M.B. Rasheed, Á. Llamazares, R. Gutiérrez-Moreno, M. Ocaña, P. Revenga
{"title":"Context-aware state estimation in battery management systems: Leveraging nonlinear dynamics with physics-guided parameter identification","authors":"M.B. Rasheed, Á. Llamazares, R. Gutiérrez-Moreno, M. Ocaña, P. Revenga","doi":"10.1016/j.segan.2025.101979","DOIUrl":"10.1016/j.segan.2025.101979","url":null,"abstract":"<div><div>Black accurate remaining range estimation remains a critical issue to promote plug-in and electric vehicle adoption, primarily due to underlying uncertainties in voltage and current-dependent state estimation. To overcome these challenges, the proposed work introduces a novel framework for range estimation while integrating an enhanced equivalent circuit model with a physics-guided temperature-compensated Extended Kalman Filter algorithm. Firstly, comprehensive mathematical models are developed and validated that integrate: (i) proposed enhanced 3rd-order equivalent circuit modeling (p-eTECM) with control parameter optimization, (ii) data-and-model-driven parameter identification using Coulomb counting and voltage scaling analysis, (iii) comprehensive sensitivity analysis to rank important parameters to improve accuracy, and (iv) application-specific model selection criteria based on performance trade-offs. However, unlike existing frameworks that incorporate higher-order RC models that are universally superior, the proposed work identifies that model selection should be application-dependent for different battery management functions. The novel contributions include: parameter & voltage optimization from the pack-level, while systematically eliminating voltage bias through online parameter optimization, and developing a comprehensive sensitivity analysis algorithm to validate the improvements. The proposed framework demonstrates that parameter calibration is more crucial with capacity correction and voltage scaling, to eliminate systematic biases that render models impractical. This study further reveals that 3rd-order model outperforms in voltage prediction (8.3 % improvement) while the 2nd-order model provides better SOC tracking (13 % improved accuracy), establishing clear application-specific selection criteria. Key results demonstrate that both models have achieved excellent performance in terms of SOC errors (<span><math><mo><</mo></math></span>0.2 %), and range accuracy (155–170 km) with real-time computational efficiency, validating the practical applicability for diverse battery management applications while providing a systematic methodology for future battery modeling research.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101979"},"PeriodicalIF":5.6,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Blockchain-based trading of energy savings assets: Enhancing transparency and efficiency in the energy market","authors":"Amit S. Chopade , Nita R. Patne , Nitesh A. Funde","doi":"10.1016/j.segan.2025.101980","DOIUrl":"10.1016/j.segan.2025.101980","url":null,"abstract":"<div><div>Rising energy demands and increasing carbon emissions have intensified the need for scalable, transparent energy efficiency (EE) mechanisms. This study presents a blockchain-based system for trading energy savings assets (ESAs) digitized representations of verified energy savings generated primarily through municipal public lighting projects under the energy service company (ESCO) model. The proposed system leverages Hyperledger Fabric (HLF), a permissioned blockchain platform, to enable secure, transparent, and automated trading of surplus energy savings among urban local bodies (ULBs). It integrates IoT-enabled smart meters for real-time energy monitoring, smart contracts for automated transaction execution, and a decentralized ledger for tamper-proof recordkeeping, eliminating the need for third-party physical verification. Experimental results demonstrate that most ULBs retrofitting conventional lights with LEDs achieve surplus energy savings beyond mandated benchmarks, qualifying for ESA generation. The system supports asset creation with a throughput of 0.44 requests per second and trade execution with an average response time of 6.64 s over HLF blockchain platform. By ensuring transparency, auditability, and operational efficiency, the system establishes a foundation for a trust-driven EE marketplace, particularly valuable for emerging economies aiming to advance smart city initiatives and meet carbon reduction goals.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101980"},"PeriodicalIF":5.6,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266129","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}
Xin Liu , Weijun Zhang , Zhongyuan Chi , Tianchi Jiang , Yuzhang Ji
