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.
{"title":"JuliaGrid: An open-source julia-based framework for power system state estimation","authors":"Mirsad Cosovic , Ognjen Kundacina , Muhamed Delalic , Armin Teskeredzic , Darijo Raca , Amer Mesanovic , Dragisa Miskovic , Dejan Vukobratovic , 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":"2026-03-01","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}
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.
{"title":"EnergyFlow: Predictive trading platform for decentralized energy exchange","authors":"Vidya Krishnan Mololoth, Christer Åhlund, 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":"2026-03-01","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}
Pub Date : 2026-03-01Epub Date: 2025-12-13DOI: 10.1016/j.segan.2025.102092
Etiane O.P. Carvalho , Wandry R. Faria , Leonardo H. Macedo , Gregorio Muñoz-Delgado , Javier Contreras , Benvindo R. Pereira Junior , João Bosco A. London Junior
This paper introduces a bilevel programming model for service restoration in distribution systems, integrating private distributed generations (DGs) and market strategies. The upper-level problem minimizes costs associated with unsupplied loads and voltage regulator parameters, while the lower-level problem maximizes the profits of DG owners. By incorporating realistic market-based pricing to incentivize privately owned DGs during contingencies, the model addresses the gap in current literature, where DG ownership and production costs are often overlooked. Validation using a 53-node test system under multiple fault scenarios demonstrates the model’s effectiveness in achieving cost-efficient restoration and providing fair compensation to DG owners. This approach ultimately enhances the resilience and reliability of distribution systems.
{"title":"A novel bilevel model for service restoration in distribution systems integrating technical constraints and the energy market environment","authors":"Etiane O.P. Carvalho , Wandry R. Faria , Leonardo H. Macedo , Gregorio Muñoz-Delgado , Javier Contreras , Benvindo R. Pereira Junior , João Bosco A. London Junior","doi":"10.1016/j.segan.2025.102092","DOIUrl":"10.1016/j.segan.2025.102092","url":null,"abstract":"<div><div>This paper introduces a bilevel programming model for service restoration in distribution systems, integrating private distributed generations (DGs) and market strategies. The upper-level problem minimizes costs associated with unsupplied loads and voltage regulator parameters, while the lower-level problem maximizes the profits of DG owners. By incorporating realistic market-based pricing to incentivize privately owned DGs during contingencies, the model addresses the gap in current literature, where DG ownership and production costs are often overlooked. Validation using a 53-node test system under multiple fault scenarios demonstrates the model’s effectiveness in achieving cost-efficient restoration and providing fair compensation to DG owners. This approach ultimately enhances the resilience and reliability of distribution systems.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"45 ","pages":"Article 102092"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840578","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}
Pub Date : 2026-03-01Epub Date: 2025-12-29DOI: 10.1016/j.segan.2025.102111
Md Ohirul Qays , Iftekhar Ahmad , Daryoush Habibi , Mohammad A.S. Masoum , Thair Mahmoud
Owing to the higher-integration of renewable energy generators (REGs), conventional coal-based synchronous generators are being decommissioned from generation fleets, resulting in system strength and reliability concerns. Along with the increasing load demand, deficiency of system strength can be a huge risk to system stability and can eventually lead to blackouts by disconnecting REGs from grid systems. In the literature, researchers and power engineers have proposed to deploy synchronous condensers (SynCons) as a mitigation strategy to address the system strength and reliability challenges. SynCons are, however, expensive and require investigation for higher reliability results before installation. To address the concerns, SynCons’ optimal sizes, placement and reliability assessment are investigated in this paper. The proposed solution is achieved by modeling an optimization problem and retaining SynCons-related costs low while maintaining short circuit ratio and minimizing loss of load probability, measurement indexes of system strength and reliability analysis of a grid above a satisfactory level respectively. A hybrid data-driven gated recurrent unit (GRU)-classical optimization framework is developed for data processing and achieving the optimization results. The implemented learning model is capable of achieving higher accuracy 99.691 % and lower computation time 0.023 sec when compared with the existing learning models. Additionally, the obtained results, such as transient stability and economic analysis of SynCons-conducted weak-grid present that the proposed solution can significantly perform 21.581 % cost minimization and 6.391 % reliability enhancement.
