Energy Informatics最新文献

{"title":"ESM-Regio: simulation and optimization of regional energy systems in carbon-neutral scenarios","authors":"Kevin-Martin Aigner,&nbsp;Peter Bazan,&nbsp;Sebastian Bottler,&nbsp;Robert Burlacu,&nbsp;Berenike Bölting,&nbsp;Nora Elhaus,&nbsp;Jürgen Karl,&nbsp;Frauke Liers,&nbsp;Natalia Luna-Jaspe,&nbsp;Klaus Markolf,&nbsp;Erich Maurer,&nbsp;Marco Pruckner,&nbsp;Daniel Scharrer,&nbsp;Leo Strobel,&nbsp;Christian Weindl,&nbsp;Reinhard German","doi":"10.1186/s42162-026-00648-3","DOIUrl":"10.1186/s42162-026-00648-3","url":null,"abstract":"<div><p>This work presents a high-resolution, sector-coupled energy system modeling framework developed within the research project ESM-Regio, designed to analyze regional energy transitions and the sector coupling potential in the context of Germany’s 2045 climate targets. In contrast to existing regional energy system models, our framework explicitly models medium-voltage distribution grids and incorporates asset aging into a sector-coupled techno-economic optimization. The framework operates on the medium-voltage level at a 15-minute temporal resolution and is designed to analyze regions ranging city to county (NUTS-3 level). The electricity, heat, transport, and gas sectors are integrated into the framework using a component-based simulation architecture. All sectors are represented with sectoral submodels which are iteratively coupled through alternating simulation and mixed-integer optimization stages to minimize operational system costs while accounting for technical grid constraints and asset aging. The methodology is applied to the distribution grid area of the regional utility Stadtwerke Bayreuth, for three representative weeks in each of the years 2019, 2030, and 2045. The results highlight the increasing importance of coordinated flexibility—such as building thermal inertia, battery storage, and electric vehicles—in mitigating grid stress under increased electrification. Across the investigated scenarios, coordinated flexibility usage reduces energy procurement costs by up to 22% and significantly lowers line and transformer loading. However, we find that while intelligent flexibility usage can reduce the need for grid expansion, it can not completely eliminate it in the carbon-neutral 2045 scenarios. The presented framework offers a scalable, data-driven approach to assess the impacts of sector coupling and to support infrastructure and regional energy transition planning aligned with national decarbonization goals.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00648-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147642921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent detection of topological relationships of substation electrical main wiring diagram based on CIM/SVG","authors":"Qiang Sun,&nbsp;Zhiqing Song,&nbsp;Zheng Lv,&nbsp;Qi Xiao,&nbsp;Yanqian Lu","doi":"10.1186/s42162-026-00657-2","DOIUrl":"10.1186/s42162-026-00657-2","url":null,"abstract":"<div>\u0000 \u0000 <p>Due to the complexity of substation systems and the diversity of data sources, potential inaccuracies or inconsistencies in data may compromise the accuracy of intelligent detection. To address this issue, an intelligent detection method for topological relationships in substation electrical main wiring diagrams is developed based on the integration of the Common Information Model (CIM) and Scalable Vector Graphics (SVG). First, main wiring diagrams are classified into three structural types: chain, ring, and network. Second, substation conductive equipment is categorized, and the primary node–node correlation matrix is extracted. Third, redundant regions in the wiring diagram images are segmented. By integrating CIM and SVG, the electrical main wiring diagram is automatically generated. Subsequently, component names and coordinates are obtained using the Faster R-CNN deep learning model. A key contribution of this work is the introduction of a dynamic topology prediction module based on a Temporal Graph Convolutional Network (TGCN), which enables real-time adaptation to changes in substation operation modes, thereby enhancing system robustness and operational stability. Finally, topological relationships are intelligently detected using graph theory and adjacency matrices. Experimental results show: correlation matrix extraction time &lt; 20 ms, accuracy = 85.02%, F1-score = 85.42%, average precision (AP) of components &gt; 0.8, and a 91.59% improvement in detection accuracy.</p>\u0000 </div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00657-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147796468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Control strategies for district heating and cooling systems: a comprehensive review across production, distribution, and end-user levels","authors":"Nima Monghasemi,&nbsp;Amir Vadiee,&nbsp;Stavros Vouros,&nbsp;Konstantinos Kyprianidis","doi":"10.1186/s42162-026-00650-9","DOIUrl":"10.1186/s42162-026-00650-9","url":null,"abstract":"<div>\u0000 \u0000 <p>The evolution of district heating and cooling systems into sophisticated energy networks is essential for global decarbonization. However, a fundamental tension exists between rapid innovation in advanced control algorithms and the slow replacement cycle of physical infrastructure, making intelligent system-wide control the primary enabler of network modernization. Despite this critical role, the existing literature remains fragmented and lacks a comprehensive synthesis of control strategies across the production, distribution, and end-user levels. This analysis confirmed a definitive shift from isolated, single-level control to holistic frameworks that unlock system-wide flexibility. This review establishes that successful implementation requires addressing distinct objectives at each operational level, from multisource management in production to occupant-centric control at the end-user level. A critical finding is the credibility gap between the demonstrated potential of advanced control and its limited practical application. This disparity is rooted in systemic challenges, including intensive modeling requirements, computational scalability limits, and unresolved human-in-the-loop problems. To bridge this gap, this review presents a structured framework that synthesizes the current state of district heating and cooling control and proposes a forward-looking research roadmap. This roadmap prioritizes the development of hybrid intelligent controllers that integrate learning-based methods with model predictive control, creating persistent digital twins for human-centric applications, and designing secure and decentralized coordination architectures for next-generation thermal networks.