Energy Informatics最新文献

{"title":"Analysis of distribution network reliability based on distribution automation technology","authors":"Liao Qinglong,&nbsp;Wu Xiaodong,&nbsp;Xie Song,&nbsp;Xaio Xiang,&nbsp;Peng Bo","doi":"10.1186/s42162-025-00478-9","DOIUrl":"10.1186/s42162-025-00478-9","url":null,"abstract":"<div><p>The growing complexity and need for electricity in contemporary grids have resulted in an increased dependence on Distribution Automation Technology (DAT) to improve the effectiveness and reliability of distribution networks. Automation technologies, like smart sensors and fault detection systems, are critical for enhancing operational efficiency and lowering power outages in distribution networks. This study investigates the influence of distribution automation on the dependability of electricity networks, concentrating on important functional metrics and their relationship with network efficiency. Objectives: The main objective of this research is to examine the factors that influence the reliability of distribution networks, with a focus on distribution automation technology. This study uses a variety of efficiency indicators, like automation coverage, fault detection time, and consumer complaints, to discover the primary factors of network reliability. This paper introduced the Reliability-Optimized Meta-Learning Ensemble (ROME) algorithm, which seeks to predict the reliability category of various areas using these indicators. Methodology: This study utilizes the Distribution Network Reliability Dataset, which includes several areas with a variety of characteristics such as network age, automation coverage, smart sensor installation, power outages, fault detection time, and other operational metrics. The ROME algorithm is used, which integrates numerous base models (SVM, Random Forest, MLP) and a meta-learner (Gradient Boosting) to predict each region’s Reliability Category (High, Medium, Low). The dataset is thoroughly preprocessed, which includes mean and mode imputation, label encoding, standardization, and SMOTE balancing. Recursive Feature Elimination (RFE) is used for feature selection. Results: The findings show a strong correlation between automation coverage, fault detection time, and reliability category. When compared to traditional classification techniques, the ROME algorithm surpassed SVM, RF, MLP, and GB models with 94.7% accuracy, 0.18 Log-Loss, 91.2% Jaccard Index, 0.08% fall-out, and 95.3% specificity. Conclusion: This research emphasizes the value of distribution automation in improving network reliability. Utilities and grid operators can use the ROME algorithm to better predict and enhance network reliability. The results highlight the requirement for targeted investments in automation technologies, particularly in regions with lower reliability scores, to guarantee sustainable and effective electricity distribution.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00478-9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489611","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":"Improving power distribution networks with dwarf mongoose optimization for improved photovoltaic incorporation in rural-urban settings","authors":"Guo Chen,&nbsp;JIA Honggang,&nbsp;Zeng Jian,&nbsp;Zhang Zhiqi,&nbsp;Zhou Xingxing","doi":"10.1186/s42162-025-00484-x","DOIUrl":"10.1186/s42162-025-00484-x","url":null,"abstract":"<div><p>This paper aimed to assess new connotations and characteristics of power distribution networks in new situations like integrating photovoltaic (PV) systems. Power system emission reduction is an ongoing subject of discourse, and solar energy production using PV is gaining popularity. Centralized and unidirectional systems, nevertheless, provide difficulties. An investigation is expected to comprehend the network’s design and PV integration capacity’s (PV-IC’s) responsiveness to subsequent generations.With an emphasis on low and medium-voltage networks, the paper presents a unique dwarf mongoose optimization (DMO)approachfor developing efficient network configurations. It analyzes the effect of network configuration on PV-IC.This study is experimented with MATLAB/Simulink platform based on the DMO technique. Different PV system numbers and peak powers, together with alternate providing substations, have been modeled for a certain set of load locations. The load time series computed shows rural-urban zones, and the proposed DMO is implemented on several topological generations. The computed results indicate that network topologies must be changed to accommodate raised solar energy production and PV-IC, with rural regions attaining up to 8.2 kW using TF (+). Our proposed DMO approach surpassed alternatives, with rural regions having a higher PV-based load of 190 kW compared to 120 kW in urban areas. Voltage control tactics, like RPC and Curt, benefit up to 55% of rural customers versus 15% in urban areas. Policy changes for household PV incorporation may be needed to maximize solar energy use.