Energy Informatics最新文献

{"title":"Evaluation method of distribution network operation status based on local fuzzy measure in boundary region","authors":"Bing Yu,&nbsp;Peng Xie,&nbsp;Zhonglin Ding,&nbsp;Letian Li,&nbsp;Changan Chen,&nbsp;Chunfeng Jing","doi":"10.1186/s42162-024-00432-1","DOIUrl":"10.1186/s42162-024-00432-1","url":null,"abstract":"<div><p>With the increasing complexity of the distribution network, the proportion of abnormal data in the monitoring data of the distribution network and its daily work is extremely low. Traditional clustering analysis methods are difficult to effectively solve the imbalance problem. Therefore, this paper introduces the influence parameters that can adaptively adjust the cluster center of local samples in the boundary area, and improves the cluster center update formula, and proposes a method of distribution network operation state evaluation based on the local blur measurement of the boundary region. The research results found that the five evaluation indicators of the proposed algorithm were 112, 0, 2, 26, and 5, respectively, all of which were superior to the comparison algorithms. The research results showed that the cluster center update optimization method based on local fuzzy measure in boundary region could effectively reduce the negative impact of the edge region occupied by most clusters on its clustering effect, so that the cluster center was always in an ideal position. At the same time, the example results showed that the research method had a risk prediction of 0.91 for power outage networks, which was close to the real situation and had high accuracy. It can provide reference for the operation and maintenance work of power grid personnel, eliminate hidden dangers in advance, and ensure the safe operation of the power grid.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-024-00432-1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142694855","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":"Two-stage optimization strategy for the active distribution network considering source-load uncertainty","authors":"Yong Fang,&nbsp;Yi Mu,&nbsp;Chun Liu,&nbsp;Xiaodong Yang","doi":"10.1186/s42162-024-00435-y","DOIUrl":"10.1186/s42162-024-00435-y","url":null,"abstract":"<div><p>This study aims to advance the development of the active distribution network (ADN) by optimizing resource allocation across different stages to enhance overall system performance and economic benefits. First, an ADN optimization model is constructed based on a two-stage robust optimization approach. The first stage focuses on determining optimal decision variables within the uncertainty set, while the second stage adjusts control variables based on the initial stage decisions. This model effectively addresses source-load uncertainties while preserving the flexibility and adaptability of decision-making solutions. Additionally, this study explores uncertainty models that incorporate correlation factors. The IEEE33-node model is employed to validate the effectiveness and superiority of the proposed optimization strategy through numerical simulations. Simulation results demonstrate that Model 3 comprehensively accounts for photovoltaic and wind turbine generator planning by optimizing their capacity configurations, leading to a 23% increase in distributed generation (DG) penetration. During high-load periods (e.g., 13:00 and 16:00), DG output reaches 47% and 50% of the demand load, underscoring the critical role of DG in supporting the power grid during peak hours. Overall, the proposed two-stage optimization strategy considers source-load uncertainties, significantly reducing economic costs, enhancing DG output, and improving overall system performance. In scenarios with correlated uncertainties, the optimized results exhibit greater accuracy and reliability, providing robust support for the planning and operation of practical distribution networks.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-024-00435-y","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142694856","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":"Combinatorial chance-constrained economic optimization of distributed energy resources","authors":"Jens Sager,&nbsp;Astrid Nieße","doi":"10.1186/s42162-024-00430-3","DOIUrl":"10.1186/s42162-024-00430-3","url":null,"abstract":"<div><p>The transformation of the energy system towards sustainable energy sources is characterized by an increase in weather dependent distributed energy resources (DER). This adds a layer of uncertainty in energy generation on top of already uncertain load distribution. At the same time, many households are fitted with renewable generation units and storage systems. The increased intermittent generation in the distribution grid leads to new challenges for the commitment and economic dispatch of DER. The main challenge addressed in this work is to decide which available resources to select for a given task. To solve this, we introduce Stochastic Resource Optimization (SRO), a general purpose, combinatorial, chance-constrained optimization model for the short-term economic selection of stochastic DER. It incorporates correlations between stochastic resources are using copula theory. The contributions of this paper are twofold: First, we validate the applicability of the SRO formulation on a simplified congestion management use-case in a small neighbourhood grid comprised of prosumer households. Second, we provide an analysis of the performance of different solving algorithms for SRO problems and their run-times. Our results show that a fast metaheuristic algorithm can provide high quality solutions in acceptable time on the evaluated problem sets.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-024-00430-3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142694726","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":"Micro-grid source-load storage energy minimization method based on improved competitive depth Q - network algorithm and digital twinning","authors":"Yibo Lai,&nbsp;Weiyan Zheng,&nbsp;Zhiqing Sun,&nbsp;Yan Zhou,&nbsp;Yuling Chen","doi":"10.1186/s42162-024-00416-1","DOIUrl":"10.1186/s42162-024-00416-1","url":null,"abstract":"<div><p>Aiming at the frequency instability caused by insufficient energy in microgrids and the low willingness of grid source and load storage to participate in optimization, a microgrid source and load storage energy minimization method based on an improved competitive deep Q network algorithm and digital twin is proposed. We have constructed a basic framework structure for the coordinated operation of source grid load and energy storage, and analyzed the modules on the power supply side, grid side, load side, and energy storage side. Under the improved competitive deep Q network algorithm, modifications were made to the energy storage of microgrid loads. Based on the processing results, the objective function for optimizing microgrid source load energy storage is constructed using digital twin technology, and the optimization of the objective function is achieved to solve the optimization objective function for microgrid source load energy storage and complete the optimization of microgrid source load energy storage. The experimental results show that this method can control the distortion rate within 5.12%, with frequency fluctuations around 50.0 Hz, and relatively good MSE, MAE, and R2 values. This method can effectively control frequency fluctuations and has a good effect on optimizing energy storage for microgrid power sources and loads.