{"title":"Optimal energy scheduling for multi-energy network based on incentive integrated demand response","authors":"Xin Liu , Weijun Zhang , Zhongyuan Chi , Tianchi Jiang , Yuzhang Ji","doi":"10.1016/j.segan.2025.101986","DOIUrl":"10.1016/j.segan.2025.101986","url":null,"abstract":"<div><div>In recent years, the rapid expansion of data center construction has led to rising energy demand, forming new energy-intensive industrial clusters supported by integrated energy systems (IES). This study investigates the use of integrated demand response (IDR) to manage the complex coordination of electricity, heating, and cooling in such systems. A framework is proposed that connects data centers with surrounding residential and commercial buildings through an IES, enabling joint scheduling of multiple energy flows. A two-stage bilevel optimization model is developed to capture the interaction between the integrated energy service provider (IESP) and users. The upper level minimizes total operating costs, while the lower level maximizes user benefits under comfort constraints. Simulation results show that the proposed IDR strategy effectively balances multi-energy demands, enables rapid load adjustments, smooths the energy supply curve, and reduces dependence on costly peak-hour energy. The strategy achieves an average daily cost reduction of approximately 3.25 %. Compared with conventional approaches focused only on electricity price response, this work presents a generalized and scalable IDR framework. It incorporates energy conversion relationships, user-side flexibility, and incentive design, making it more applicable to complex, multi-energy environments. The results highlight the potential of this approach to enhance economic efficiency and operational reliability in future energy-intensive clusters.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101986"},"PeriodicalIF":5.6,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219716","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}
Ashutosh Shukla , Erhan Kutanoglu , John J. Hasenbein
{"title":"Co-optimization of short- and long-term decisions for the transmission grid’s resilience to flooding","authors":"Ashutosh Shukla , Erhan Kutanoglu , John J. Hasenbein","doi":"10.1016/j.segan.2025.101973","DOIUrl":"10.1016/j.segan.2025.101973","url":null,"abstract":"<div><div>We present and analyze a three-stage stochastic optimization model that integrates output from a geoscience-based flood model with a power flow model for transmission grid resilience planning against flooding. The proposed model coordinates the decisions made across multiple stages of resilience planning and recommends an optimal allocation of the overall resilience investment budget across short- and long-term measures. While doing so, the model balances the cost of investment in both short- and long-term measures against the cost of load shed that results from unmitigated flooding forcing grid components go out-of-service. We also present a case study for the Texas Gulf Coast region to demonstrate how the proposed model can provide insights into various grid resilience questions. Specifically, we demonstrate that for a comprehensive yet reasonable range of economic values assigned to load loss, we should make significant investments in the permanent hardening of substations such that we achieve near-zero load shed. We also show that not accounting for short-term measures while making decisions about long-term measures can lead to significant overspending (up to 14 %). Furthermore, we demonstrate that a technological development enabling to protect substations on short notice before imminent hurricanes could vastly influence and reduce the total investment budget (up to 56 %) that would otherwise be allocated for more expensive substation hardening. Lastly, we also show that for a wide range of values associated with the cost of mitigative long-term measures, the proportion allocated to such measures dominates the overall resilience spending.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101973"},"PeriodicalIF":5.6,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266131","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}
Naren Mantilla , Juan C. Oviedo-Cepeda , David Toquica , Kodjo Agbossou , Nilson Henao
{"title":"A market-clearing mechanism based on hierarchical-spectral clustering with multiple similarity measures for flexibility spot markets","authors":"Naren Mantilla , Juan C. Oviedo-Cepeda , David Toquica , Kodjo Agbossou , Nilson Henao","doi":"10.1016/j.segan.2025.101983","DOIUrl":"10.1016/j.segan.2025.101983","url":null,"abstract":"<div><div>Modern power systems face increasing congestion issues due to rising electricity demand and the integration of distributed energy resources. Flexibility markets offer a promising solution to deal with congestion by enabling system operators to incentivize consumption or production shifts through real-time spot trades. However, market mechanisms must maintain clearing tractability despite the expanding number of flexibility providers and the complexity of bid structures. This paper presents a hierarchical-spectral clustering approach with affinity matrix aggregation to improve market-clearing performance. The proposed method forms network-aware clusters by aggregating affinity matrices based on flexumers’ geographical location, electrical proximity, and behavioral preferences. The simulation results on the IEEE 33-bus and a large-scale distribution grid, based on the 9241-bus PEGASE test system, demonstrate a clear trade-off between computational burden and economic outcomes. Notably, the framework can reduce market-clearing times while maintaining acceptable levels of economic efficiency, providing valuable insights for the design of scalable flexibility markets.