{"title":"Addressing system strength and reliability concerns in renewable energy-based weak grids using synchronous condensers determined by hybrid GRU-classical optimization method","authors":"Md Ohirul Qays , Iftekhar Ahmad , Daryoush Habibi , Mohammad A.S. Masoum , Thair Mahmoud","doi":"10.1016/j.segan.2025.102111","DOIUrl":"10.1016/j.segan.2025.102111","url":null,"abstract":"<div><div>Owing to the higher-integration of renewable energy generators (REGs), conventional coal-based synchronous generators are being decommissioned from generation fleets, resulting in system strength and reliability concerns. Along with the increasing load demand, deficiency of system strength can be a huge risk to system stability and can eventually lead to blackouts by disconnecting REGs from grid systems. In the literature, researchers and power engineers have proposed to deploy synchronous condensers (SynCons) as a mitigation strategy to address the system strength and reliability challenges. SynCons are, however, expensive and require investigation for higher reliability results before installation. To address the concerns, SynCons’ optimal sizes, placement and reliability assessment are investigated in this paper. The proposed solution is achieved by modeling an optimization problem and retaining SynCons-related costs low while maintaining short circuit ratio and minimizing loss of load probability, measurement indexes of system strength and reliability analysis of a grid above a satisfactory level respectively. A hybrid data-driven gated recurrent unit (GRU)-classical optimization framework is developed for data processing and achieving the optimization results. The implemented learning model is capable of achieving higher accuracy 99.691 % and lower computation time 0.023 sec when compared with the existing learning models. Additionally, the obtained results, such as transient stability and economic analysis of SynCons-conducted weak-grid present that the proposed solution can significantly perform 21.581 % cost minimization and 6.391 % reliability enhancement.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"45 ","pages":"Article 102111"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884017","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}
Pub Date : 2026-03-01Epub Date: 2026-02-07DOI: 10.1016/j.segan.2026.102152
Pablo José Hueros-Barrios , Jorge Espolio-Maestro , Sergio Rodríguez-Carrasco , Carlos Santos-Pérez , Francisco Javier Rodríguez-Sánchez , Pedro Martín Sánchez , Miguel Tradacete-Ágreda , Frede Blaabjerg
The deployment of hybrid green hydrogen systems faces challenges due to complex integration and exposure to cyber threats. The lack of secure testbeds to replicate severe anomalies limits the analysis of fault propagation without endangering physical assets. Consequently, it is necessary to develop a testbed to replicate anomalies in the operation of a green hydrogen generation system to facilitate its physical implementation. This article presents the development of a real-time Digital Twin Prototype (DTP) testbed for green hydrogen production systems integrating photovoltaic (PV) generation, a proton exchange membrane water electrolyzer (PEMWE), and a battery energy storage system (BESS), structured with a DC bus and a supercapacitor. The platform is implemented using Hardware In the Loop (HIL) to emulate system dynamics, enabling the safe testing of cyber-physical anomalies such as False Data Injection Attacks (FDIA) and DC bus short circuits. Historical weather data, including irradiance and temperature from a real-site weather station, are streamed to the HIL-based model via User Datagram Protocol (UDP) communication, replicating realistic operating conditions. A cost-effective real-time monitoring architecture is established using a low-cost Single Board Computer (SBC), with data logged in InfluxDB and visualized through Grafana. Results are analysed through flowcharts depicting failure propagation, offering insights into system resilience and control performance. The testbed facilitates the validation of anomaly detection techniques and Energy Management Systems (EMS), while minimizing the need for physical prototyping. This approach enhances operational safety and accelerates development efficiency in renewable hydrogen infrastructures.