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-024-00416-1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142694811","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":"Fault detection of key parts of wind turbine based on BP neural network combination prediction model","authors":"Jingjing Zhang,&nbsp;Liming Liu,&nbsp;Lei Wang,&nbsp;Wei Xi","doi":"10.1186/s42162-024-00436-x","DOIUrl":"10.1186/s42162-024-00436-x","url":null,"abstract":"<div><p>A BP neural network incorporated regression forecast technique based upon fragment swarm optimization (PSO) is proposed to design the state of crucial components of wind turbine so regarding realize mistake identification and detection. Firstly, specification recognition is carried out on the collection and tracking information of the system, and parameters connected to fault detection are extracted. Then, the residual optimization issue is made use of to establish the forecast model of nonlinear state evaluation and semantic network combination, and the gearbox temperature level or generator bearing are input as criteria right into the semantic network combination model and single model specifically, and the precision of the design is mirrored by the examination index. Lastly, BP design and PSO-BP combined forecast model are developed respectively by using the actual operation data of wind ranch SCADA, and the mistake state is evaluated according to whether the anticipated residual exceeds the set threshold, so regarding keep an eye on the temperature level of wind turbine transmission and generator bearing. By contrasting the data videotaped prior to and after the failing and making the information prediction analysis, the speculative results show that the forecast model established in this paper is viable for the device element fault detection.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-024-00436-x","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679666","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":"Fault diagnosis of intelligent substation relay protection system based on transformer architecture and migration training model","authors":"Yao Mei,&nbsp;Saisai Ni,&nbsp;Haibo Zhang","doi":"10.1186/s42162-024-00429-w","DOIUrl":"10.1186/s42162-024-00429-w","url":null,"abstract":"<div><p>In the context of global energy transformation, the construction of smart grids is becoming a novel vogue in the evolution of power systems. As the core node of the smart grid, the efficient operation of the intelligent substation relay protection system is essential to the safety and stability of the power system. However, with the expansion of power grid-scale and complexity, traditional relay protection systems need help with fault diagnosis accuracy and response speed. This study proposes a fault diagnosis scheme of an intelligent substation relay protection system based on Transformer architecture and migration training model, aiming at improving the intelligent level of fault diagnosis. By introducing the Transformer architecture, the model can efficiently process high-dimensional and nonlinear complex data of substations, significantly improving the accuracy of fault pattern recognition from 82% of the original model to 96%, and the response speed is also increased by 30%. At the same time, using transfer learning technology, the adaptability and generalization capabilities of the model in new scenarios have been significantly enhanced, reducing the dependence on a large amount of new data and accelerating the deployment of the model among different substations. The experimental results show that this scheme can quickly and accurately identify various fault types and effectively locate fault points. This study not only promotes the development of intelligent technology for power systems but also lays a solid foundation for the safe and stable operation of smart grids and the sustainable development of the power industry.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-024-00429-w","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672569","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":"Approach for energy efficient building design during early phase of design process","authors":"Aviruch Bhatia,&nbsp;Shanmukh Dontu,&nbsp;Vishal Garg,&nbsp;Reshma Singh","doi":"10.1186/s42162-024-00426-z","DOIUrl":"10.1186/s42162-024-00426-z","url":null,"abstract":"<div><p>Energy consumption in the building sector is about 40% of total energy consumed globally and is trending upwards, along with its contribution to greenhouse gas (GHG) emissions. Given the adverse impacts of GHG emissions, it is crucial to integrate energy efficiency into building designs. The most significant opportunities for enhancing energy performance are present during the initial phases of building design, when there is less impact of other design constraints. Various tools exist for simulating different design options and providing feedback in terms of energy consumption and comfort parameters. These simulation outputs must then be analyzed to derive design solutions. This paper presents an innovative approach that utilizes user input parameters, processes them through cloud computing, and outputs easily understandable strategies for energy-efficient building design. The methodology employs Asynchronous Distributed Task Queues (DTQ) - a more scalable and reliable alternative to conventional speedup techniques-for conducting parametric energy simulations in the cloud. The goal of this approach is to assist design teams in identifying, visualizing, and prioritizing energy-saving design strategies from a range of possible solutions for each project. Furthermore, a tool ‘eDOT’ has been developed utilizing the discussed methodology. Unlike existing tools, eDOT leverages artificial intelligence to dynamically generate and provide design strategies during the early phases of design process. By simplifying the simulation process, eDOT enables design teams to make informed, data-driven decisions without needing to interpret complex simulation outputs. A case study simulated for two locations is provided in this paper to demonstrate the effectiveness of eDOT, further underscoring its practical impact on energy-efficient building design.