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00484-x","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143496966","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":"Energy management in a microgrid equipped with an electric vehicle based on the internet of things considering responsive load","authors":"Mohamad Mahdi Erfani Majd,&nbsp;Reza Davarzani,&nbsp;Mahmoud Samiei Moghaddam,&nbsp;Ali Asghar Shojaei,&nbsp;Mojtaba Vahedi","doi":"10.1186/s42162-025-00475-y","DOIUrl":"10.1186/s42162-025-00475-y","url":null,"abstract":"<div><p>Advancements in renewable energy technologies have positioned microgrids as essential applications of the Internet of Things (IoT), necessitating innovative energy management systems. This study introduces a dual-layer energy trading framework designed to optimize interactions among interconnected microgrids and users. The upper layer focuses on energy exchanges between microgrids, while the lower layer manages transactions among local users. A novel Energy-Trading Management Algorithm (ETMA), based on the Meerkat Optimization Algorithm (MOA), is proposed to tackle the complexities of this system. By integrating a multiblockchain structure with a Delegated Proof of Reputation (DPoR) consensus protocol, the framework ensures secure and private transactions while incentivizing compliance among participants. Experimental validation with real-world data from Guizhou demonstrates significant improvements in efficiency and utility for both users and microgrid operators (MGOs) compared to traditional methods. This approach sets a new benchmark for scalable, secure, and efficient energy management in microgrid environments. </p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00475-y","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489593","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":"Research on building energy consumption prediction algorithm based on customized deep learning model","authors":"Zheng Liang,&nbsp;Junjie Chen","doi":"10.1186/s42162-025-00483-y","DOIUrl":"10.1186/s42162-025-00483-y","url":null,"abstract":"<div><p>Forecasting energy usage in buildings is essential for implementing energy saving measures. Precisely forecasting building energy use is difficult due to uncertainty and noise disruption.To achieve enhanced accuracy in predicting energy use in buildings, a deep learning approach is proposed. This paper proposes a customized convolutional neural network with Q-Learning (CCNN-QL) based reinforcement learning algorithm for predicting energy consumption in building.The suggested CCNN-QL model offers an auto-learning feature that predicts building energy consumption through an automated method, continually improving its predictive accuracy.To assess its performance, various building types were selected to study the factors influencing excessive energy consumption, and data were collected from multiple Chinese cities. The suggested model’s performance has been assessed using evaluation metrics, resulting in a low Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), indicating superior accuracy relative to comparable studies. Experimental results indicate that the suggested technique has superior predictive performance across several scenarios of building energy usage.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00483-y","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489594","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":"A multi-agent approach with verifiable and data-sovereign information flows for decentralizing redispatch in distributed energy systems","authors":"Paula Heess,&nbsp;Stefanie Holly,&nbsp;Marc-Fabian Körner,&nbsp;Astrid Nieße,&nbsp;Malin Radtke,&nbsp;Leo Schick,&nbsp;Sanja Stark,&nbsp;Jens Strüker,&nbsp;Till Zwede","doi":"10.1186/s42162-024-00464-7","DOIUrl":"10.1186/s42162-024-00464-7","url":null,"abstract":"<div><p>The need to harness the flexibility of small-scale assets for system stabilization, including redispatch, is growing rapidly with the increasing prevalence of distributed generation, such as photovoltaic systems and heavy loads, in particular heat pumps and electric vehicles. Integrating these resources into the redispatch process presents special requirements: On the one hand, building trust with the owners of such assets requires privacy and a reasonable degree of autonomy and engagement. On the other hand, besides the system’s scalability and robustness, the verifiability and traceability of provided data are essential for grid operators who depend on the reliable provision of redispatch services. To date, research and practice have encountered significant challenges in defining a system that enables the inclusion of decentralized flexibilities while satisfying necessary requirements. To that end, we present a novel conceptual system design that addresses these challenges by combining a multi-agent system (MAS) approach with verifiable information flows through digital self-sovereign identities (SSIs) and Zero-Knowledge-Proofs (ZKPs). Single agents, as edge devices, operate locally and autonomously, respecting customer preferences, while MAS provide the ability to design