{"title":"Real-time digital twin prototype for cyber-physical analysis of anomalies in PV–PEM–BESS systems for green hydrogen production","authors":"Pablo José Hueros-Barrios , Jorge Espolio-Maestro , Sergio Rodríguez-Carrasco , Carlos Santos-Pérez , Francisco Javier Rodríguez-Sánchez , Pedro Martín Sánchez , Miguel Tradacete-Ágreda , Frede Blaabjerg","doi":"10.1016/j.segan.2026.102152","DOIUrl":"10.1016/j.segan.2026.102152","url":null,"abstract":"<div><div>The deployment of hybrid green hydrogen systems faces challenges due to complex integration and exposure to cyber threats. The lack of secure testbeds to replicate severe anomalies limits the analysis of fault propagation without endangering physical assets. Consequently, it is necessary to develop a testbed to replicate anomalies in the operation of a green hydrogen generation system to facilitate its physical implementation. This article presents the development of a real-time Digital Twin Prototype (DTP) testbed for green hydrogen production systems integrating photovoltaic (PV) generation, a proton exchange membrane water electrolyzer (PEMWE), and a battery energy storage system (BESS), structured with a DC bus and a supercapacitor. The platform is implemented using Hardware In the Loop (HIL) to emulate system dynamics, enabling the safe testing of cyber-physical anomalies such as False Data Injection Attacks (FDIA) and DC bus short circuits. Historical weather data, including irradiance and temperature from a real-site weather station, are streamed to the HIL-based model via User Datagram Protocol (UDP) communication, replicating realistic operating conditions. A cost-effective real-time monitoring architecture is established using a low-cost Single Board Computer (SBC), with data logged in InfluxDB and visualized through Grafana. Results are analysed through flowcharts depicting failure propagation, offering insights into system resilience and control performance. The testbed facilitates the validation of anomaly detection techniques and Energy Management Systems (EMS), while minimizing the need for physical prototyping. This approach enhances operational safety and accelerates development efficiency in renewable hydrogen infrastructures.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"45 ","pages":"Article 102152"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147395630","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}
Pub Date : 2026-03-01Epub Date: 2026-01-29DOI: 10.1016/j.segan.2026.102126
Li Li , Xinyi Zhang , Yiming Yuan , Hua Cai , Jianxin Zhang , Jianjun Wang
Climate change-induced extreme weather is heightening the likelihood of power outages in residential communities that are highly dependent on electricity. While encouraging more households to install distributed energy systems (DERs) could significantly enhance the resilience of community, the question of how much DER capacity is sufficient remains largely unaddressed in current research. Inspired by ecosystem resilience theory, this study developed a resilience evaluation metrics for residential community microgrid from the perspective of balancing sufficient redundancy and efficiency. The resilience performance for two communities with distinct load characteristics was simulated and compared under various DERs deployment and community-level collaborative energy programs, revealing that configuring photovoltaic capacity to utilize 80 % of solar generation with peer-to-peer energy sharing achieves the sufficient redundancy, irrespective of community load patterns or demand levels. Based on this principle, communities can tailor household-level DER capacity combinations to their local conditions. Meanwhile, integrating community coordinated load curtailment can further lower the requirement of photovoltaic capacity for achieving sufficient redundancy, for example, a 50 % load curtailment during outages can reduce the required photovoltaic coverage by 40 %. Practical recommendations are provided to optimize DERs configuration strategy and integrate collaborate energy programs in residential communities to achieve sufficient redundancy and highest resilience based on the above findings.