</p>\u0000 </div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00650-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147642910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of energy demands in future scenarios for a residential district via co-simulation of heat, electricity, and mobility: the case of the district “Am Ölper Berge”","authors":"Fernando Penaherrera,&nbsp;Ihsan Ünal,&nbsp;Lars Kühl,&nbsp;Astrid Nieße","doi":"10.1186/s42162-026-00658-1","DOIUrl":"10.1186/s42162-026-00658-1","url":null,"abstract":"<div><h3>Purpose</h3><p>This study examines the thermal, electrical, and mobility energy demand and supply of the residential district “Am Ölper Berge\" in Lower Saxony, and evaluates potential developments of the district energy supply system and the trade-offs associated with its modernization.</p><h3>Methods</h3><p>A reference scenario for the year 2020 serves as the baseline for evaluating future development points (2030, 2040, 2050), including increasing electrification of thermal energy supply, improvements in building energy efficiency, and the integration of renewable energy sources for local generation. The scenarios include step-by-step measures such as facade insulation, implementation of photovoltaic systems, district heating supply, implementation of heat pumps, and the integration of charging infrastructure for electric vehicles. This is achieved using several control models integrated into the co-simulation platform mosaik.</p><h3>Results</h3><p>In the 2050 scenario, heat supply is provided via heat pumps and a low-temperature district heating network, supplemented by neighborhood energy storage and extensive use of photovoltaic systems. Four extended scenarios examine centralized and decentralized control strategies that monitor grid voltage across the district and optimize electric vehicle charging. The analysis includes charging strategies such as maximum charging power, night-time charging, forecast-based charging, and solar-optimized charging. The results show significant differences in external energy demands and CO<span>(_2)</span> emissions, with solar-optimized charging of electrical vehicles combined with areal use of power flexibilities for grid control offering the greatest environmental and operational benefits.</p><h3>Conclusion</h3><p>The study provides key insights into the interactions between building energy demands, decentralized generation, and grid operation, thereby supporting the planning of energy supply for urban districts in alignment with emission reduction targets.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00658-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147643016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explainable AI-driven energy forecasting: A DGMR-based feature extraction and EGST-Net prediction framework for transparent decision-making","authors":"Swaroopa Rani B,&nbsp; Dr. Chandrashekar Jatoth,&nbsp;Dr. Sonti Venu","doi":"10.1186/s42162-026-00656-3","DOIUrl":"10.1186/s42162-026-00656-3","url":null,"abstract":"<div>\u0000 \u0000 <p>Transparency in Decision-Making the modern energy management systems, Transparency in Decision-Making requires proper and understandable forecasting models capable of controlling and documenting the intricate dynamics of consumption when implemented within the smart grid scenario. However, the existing energy prediction techniques, including traditional statistical models and independent deep learning techniques, are often incapable of learning a nonlinear spatiotemporal relationship, and offer poor interpretability, resulting in a reduction of reliability to real-life use. This paper proposes an Explainable AI-based energy forecasting model to address these limitations, which will be a combination of the Dynamic Gated Memory Refinement (DGMR) and a hybrid EGST-Net framework comprising Convolutional Long Short-Term Memory (ConvLSTM) and Transformer-based attention models. The suggested method is novel in a sense that it is adaptably capable of refining feature of its aim that chooses selectively, in informative temporal-spatial patterns and eliminates noise, and an attention-driven elucidation section that enhances intelligibility in the determination to make forecasts.The framework conducts the multi-stage processing of the data (data preprocessing, DGMR-based feature extraction, spatiotemporal learning with the help of EGST-Net, and attention-based interpretability analysis) and was launched on the Python environment with deep learning libraries to provide effective training and testing of the model. The experimental findings showed better performance than the traditional baseline models with a prediction accuracy of 96% and little forecasting error scores. The suggested system will appeal to energy providers, smart grid operators, policymakers, and other industrial stakeholders as it can facilitate resource allocation and efficient demand prediction, as well as make the decisions made by AI interpretable. All in all, the framework is part of the achievement of intelligent, transparent, and scalable energy management solutions to the development of next generation smart energy infrastructures.