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101983"},"PeriodicalIF":5.6,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145157711","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 multi-stage joint expansion planning of transmission system and integrated electrical, heating, and cooling distribution systems-A security perspective","authors":"Yousef Allahvirdizadeh","doi":"10.1016/j.segan.2025.101982","DOIUrl":"10.1016/j.segan.2025.101982","url":null,"abstract":"<div><div>This paper proposes a joint multi-stage expansion planning approach for a comprehensive structure that integrates the Transmission System (TS) with electrical, heating, and cooling Distribution Systems (DSs), incorporating a security perspective. The security of both the TS and Integrated DSs is modeled by accounting for the potential failures in TS lines, electrical feeders, heating and cooling pipelines, and Demand Side Resources (DSRs). A three-level iterative approach, based on the diagonalization algorithm, is used to structure the problem. In the first level, the Transmission Expansion Planning (TEP) problem is formulated. The second level addresses the integrated Distribution Expansion Planning (DEP), while the third level involves market clearing and the updating of Locational Marginal Prices (LMPs) by the Independent System Operator (ISO). The related uncertainties such as the output of RESs, market prices, and load demands are addressed using a stochastic framework. This framework employs Monte Carlo simulation for scenario generation and the k-medoids methodology as an effective scenario reduction technique. The proposed model optimizes the total installation, operation, emission, and Expected Energy Not Served (EENS) costs for both the Transmission System Operator (TSO) and Distribution System Operators (DSOs). Numerical studies are conducted on the modified 30-bus, and 118 bus IEEE TS systems comprising 18 bus and 30 bus integrated electrical, heating, and cooling DSs, respectively. on the 30-bus IEEE TS and 18-bus IDSs to demonstrate the effectiveness of the proposed approach in reducing the total system cost by 17.78 % compared to the separate expansion planning of TS and IDSs.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101982"},"PeriodicalIF":5.6,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145157710","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":"Framework for enhancing fairness and security in active distribution networks via transactive control signals","authors":"Hajar Abdolahinia , Morteza Aryani , Moein Moeini-Aghtaie , Mohammad Heidarizadeh , Inoccent Kamwa","doi":"10.1016/j.segan.2025.101968","DOIUrl":"10.1016/j.segan.2025.101968","url":null,"abstract":"<div><div>With the development of peer-to-peer (P2P) energy trading, transactive energy markets (TEMs) face challenges such as ensuring fair access for market participants to the upstream grid and addressing limitations of distribution networks. To tackle these challenges, the distribution system operator (DSO) should incentivize market participants to contribute to their mitigation. To this end, this paper proposes a bi-level framework in which the DSO designs transactive control signals (TCSs) to influence the behavior of market participants. In this framework, fairness, security, and peak shaving signals are introduced at the upper level, while P2P energy trading is implemented at the lower level. The TCSs are calculated based on sensitivity analyses related to fair allocation and secure network operation constraints. As the results demonstrate, the goals of fairness and network security are achieved simultaneously. Moreover, while market participants effectively contribute to meeting technical objectives, their privacy is preserved.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101968"},"PeriodicalIF":5.6,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219858","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}
Baxter Kamana-Williams , R.J. Hooper , Daniel Gnoth , J. Geoffrey Chase
{"title":"The peak load effects of demand response with simple time-of-use pricing","authors":"Baxter Kamana-Williams , R.J. Hooper , Daniel Gnoth , J. Geoffrey Chase","doi":"10.1016/j.segan.2025.101981","DOIUrl":"10.1016/j.segan.2025.101981","url":null,"abstract":"<div><div>Increasing electrification of energy systems, required for greenhouse gas emissions reductions, poses challenges for electricity systems from increased peak demand. Demand response can reduce peak demand, but acceptability is limited by consumer concerns about effort, complexity, and lack of control. This study assesses the potential of simple demand response programs using existing electricity pricing structures for a median-income residential low-voltage distribution network in Auckland, Aotearoa New Zealand, and draws generalised conclusions applicable across a broad range of circumstances. Using an agent-based model validated against real transformer data, the electricity demand in 50 households is simulated under time-of-use electricity pricing structures, with participation varied between 0 % and 100 %. Time-of-use schedules without modified demand reduces household electricity costs by 14 %, with further reductions of up to 35 % through DR participation. Demand response can reduce peak electricity demand by up to 5.7 %, but high levels of hot water cylinder (or “water heater”) delayed heating can increase peak demand by up to 32.9 %. These findings highlight the need for careful DR program design to avoid unintended peak demand increases and ensure equitable access to DR benefits. Regulators could consider facilitating the adoption of DR-capable technologies to enhance program effectiveness and support the energy transition.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101981"},"PeriodicalIF":5.6,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145121136","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}