{"title":"Toward sufficient redundancy: Optimizing the PV-BES configuration with collaborative energy programs for resilient residential communities","authors":"Li Li , Xinyi Zhang , Yiming Yuan , Hua Cai , Jianxin Zhang , Jianjun Wang","doi":"10.1016/j.segan.2026.102126","DOIUrl":"10.1016/j.segan.2026.102126","url":null,"abstract":"<div><div>Climate change-induced extreme weather is heightening the likelihood of power outages in residential communities that are highly dependent on electricity. While encouraging more households to install distributed energy systems (DERs) could significantly enhance the resilience of community, the question of how much DER capacity is sufficient remains largely unaddressed in current research. Inspired by ecosystem resilience theory, this study developed a resilience evaluation metrics for residential community microgrid from the perspective of balancing sufficient redundancy and efficiency. The resilience performance for two communities with distinct load characteristics was simulated and compared under various DERs deployment and community-level collaborative energy programs, revealing that configuring photovoltaic capacity to utilize 80 % of solar generation with peer-to-peer energy sharing achieves the sufficient redundancy, irrespective of community load patterns or demand levels. Based on this principle, communities can tailor household-level DER capacity combinations to their local conditions. Meanwhile, integrating community coordinated load curtailment can further lower the requirement of photovoltaic capacity for achieving sufficient redundancy, for example, a 50 % load curtailment during outages can reduce the required photovoltaic coverage by 40 %. Practical recommendations are provided to optimize DERs configuration strategy and integrate collaborate energy programs in residential communities to achieve sufficient redundancy and highest resilience based on the above findings.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"45 ","pages":"Article 102126"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147395631","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}
Pub Date : 2026-03-01Epub Date: 2026-01-26DOI: 10.1016/j.segan.2026.102136
Danyang Xu, Zeyu Liu, Kai Hou, Hongjie Jia
As carbon neutrality goals accelerate, power systems are undergoing profound transformations marked by large-scale renewable integration and the segmentation of bulk systems into asynchronous grids through HVDC interconnections. These changes introduce significant challenges to frequency security and exacerbate the operational difficulties caused by renewable uncertainty. To address these issues, this paper proposes an uncertainty-aware frequency-constrained scheduling approach for multi-area asynchronous grids (UFCS-MAG). The method leverages HVDC links to coordinate the sharing of primary frequency response (PFR) and regulation reserves, explicitly modelling their coupling under uncertain HVDC power transfers. The emergency frequency response (EFR) capability of HVDC is incorporated into the system’s frequency response model. A piecewise analytical method is then developed to obtain a second-order cone (SOC) representation of the maximum frequency deviation (MFD) constraint. A mechanism for inter-area regulation reserve sharing is introduced, with uncertainty addressed through a distributionally robust chance-constrained (DRCC) framework. Furthermore, to capture the coupling between HVDC responses and variable infeed losses, the HVDC infeed loss is embedded within frequency constraints shaped by prior HVDC responses. A conservative approximation is then devised to reformulate the MFD constraint into a DRCC-compatible form. Case studies on modified IEEE 14-bus and 118-bus systems demonstrate that the proposed UFCS-MAG ensures frequency security, facilitates efficient reserve sharing, and improves economic performance under high renewable uncertainty.
{"title":"Uncertainty-aware frequency-constrained scheduling for multi-area asynchronous grids with high renewable energy penetration","authors":"Danyang Xu, Zeyu Liu, Kai Hou, Hongjie Jia","doi":"10.1016/j.segan.2026.102136","DOIUrl":"10.1016/j.segan.2026.102136","url":null,"abstract":"<div><div>As carbon neutrality goals accelerate, power systems are undergoing profound transformations marked by large-scale renewable integration and the segmentation of bulk systems into asynchronous grids through HVDC interconnections. These changes introduce significant challenges to frequency security and exacerbate the operational difficulties caused by renewable uncertainty. To address these issues, this paper proposes an uncertainty-aware frequency-constrained scheduling approach for multi-area asynchronous grids (UFCS-MAG). The method leverages HVDC links to coordinate the sharing of primary frequency response (PFR) and regulation reserves, explicitly modelling their coupling under uncertain HVDC power transfers. The emergency frequency response (EFR) capability of HVDC is incorporated into the system’s frequency response model. A piecewise analytical method is then developed to obtain a second-order cone (SOC) representation of the maximum frequency deviation (MFD) constraint. A mechanism for inter-area regulation reserve sharing is introduced, with uncertainty addressed through a distributionally robust chance-constrained (DRCC) framework. Furthermore, to capture the coupling between HVDC responses and variable infeed losses, the HVDC infeed loss is embedded within frequency constraints shaped by prior HVDC responses. A conservative approximation is then devised to reformulate the MFD constraint into a DRCC-compatible form. Case studies on modified IEEE 14-bus and 118-bus systems demonstrate that the proposed UFCS-MAG ensures frequency security, facilitates efficient reserve sharing, and improves economic performance under high renewable uncertainty.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"45 ","pages":"Article 102136"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147395735","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}