</p>\u0000 </div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00656-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147737669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measurement of green development energy level and analysis of obstacle factors in the Yangtze River Delta urban agglomeration based on the improved WRSR model and obstacle degree model","authors":"Hanjie Xiao,&nbsp;Hui Jiang,&nbsp;Qianhui Bao,&nbsp;Liang Wu","doi":"10.1186/s42162-026-00653-6","DOIUrl":"10.1186/s42162-026-00653-6","url":null,"abstract":"<div>\u0000 \u0000 <p>Accurately measuring the green development energy level of the Yangtze River Delta urban agglomeration and identifying its key drivers are crucial for promoting regional sustainable development, yet they remain complex challenges. This study first establishes a comprehensive evaluation index system covering four dimensions: green economy, green innovation, green support, and green openness. Combined weights are determined by integrating the G1 method and entropy method through game theory, and an optimized Non-integer Weighted Rank-Sum Ratio (WRSR) evaluation model is proposed. Subsequently, using the improved WRSR model and obstacle degree model, the green development energy levels of 27 representative cities in the region from 2006 to 2021 are measured and analyzed. The results indicate that: (1) The overall green development energy level of the agglomeration increased slowly from 0.275 in 2006 to 0.300 in 2021, with significant disparities among cities. (2) Cities are classified into four tiers, with Shanghai and Nanjing as higher-level cities, Wuxi, Changzhou, and Suzhou as high-level cities, Chuzhou, Xuancheng, and Anqing as low-level cities, and Chizhou as a lower-level city, revealing a core-periphery spatial pattern. (3) Green openness and green innovation are key influencing factors, with obstacles mainly arising from indicators such as foreign trade dependence, actual utilized foreign direct investment, and the number of full-time teachers in higher education institutions. This research provides a methodological reference for evaluating urban green development and offers targeted policy insights to support coordinated green transition across city tiers.</p>\u0000 </div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00653-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147737447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification model of distribution equipment insulation aging enhancement based on SCADA knowledge graph","authors":"Shuai Zhang,&nbsp;Wei Zhang,&nbsp;Song Wang,&nbsp;Lianwei Bao,&nbsp;Zhou Yu","doi":"10.1186/s42162-026-00639-4","DOIUrl":"10.1186/s42162-026-00639-4","url":null,"abstract":"<div><p>With the continuous advancement of scientific and technological integration in power facilities, higher requirements have been raised for identifying the insulation aging state of distribution equipment. At present, Supervisory Control and Data Acquisition (SCADA) systems face bottlenecks due to the limited information dimensions of single-sensor data and the heavy computational burden of complex models, which restrict their deployment and application in practical scenarios. To address these challenges, a multimodal data fusion framework is introduce and collaborative analysis and feature extraction are performed on monitoring signals from different physical characteristics. Furthermore, a lightweight Knowledge Graph-Enhanced Dynamic Graph Neural Network (KGE-DGNN) is innovatively proposed by integrating an adaptive feature weighting module. This model can autonomously enhance the contribution of key modalities while maintaining efficient computational logic, significantly reducing resource consumption and improving the overall performance of insulation aging identification. Experimental results demonstrate that the recognition accuracy reaches 98.5%, which is 8% higher than that of the baseline method. The computational efficiency achieves an average single recognition time of 120 ms. Moreover, the peak memory occupancy remains below 350 MB, which fully validates its application potential in real-time diagnostic scenarios and considerably improves the balance between accuracy and efficiency. Thus, the proposed method provides a novel and reliable intelligent diagnosis tool for early fault warning in distribution equipment. Its technical approach holds great value in promoting the development of condition-based maintenance toward precision and intelligence.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00639-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147642674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Privacy-preserving energy scheduling for data center clusters based on evolutionary game theory","authors":"Liwen Guo,&nbsp;Jingsi Yang,&nbsp;Zhenhai Li,&nbsp;Ge Yang,&nbsp;Qiang Wang,&nbsp;Yiwei Cui,&nbsp;Zhuangxi Tan","doi":"10.1186/s42162-026-00641-w","DOIUrl":"10.1186/s42162-026-00641-w","url":null,"abstract":"<div><p>The substantial energy consumption of data centers challenges power grid stability and sustainable power development. While coordinating data centers for demand response is a viable solution, existing centralized approaches face scalability bottlenecks and privacy risks, and traditional game-theoretic models often rely on ideal assumptions of perfect rationality. To address these gaps, this paper proposes a privacy-preserving decentralized scheduling framework based on evolutionary game theory. Unlike centralized methods that require full state disclosure, the proposed framework models the customer directrix load (CDL) tracking problem as a population evolution process, enabling data centers to optimize strategies using only local information and aggregated signals. To address the limitations of standard game-theoretic approaches which often neglect physical constraints, this work incorporates a computation-power coupling model and employs a bounded rationality perspective. Furthermore, to solve the optimization in high-dimensional discrete spaces where traditional replicator dynamics fail, a hybrid algorithm (EGT-QCDE) integrating Q-learning with compound differential evolution is developed. Case studies based on real-world data validate that the proposed method guides the data center cluster to an evolutionarily stable strategy. The solution achieves reductions in overall operational costs while maintaining accurate tracking of the power grid’s dispatch targets.