robust, reliable, and scalable systems. SSI enables agents to manage their data autonomously, while ZKPs are used to protect users’ privacy through selective data disclosure which allows the verification of the correctness of information without disclosing the underlying data. To validate the feasibility of this design, a case study is included to demonstrate the functionality of key sub-processes, such as baseline optimization, aggregation, and disaggregation, in a realistic scenario. This case study, supported by a prototype implementation, provides initial evidence of the concept’s soundness and lays the groundwork for future evaluation through extensive simulations and field testing. Together, the technologies included in the conceptual system design balance full transparency for grid operators with autonomy and data economy for asset owners.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-024-00464-7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480972","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":"Enhancing virtual power plant efficiency: three-stage optimization with energy storage integration","authors":"Yuanbao Zhou,&nbsp;Shan Wu,&nbsp;Yuxiang Deng,&nbsp;Meihui Jiang,&nbsp;Yuxin Fu","doi":"10.1186/s42162-025-00477-w","DOIUrl":"10.1186/s42162-025-00477-w","url":null,"abstract":"<div><p>This study presents a three-stage scheduling optimization model for Virtual Power Plants (VPPs) that integrates energy storage systems to enhance operational efficiency and economic viability. The model addresses the challenges posed by the increasing integration of distributed renewable energy sources, such as wind and solar power, which often lead to fluctuations in power generation and grid instability. By employing a systematic approach, the model establishes a framework for day-ahead, intraday, and real-time scheduling, considering the response speed and timing of different energy storage devices. It uses comprehensive wind and solar power forecasts to formulate the declared output plan in the Day-Ahead Stage (DAS), adjusts scheduling plans in the Intraday Stage (IS) with pumped storage combined with thermal power plants, and employs the rapid response characteristics of energy storage batteries in the Real-Time Stage (RTS) to smooth deviations in real-time wind and solar scenarios. Simulations verify the model’s rationality and the feasibility of its operational strategy, demonstrating that multi-stage scheduling and the synergistic effect of energy storage effectively reduce deviations between real-time and declared outputs, thereby improving economic benefits.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00477-w","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465991","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":"Ground fault insulation monitoring method for smart substation based on Mahalanobis distance and automatic code generation","authors":"Xiang Li,&nbsp;Haopeng Shi,&nbsp;Ke Yang,&nbsp;Qiyan Dou,&nbsp;Najuan Jia","doi":"10.1186/s42162-025-00470-3","DOIUrl":"10.1186/s42162-025-00470-3","url":null,"abstract":"<div><p>The existence of a ring network significantly increases the proportion of harmonic components in the DC system branch current, correspondingly diminishing the low-frequency components, resulting in a decrease in signal-to-noise ratio and negatively impacting the accuracy of ground fault insulation monitoring. Consequently, an intelligent substation grounding fault isolation monitoring method based on Mahalanobis distance and code automatic generation is proposed. Utilizing the characteristics of grounding faults, the complex wavelet method is employed for fault detection, effectively addressing the issue of inaccurate results caused by directly extracting low-frequency components when the branch harmonic content is high. Based on the detection results, the Mahalanobis distance algorithm is utilized for fault localization. Subsequently, monitoring software was designed using automatic code generation technology, combined with real-time monitoring of DC bus to ground insulation resistance and inspection of faulty branches, to achieve insulation monitoring of grounding faults. The experimental results demonstrate that the amplitude error of this method is as low as 0.03%, the phase error is only 0.15%, the relative error remains below 1.0%, the monitoring accuracy is as high as 92%, and the maximum monitoring response time is only 2 s, substantiating that this method exhibits excellent monitoring performance and can significantly reduce errors in the monitoring process.substantiating that this method exhibits excellent monitoring performance and can significantly reduce errors in the monitoring process.