Muhammad Kazim , Harun Pirim , Chau Le , Trung Le , Om Prakash Yadav
{"title":"Edge-level explainable graph neural networks with network centric features for transmission line failure prediction in power grids","authors":"Muhammad Kazim , Harun Pirim , Chau Le , Trung Le , Om Prakash Yadav","doi":"10.1016/j.segan.2025.101969","DOIUrl":"10.1016/j.segan.2025.101969","url":null,"abstract":"<div><div>Cascading outages in high-voltage power grids pose a severe risk, causing blackouts with global economic losses estimated at <span><math><mo>≈</mo><mi>$</mi><mn>100</mn></math></span> billion annually. These outages disrupt economic activity and impact energy efficiency and sustainability goals by necessitating less efficient backup generation and hindering the integration of renewable energy sources. This paper introduces a pioneering, explainable Graph Neural Network (GNN) framework for edge-level transmission line failure prediction, addressing a critical gap in grid resilience analytics. Our work presents two key innovations: first, it is the first framework of its kind to be systematically validated across four standard power grid benchmarks (IEEE-24, 39, 118, and the UK grid), demonstrating robust generalization. Second, it advances an interdisciplinary approach by uniquely integrating network science principles with deep learning, augmenting traditional electrical data with topological descriptors like betweenness centrality and load ratio. This fusion enhances the predictive power of three GNN architectures: GINE, GAT, and EdgeAwareGC. On the IEEE-24 test system, this integration boosts macro-F1 scores from 0.498 to 0.871 for EdgeAwareGC and from 0.335 to 0.859 for GINE. This scalability and effectiveness are further demonstrated on the highly imbalanced IEEE-118 network, where EdgeAwareGC achieves a strong F1 score of 0.553. Explainability techniques such as gradient-based attribution and GNNExplainer uncover key physical and topological predictors, providing actionable guidance for grid operators. Our findings inform grid modernization policies, supporting initiatives like the U.S. Department of Energy’s Grid Resilience and Innovation Partnerships (GRIP) program. This work highlights the potential of GNNs to substantially improve power system reliability, contributing to more sustainable and efficient energy infrastructure, and aligning with global efforts toward decarbonization and enhanced energy security.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101969"},"PeriodicalIF":5.6,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145157712","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":"Spatio-temporal coordination of distributed energy and regulation capacity with ISO and DSO operation","authors":"Majid Majidi, Masood Parvania","doi":"10.1016/j.segan.2025.101864","DOIUrl":"10.1016/j.segan.2025.101864","url":null,"abstract":"<div><div>This paper proposes a bi-level co-optimization model for coordinating Distributed Energy Resources (DERs) in power distribution systems to offer energy flexibility and regulation capacity in the day-ahead electricity market. The proposed co-optimization model considers the physical constraints of the distribution system and determines the deliverable energy flexibility and regulation capacity of the DERs and flexible loads in the upper-level problem while clearing the day-ahead electricity market in the lower-level problem. To solve the proposed bi-level model, the lower-level problem is replaced with its Karush–Kuhn–Tucker (KKT) conditions, converting the proposed model into a single-level non-linear optimization model. Then, by implementing a combination of auxiliary variables and the strong duality theorem, the proposed model is formulated as a single-level Mixed-Integer Second-Order Cone Programming (MISOCP) problem, solvable using commercial solvers. Simulations are carried out on an IEEE test transmission system connected to multiple distribution systems in multiple cases. The findings verify the effectiveness of the proposed model in identifying the optimal energy flexibility and regulation capacity of DERs and flexible loads that are available in the electricity market while maintaining the quality of service constraints of flexible loads, as well as the physical limits of power transmission and distribution systems. The observations also offer a promising solution to different entities—such as load-serving entities, aggregators, and utilities—to manage local resources and optimize their portfolios in the electricity markets.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101864"},"PeriodicalIF":5.6,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219720","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}