Pub Date : 2026-03-01Epub Date: 2025-12-24DOI: 10.1016/j.segan.2025.102106
Panagiotis Herodotou , Georgios Tziolis , George Makrides , George E. Georghiou
Accurate short-term net load forecasting (STNLF) of residential buildings with increased solar photovoltaic (PV) power penetration is critical for enabling reliable operation and enhancing grid stability. This paper presents a systematic comparative analysis of twelve deep learning and machine learning (ML) models for day-ahead net load forecasting, evaluated using data from a pilot study involving 68 households in Cyprus equipped with grid-connected PV systems. The proposed approach utilized historical, weather, and temporal features derived from the dataset. A rigorous evaluation procedure was followed, including cross-validation, recursive forecasting, and multiple error metrics. Results indicate that the random forest (RF) algorithm exhibited the best performance, with normalized root mean square error of 5.71 %, normalized relative to the range of observed net load values. RF achieved this due to its robustness in capturing non-linear interactions and its ability to handle mixed feature types. In contrast, the gated recurrent unit (GRU) network presented higher adaptability to sudden weather changes, attributed to its sequential learning structure and memory capabilities. The differences in model performance were verified with Diebold-Mariano test, indicating the superiority of recurrent and ensemble models over the simpler baselines. Feature importance analysis showed that lagged net load features were important in all models, but deep learning (DL) models better captured the impact of temporal and weather variables more effectively. The systematic approach for STNLF in PV-integrated residential buildings used in this study extends to the broader field of solar-integrated residential microgrids, promoting adaptable, interpretable models for effective energy management and renewable energy integration.
{"title":"Comparative analysis of machine learning methods for residential net load forecasting of solar-integrated households","authors":"Panagiotis Herodotou , Georgios Tziolis , George Makrides , George E. Georghiou","doi":"10.1016/j.segan.2025.102106","DOIUrl":"10.1016/j.segan.2025.102106","url":null,"abstract":"<div><div>Accurate short-term net load forecasting (STNLF) of residential buildings with increased solar photovoltaic (PV) power penetration is critical for enabling reliable operation and enhancing grid stability. This paper presents a systematic comparative analysis of twelve deep learning and machine learning (ML) models for day-ahead net load forecasting, evaluated using data from a pilot study involving 68 households in Cyprus equipped with grid-connected PV systems. The proposed approach utilized historical, weather, and temporal features derived from the dataset. A rigorous evaluation procedure was followed, including cross-validation, recursive forecasting, and multiple error metrics. Results indicate that the random forest (RF) algorithm exhibited the best performance, with normalized root mean square error of 5.71 %, normalized relative to the range of observed net load values. RF achieved this due to its robustness in capturing non-linear interactions and its ability to handle mixed feature types. In contrast, the gated recurrent unit (GRU) network presented higher adaptability to sudden weather changes, attributed to its sequential learning structure and memory capabilities. The differences in model performance were verified with Diebold-Mariano test, indicating the superiority of recurrent and ensemble models over the simpler baselines. Feature importance analysis showed that lagged net load features were important in all models, but deep learning (DL) models better captured the impact of temporal and weather variables more effectively. The systematic approach for STNLF in PV-integrated residential buildings used in this study extends to the broader field of solar-integrated residential microgrids, promoting adaptable, interpretable models for effective energy management and renewable energy integration.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"45 ","pages":"Article 102106"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840579","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}
Pub Date : 2026-03-01Epub Date: 2026-01-27DOI: 10.1016/j.segan.2026.102131
Majd Olleik, Amir Boushahine, Kareem Abou Jalad
Achieving universal access to affordable and clean energy is a central goal of the United Nations Sustainable Development Agenda. However, in many developing and fragile contexts, large populations remain disconnected from the national grid or experience highly unreliable supply. Hybrid Renewable Energy Systems (HRES) offer a viable off-grid solution, but their long-term planning is complicated by uncertainty around future grid interconnection both in timing and technical or economic conditions. This paper proposes a decision analysis framework that incorporates grid interconnection uncertainty into off-grid HRES planning. The framework employs a receding horizon approach that combines deterministic and stochastic optimization models while integrating early asset retirement decisions and residual value assessments. A case study from Lebanon, a country with persistent electricity sector challenges, demonstrates the framework’s utility. Results highlight three key insights: (i) incremental planning enables better adaptation to evolving grid conditions, (ii) planning the HRES for the worst-case scenario of no grid availability and then adjusting once the grid is available is a valid heuristic, and (iii) policy interventions that improve market liquidity for used HRES assets can mitigate the risks associated with uncertain grid interconnection, enhancing the economic resilience of off-grid investments. These findings offer both methodological and policy contributions to energy planning in uncertain and underserved regions.
