvable and having slow solution speed. Case studies, based on real operational and publicly available data, demonstrate that the DHPE model generates more reasonable planning solutions than the static hydrogen production efficiency model, and the operational levelized cost of hydrogen is reduced by approximately 0.3 %–3.9 %, while the renewable energy self-consumption increases by approximately 2.5 %–6.5 %.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"45 ","pages":"Article 102120"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146022908","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":"Short-term optimal scheduling of wind-solar-hydro-storage systems under extreme heat scenarios with uncertainty consideration","authors":"Mingyue Zhang ,&nbsp;Yang Han ,&nbsp;Te Zhou ,&nbsp;Yongchao Sun ,&nbsp;Huaiyu Zhang ,&nbsp;Congling Wang ,&nbsp;Fan Yang","doi":"10.1016/j.segan.2025.102096","DOIUrl":"10.1016/j.segan.2025.102096","url":null,"abstract":"<div><div>Extreme heat events threaten power system reliability by reducing hydropower output and intensifying load peaks. This study proposes a short-term scheduling framework for wind-solar-hydro-storage systems under such conditions. A hybrid forecasting model integrating bidirectional temporal convolutional networks (BiTCN), bidirectional long short-term memory (BiLSTM) with attention mechanism, and quantile regression forest (QRF) is developed to jointly predict wind speed, solar irradiance, and power load, thereby providing probabilistic scenarios. Based on these forecasts, a two-timescale scheduling framework is established, where the day-ahead stage employs an <em>ε</em>-constraint multi-objective programming approach to balance hydropower regulation, renewable energy absorption, and output smoothness, while the intraday stage adopts a rolling chance-constrained model updated every 15 min. To enhance climate adaptability, two adaptive modules are incorporated: an <em>ε</em>-bound feedback mechanism based on plan deviations and a thermal correction model utilizing the human comfort index to adjust temperature-sensitive outputs. A case study conducted on the Xiluodu Hydropower Station in Sichuan Province, China, under the extreme heat conditions of summer 2022 validates the effectiveness of the proposed framework. Tested on the highly fluctuating wind-speed dataset, the proposed BiTCN-BiLSTM-AM model achieves an R² of 0.930, representing improvements of 0.032 and 0.039 over the TCN-LSTM-AM and Transformer models, respectively. In terms of dispatch performance, compared with no-storage and static-dispatch strategies, renewable utilization increases from 92.023 % and 93.692–100 %, with total generation gains of 102.489 MW and 117.101 MW. These results demonstrate that the proposed approach enables robust, adaptive, and climate-resilient scheduling for clean-energy-dominated power grids.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"45 ","pages":"Article 102096"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738361","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 sequential conic programming algorithm for calculating voltage stability margins","authors":"Long Fu ,&nbsp;Gexiang Zhang ,&nbsp;Jianping Dong ,&nbsp;Gang Wang ,&nbsp;Zhao Yang Dong ,&nbsp;Yaran Li","doi":"10.1016/j.segan.2025.102108","DOIUrl":"10.1016/j.segan.2025.102108","url":null,"abstract":"<div><div>With the increasing power demand and major blackout events, power systems are operating under more stressed conditions, approaching their stability limits. Voltage stability margin (VSM) characterizes a measure of distance to the power flow insolvability/infeasibility boundary that needs to be precisely calculated and effectively monitored, yet it can be challenging considering varying operational constraints and loading scenarios. Focusing on static power flow equations in this paper, a novel sequential conic programming (SCP) algorithm is proposed based on linear approximations of non-convex functions for an optimization-based VSM calculation. Compared with existing methods, the performance of proposed SCP is more robust against different operating scenarios where desired features of being initialization-free, exact, scalable, and applicable can be appropriately achieved. Multiple test cases validate the advantages and effectiveness of the proposed approach.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"45 ","pages":"Article 102108"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884018","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":"Enhancing grid resilience to extreme events: A synergistic framework integrating vegetation dynamics and microgrid capabilities","authors":"Umar Salman ,&nbsp;Zongjie Wang","doi":"10.1016/j.segan.2025.102094","DOIUrl":"10.1016/j.segan.2025.102094","url":null,"abstract":"<div><div>Grid resilience against extreme weather events is critical for utilities and operators. Overhead distribution lines are particularly vulnerable due to secondary damage caused by falling trees or branches during such events. This paper proposes a vegetation dynamics integrated-resilience assessment framework incorporating microgrid capabilities to address these challenges. The methodology introduces a tree failure model that accounts for tree characteristics in assessing pole and line fragility. Grid resilience is evaluated under four extreme event scenarios, superstorms, hurricanes, earthquakes, and ice storms, considering both islanded and microgrid-operating conditions. Simulation case studies on an IEEE 69-node radial distribution system, performed using Monte Carlo simulations, have demonstrated the effectiveness of the vegetation dynamics integrated-resilience assessment framework in integrating vegetation dynamics for comprehensive vulnerability assessments of power systems. Across cases and events, distributed generation reduced EDNS by 60 %–100 % and LOLP by 60 %–95 %, with the largest gains in hurricane/earthquake conditions, underscoring the importance of DG siting relative to event centers and network bottlenecks. This approach provides practical insights for mitigating disruptions in power distribution systems caused by extreme events.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"45 ","pages":"Article 102094"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884019","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}