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00470-3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143446449","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":"Distributed photovoltaic cluster output monitoring method based on time series data acquisition","authors":"Hua Ye,&nbsp;Xuegang Lu,&nbsp;Wei Zhang,&nbsp;Fei Cheng,&nbsp;Ying Zhao","doi":"10.1186/s42162-025-00480-1","DOIUrl":"10.1186/s42162-025-00480-1","url":null,"abstract":"<div><p>The data processing efficiency of distributed photovoltaic cluster output monitoring needs to be improved, improving the prediction effect of distributed photovoltaic power station cluster can effectively improve the security of power system operation and reduce the difficulty of power grid management. In order to obtain a reliable distributed photovoltaic cluster output monitoring method, this paper analyzes the output relationship of cluster power stations, combining time series data analysis methods for distributed cluster processing and monitoring data processing, a combined model of ceemdan and Bayesian neural network is proposed, the representative power plant prediction values obtained by establishing a combination model are weighted to obtain the cluster output prediction values. Compared with the simple superposition of the predicted values of cluster power stations, the average absolute error of this method is reduced by 3.3%, and the root mean square error is reduced by 5.86%. It is concluded that this model can effectively predict the power stations in the cluster. According to the experimental analysis, the output monitoring method of distributed photovoltaic clusters based on time series data collection proposed in this paper has certain effects and can provide theoretical support for the further development of distributed photovoltaic clusters.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00480-1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143446429","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":"Impact of digital transformation on corporate sustainability: evidence from China’s carbon emissions","authors":"Jiaomei Tang,&nbsp;Kuiyou Huang,&nbsp;Ailing Xiong","doi":"10.1186/s42162-025-00479-8","DOIUrl":"10.1186/s42162-025-00479-8","url":null,"abstract":"<div><p>Climate change has become an increasingly pressing issue, underscoring the urgent global need for energy conservation and emission reduction. As one of the largest emitters, China is actively advancing comprehensive efforts to reduce emissions in pursuit of sustainable development, with enterprises playing a key role in aligning economic growth with environmental sustainability. Digital Transformation (DT) has emerged as a crucial enabler of low-carbon economic development. This study utilizes data from publicly listed companies in China, spanning the period from 2000 to 2021, and employs a two-way fixed-effects model to assess the impact of corporate DT on Carbon Emissions (CE). The findings reveal that: First, DT significantly contributes to the reduction of CE; Second, the impact of DT on CE varies across regions, industries, and firm characteristics; Third, the positive effect of DT on CE is driven by mechanisms such as technological advancement, innovation promotion, resource optimization, and improved output efficiency. These results provide both theoretical insights and empirical evidence supporting the role of DT in fostering green, low-carbon enterprise development.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00479-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423221","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":"Hybrid feature-based neural network regression method for load profiles forecasting","authors":"Aidos Satan,&nbsp;Nurkhat Zhakiyev,&nbsp;Aliya Nugumanova,&nbsp;Daniel Friedrich","doi":"10.1186/s42162-025-00481-0","DOIUrl":"10.1186/s42162-025-00481-0","url":null,"abstract":"<div><p>This study addresses the critical need for improved demand forecasting models that can accurately predict energy consumption, particularly in the context of varying geographical and climatic conditions. The work introduces a novel demand forecasting model that integrates clustering techniques and feature engineering into neural network regression, with a specific focus on incorporating correlations with air temperature. Evaluation of the model’s efficacy utilized a benchmark dataset from Tetouan, Morocco, where existing forecasting methods yielded RMSE values ranging from 6429 to 10,220 [MWh]. In contrast, the proposed approach achieved a significantly lower RMSE of 5168, indicating its superiority. Subsequent application of the model to forecast demand in Astana, Kazakhstan, as a case study, showcased its efficacy further. Comparative analysis against a baseline neural network method revealed a notable improvement, with the proposed model exhibiting a MAPE of 5.19% compared to the baseline’s 17.36%. These findings highlight the potential of the proposed approach to enhance demand forecasting accuracy, particularly across diverse geographical contexts, by leveraging climate-related inputs, the methodology also demonstrates potential for broader applications, such as flood forecasting, agricultural yield prediction, or water resource management.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00481-0","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379845","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}