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Pub Date : 2026-03-01Epub Date: 2025-12-09DOI: 10.1016/j.segan.2025.102095
Zohreh Salmani Khankahdani , Mohammad Sadegh Ghazizadeh , Vahid Vahidinasab
Smart prosumers, equipped with generation, storage, and advanced communication infrastructure, have significant potential to provide grid services. However, effectively harnessing this potential in decentralized environments requires novel optimization frameworks that coordinate system operators with prosumers while preserving data privacy. To address this challenge, a two-layer hierarchical optimization structure is proposed to maximize grid service provision by smart prosumers under high-impact low-probability (HILP) events with minimal information exchange. In the first layer, smart prosumers, including Internet data centers and battery swapping stations, optimize and announce their available flexible capacities during emergencies. In the second layer, the distribution system operator (DSO) integrates these capacities into emergency operation planning, complemented by the dynamic routing of battery logistic trucks and the execution of distribution feeder reconfiguration (DFR) to restore power to customers in fault-affected areas. Implementation on the IEEE 69-bus distribution network demonstrates that the proposed hierarchical framework reduces load shedding by 44.82 % and emergency operation costs by 28.2 % while maintaining agent data confidentiality. These results are derived under deterministic conditions, assuming reliable communication, full prosumer participation, and accessible logistics. While uncertainties such as communication delays, partial participation, or disrupted transportation are not yet modeled, the framework provides a computationally efficient basis for decentralized resilience enhancement.
{"title":"Leveraging smart prosumers for grid resilience under high-impact low-probability events: A privacy-preserving optimization framework","authors":"Zohreh Salmani Khankahdani , Mohammad Sadegh Ghazizadeh , Vahid Vahidinasab","doi":"10.1016/j.segan.2025.102095","DOIUrl":"10.1016/j.segan.2025.102095","url":null,"abstract":"<div><div>Smart prosumers, equipped with generation, storage, and advanced communication infrastructure, have significant potential to provide grid services. However, effectively harnessing this potential in decentralized environments requires novel optimization frameworks that coordinate system operators with prosumers while preserving data privacy. To address this challenge, a two-layer hierarchical optimization structure is proposed to maximize grid service provision by smart prosumers under high-impact low-probability (HILP) events with minimal information exchange. In the first layer, smart prosumers, including Internet data centers and battery swapping stations, optimize and announce their available flexible capacities during emergencies. In the second layer, the distribution system operator (DSO) integrates these capacities into emergency operation planning, complemented by the dynamic routing of battery logistic trucks and the execution of distribution feeder reconfiguration (DFR) to restore power to customers in fault-affected areas. Implementation on the IEEE 69-bus distribution network demonstrates that the proposed hierarchical framework reduces load shedding by 44.82 % and emergency operation costs by 28.2 % while maintaining agent data confidentiality. These results are derived under deterministic conditions, assuming reliable communication, full prosumer participation, and accessible logistics. While uncertainties such as communication delays, partial participation, or disrupted transportation are not yet modeled, the framework provides a computationally efficient basis for decentralized resilience enhancement.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"45 ","pages":"Article 102095"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791157","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}
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