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00641-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147642376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of time series modeling for energy consumption prediction using the CBG-EnergyNet framework","authors":"Jiang Guan Min","doi":"10.1186/s42162-026-00642-9","DOIUrl":"10.1186/s42162-026-00642-9","url":null,"abstract":"<div><h3>Background</h3><p>Energy consumption forecasting is crucial to enhance energy distribution, planning, and smart grid control. Energy and especially electricity demands are perpetually varying because of urbanization, climate change, and due to digitalization, which makes the prediction of the consumption patterns of utility consumption even more difficult, as utility providers struggle to maintain the maximum precision in their forecasts.</p><h3>Problem Statement</h3><p>Current conventional approaches to forecasting, like ARIMA, often fail to give accurate results, either because they do not perfectly capture spatial aspects of energy data, or do not model long-term temporal aspects of energy data. In addition, newer methods in deep learning, like LSTM and GRU, may also prove unable to capture long-term temporal trends as well. In addition to that, poor model tuning and minimal feature engineering make the models less reliable and less generalizable. They did not have any attention mechanisms, in addition to weak hyperparameter tuning, which led to increased errors in making predictions and higher training times.</p><h3>Methodology</h3><p>As a way to overcome such gaps, in this research, a recently conceived hybrid deep learning framework has been deployed, which is a unification of Convolutional Neural Network (CNN) to accomplish feature extraction, Bidirectional LSTM (BiLSTM) to learn a sequence, and Genetic Algorithm (GA) to optimize hyperparameters, which is referred to as CBG-EnergyNet. The model takes advantage of real-world energy data collection and uses complete data preprocessing, sliding windows calculation of the function that uses the features, and large optimization topics such as Adam and Bayesian tuning as well.</p><h3>Results</h3><p>A number of performance indicators were used to test the proposed model and compared to such models as ARIMA, LSTM, GRU, and CNN-LSTM. CBG-EnergyNet: MAE: 7.83, RMSE: 10.12, MAPE: 7.05%, R 2 Score: 0,93, Training Time: 11.5 s. The outcomes are sufficient evidence that this model helps demonstrate better performance than the measures of the existing methods in all key metrics.</p><h3>Conclusions</h3><p>The CBG-EnergyNet model presents a very important breakthrough in energy consumption prediction since the combination of spatial and temporal learning proposes a very considerable improvement in estimation performance of energy consumption predictions by optimizing hyperparameters. It provides high accuracy and is hence a very important practical tool for energy planning in the real world, especially in a smart grid network and the system of demand-side management as well. The potential of the model is an opportunity to be applied in large-scale energy in the future due to its generalizability as well as the limited computational demands.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00642-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147642375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Presenting an enhanced particle swarm optimization method for decentralized operation planning of an integrated transmission and distribution network","authors":"Jianfeng Li,&nbsp;Luoluo Wang","doi":"10.1186/s42162-026-00627-8","DOIUrl":"10.1186/s42162-026-00627-8","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper proposes a decentralized operational planning framework for integrated transmission and distribution (TN–DN) networks that enables coordinated yet autonomous decision-making between network operators. A fully nonlinear alternating-current optimal power flow (AC-OPF) model is formulated for both TN and DN subsystems to capture voltage and reactive-power constraints accurately. A iterative coordination mechanism is introduced to preserve data privacy while ensuring operational consistency through limited exchange of active and reactive power at boundary buses. To solve the resulting high-dimensional, highly constrained nonlinear optimization problem efficiently, an enhanced particle swarm optimization (PSO) algorithm is developed, incorporating time-varying learning coefficients, adaptive local search, and chaotic diversity control to accelerate convergence and improve solution quality. In addition, a robust max–min optimization strategy is integrated to identify worst-case sudden generator outages without relying on scenario enumeration or probabilistic assumptions. The approach is validated on standard IEEE test systems. Numerical results show that the decentralized framework attains solutions close to centralized optimality while lowering computational effort, improving scalability, and enhancing resilience to generator contingencies, demonstrating its practical suitability for coordinated TN–DN operation planning.</p>\u0000 </div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00627-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147643186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}