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-024-00426-z","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672701","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":"Application of multi-sensor information fusion technology in fault early warning of smart grid equipment","authors":"Zhihui Kang,&nbsp;Yanjie Zhang,&nbsp;Yuhong Du","doi":"10.1186/s42162-024-00433-0","DOIUrl":"10.1186/s42162-024-00433-0","url":null,"abstract":"<div><p>The purpose of this paper is to improve the fault early warning effect of smart grid equipment through multi-sensor information fusion technology. Therefore, based on the analytical model of power grid fault diagnosis, this paper considers the influence of distributed generation in distribution network on fault diagnosis, as well as the misoperation or refusal of protection and switch, and the false alarm or leakage of alarm signal. At the same time, in order to display the results of fault diagnosis accurately and intuitively, an analytical model of fault diagnosis of distribution network based on multi-source information fusion is proposed. Finally, this paper verifies the effectiveness of this method through an example application. This article uses the PEDL dataset for experimental research, Through the comparison of fault data, it can be seen that compared with existing methods, the method proposed in this paper achieves the highest goodness of fit for warning, indicating the best fault warning effect.When there is enough training set, the prediction accuracy of the fault set can reach over 99%, Based on experimental analysis, it can be concluded that the proposed power grid equipment model has higher accuracy and reliability compared to traditional models. And the model in this article integrates the real-time monitoring function of power grid equipment and the equipment fault warning function, which improves the practicality of the power grid equipment monitoring system.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-024-00433-0","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672695","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":"Optimization of energy acquisition system in smart grid based on artificial intelligence and digital twin technology","authors":"Zhen Jing,&nbsp;Qing Wang,&nbsp;Zhiru Chen,&nbsp;Tong Cao,&nbsp;Kun Zhang","doi":"10.1186/s42162-024-00425-0","DOIUrl":"10.1186/s42162-024-00425-0","url":null,"abstract":"<div><p>In response to the low operating speed and poor stability of energy harvesting systems in smart grids, an energy harvesting optimization method based on improved convolutional neural networks and digital twin technology is proposed in the experiment. Firstly, a smart grid data transmission framework integrating digital twin technology is proposed. A digital twin mapping method based on time, data, and topology structure is used to realize the digital twin mapping at the device level of power grid. Through data synchronization and interaction between the physical power grid and the digital twin model, the operational efficiency and reliability of the power grid are improved. Then, the classical convolutional neural network and attention mechanism are used to comprehensively analyze the physical topology data in the smart grid energy acquisition system. The improved lightweight target detection model is combined to monitor the equipment status of the smart grid and extract key features. Simultaneously utilizing convolutional attention mechanism to dynamically adjust the feature weights of channels or spaces, completing the preprocessing of energy harvesting data. Finally, combined with energy harvesting and power grid switching system, the process of energy harvesting and power grid operation are optimized together. On the training and validation sets, when the channels exceeded 60, the proposed method achieved a system energy efficiency of 55% during operation. The system energy efficiency of the other three comparative algorithms was all less than 40%. In practical applications, as the energy transfer loss increased to 1.0, the system throughput increased to 50 bits. The electricity needs of different users were met, and the difference between power allocation and optimal power allocation was small, which was very reasonable. This proves that the research has effectively optimized the energy harvesting system in the smart grid, improving the efficiency and reliability of the system in practical applications of the smart grid. At the same time, in the increasingly severe energy problem, this system can further provide technical references for the utilization of renewable energy and help achieve the goal of sustainable energy.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-024-00425-0","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672702","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":"Water resource vulnerability assessment in Hubei Province: a case study","authors":"Qiong Li,&nbsp;Jian Zhou,&nbsp;Zhinan Zhang","doi":"10.1186/s42162-024-00419-y","DOIUrl":"10.1186/s42162-024-00419-y","url":null,"abstract":"<div><p>In view of the different views of academia on the weight allocation of vulnerability assessment indicators, this study creatively proposed a data-based objective evaluation framework of water resource vulnerability, and applied it to the evaluation of water resource vulnerability in Hubei Province. According to the conceptual model of DPSIR proposed by the United Nations, five vulnerability factors are proposed: driving force, pressure, state, influence and response. In this study, 15 indicators were selected and the projection tracing model was used to identify vulnerability. Aiming at the complex problem of optimization calculation of projection index function in the projection tracing model, the accelerated genetic algorithm is used to speed up the optimization speed, solves the optimization problem in the process of projection tracing, and determines the objective weight of all indicators. Example calculation shows that the model can deal with complex multi-index optimization problems, and is an effective way to solve the comprehensive evaluation of complex vulnerability, and the weighting method is important for the evaluation of water resources vulnerability. The results of this paper show that the combination of projection tracing method and machine learning algorithm can improve the efficiency, objectivity and accuracy of high-dimensional data analysis, and can provide scientific basis for policy makers.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-024-00419-y","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672676","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}