Energy Informatics最新文献

{"title":"Comparison of deep learning algorithms for site detection of false data injection attacks in smart grids","authors":"Qassim Nasir,&nbsp;Manar Abu Talib,&nbsp;Muhammad Arbab Arshad,&nbsp;Tracy Ishak,&nbsp;Romaissa Berrim,&nbsp;Basma Alsaid,&nbsp;Youssef Badway,&nbsp;Omnia Abu Waraga","doi":"10.1186/s42162-024-00381-9","DOIUrl":"10.1186/s42162-024-00381-9","url":null,"abstract":"<div><p>False Data Injection Attacks (FDIA) pose a significant threat to the stability of smart grids. Traditional Bad Data Detection (BDD) algorithms, deployed to remove low-quality data, can easily be bypassed by these attacks which require minimal knowledge about the parameters of the power bus systems. This makes it essential to develop defence approaches that are generic and scalable to all types of power systems. Deep learning algorithms provide state-of-the-art detection for FDIA while requiring no knowledge about system parameters. However, there are very few works in the literature that evaluate these models for FDIA detection at the level of an individual node in the power system. In this paper, we compare several recent deep learning-based model that proven their high performance and accuracy in detecting the exact location of the attack node, which are convolutional neural networks (CNN), Long Short-Term Memory (LSTM), attention-based bidirectional LSTM, and hybrid models. We, then, compare their performance with baseline multi-layer perceptron (MLP)., All the models are evaluated on IEEE-14 and IEEE-118 bus systems in terms of row accuracy (RACC), computational time, and memory space required for training the deep learning model. Each model was further investigated through a manual grid search to determine the optimal architecture of the deep learning model, including the number of layers and neurons in each layer. Based on the results, CNN model exhibited consistently high performance in very short training time. LSTM achieved the second highest accuracy; however, it had required an averagely higher training time. The attention-based LSTM model achieved a high accuracy of 94.53 during hyperparameter tuning, while the CNN model achieved a moderately lower accuracy with only one-fourth of the training time. Finally, the performance of each model was quantified on different variants of the dataset—which varied in their <span>({text{l}}_{2})</span>-norm. Based on the results, LSTM, CNN obtained the highest accuracy followed by CNN-LSTM and lastly MLP.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-024-00381-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218447","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":"Construction of integrated network order system of main distribution network based on power grid operation control platform","authors":"Xi Yang,&nbsp;Kai Jia,&nbsp;Zirui Peng","doi":"10.1186/s42162-024-00368-6","DOIUrl":"10.1186/s42162-024-00368-6","url":null,"abstract":"<div><p>This study presents a major advance in grid management: the development and deployment of an integrated network command system for the main distribution network. The system integrates cutting-edge information technology, including modules such as command issuance, intelligent routing, security assurance and in-depth data analysis, opening a new era of refined and intelligent power grid management. The research focuses on the application of core technologies such as information communication technology, distributed control system, artificial intelligence and big data analysis, and strengthens the system operation foundation. The chapter on system architecture details the innovative integration of DDQN algorithm and attention mechanism, and carefully constructs intelligent scheduling engine and status monitoring and early warning system, which significantly improves real-time response, decision optimization and active security defense capabilities. Simulation experiments and actual case analysis verify the effectiveness of the system, specifically, the response time is reduced by 75.7%(from 2.1 s to 0.51 s in the traditional system), the data processing speed is still maintained at a high level under high load (100,000 data processing rate is 300/s), and the system stability is as high as 99.97%. The new system also achieved a high degree of automation, reducing annual operation and maintenance costs by 20%, and increasing user satisfaction to 90%, an increase of 28.6% over the previous period. These improvements not only optimize power quality and grid efficiency, but also further confirm that the fault response time is reduced by 30% and the user outage time is reduced by 25%. Therefore, this study not only highlights the innovation of the proposed system, but also demonstrates its significant contribution to accelerating the modernization of power grid management and ensuring safe operation with empirical data.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-024-00368-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218445","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":"Design of an integrated network order system for main distribution network considering power dispatch efficiency","authors":"Kai Jia,&nbsp;Xi Yang,&nbsp;Zirui Peng","doi":"10.1186/s42162-024-00369-5","DOIUrl":"10.1186/s42162-024-00369-5","url":null,"abstract":"<div><p>This study presents a comprehensive review of the primary distribution design of an advanced network control system, emphasizing its evolution from initial requirements to practical applications. The system solves complex problems of power management by combining real-time data analysis, intelligent decision making for resource allocation, rapid fault correction, remote monitoring and complex optimization methods, all aimed at ensuring stable and safe operation of the power grid. Its performance is geared towards fast response, efficient data processing and synchronous processing tasks, ensuring smooth operation even under heavy workloads. Security is enhanced through strict protocols, encryption methods, and controlled access systems. The system is divided into four layers-data collection, communication, decision-making and application management-using innovative tools such as Kalman filters and deep Q networks. The research showcases the integrated network command system’s prowess, achieving an average response time of 0.27 s, 98.5% dispatching accuracy, and 83.2% resource utilization, evidencing exceptional performance. It excels under various tests, including managing high loads with minimal accuracy loss, rapidly adapting to changes with a hydro model response time of 0.22 s, efficiently integrating renewables at 78.0% efficiency, and proving resilient in peak hours, affirming its capability to bolster grid efficiency, reliability, and integration of renewable energy resources. By outlining these specific achievements, this case study not only illustrates the complex design of the system, but also highlights its great potential for improving grid resilience and efficiency, attracting a wide audience interested in the future of energy management.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-024-00369-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218446","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":"Predictive digital twin for wind energy systems: a literature review","authors":"Ege Kandemir,&nbsp;Agus Hasan,&nbsp;Trond Kvamsdal,&nbsp;Saleh Abdel-Afou Alaliyat","doi":"10.1186/s42162-024-00373-9","DOIUrl":"10.1186/s42162-024-00373-9","url":null,"abstract":"<div><p>In recent years, there has been growing interest in digital twin technology in both industry and academia. This versatile technology has found applications across various industries. Wind energy systems are particularly suitable for digital twin platforms due to the integration of multiple subsystems. This study aims to explore the current state of predictive digital twin platforms for wind energy systems by surveying literature from the past five years, identifying challenges and limitations, and addressing future research opportunities. This review is structured around four main research questions. It examines commonly employed methodologies, including physics-based modeling, data-driven approaches, and hybrid modeling. Additionally, it explores the integration of data from various sources such as IoT sensors, historical databases, and external application programming interfaces. The review also delves into key features and technologies behind real-time systems, including communication networks, edge computing, and cloud computing. Finally, it addresses current challenges in predictive digital twin platforms. Addressing these research questions enables the development of hybrid modeling strategies with data fusion algorithms, which allow for interpretable predictive digital twin platforms in real time. Filter methods with dimensionality reduction algorithms minimize the computational resource demand in real-time operating algorithms. Moreover, advancements in high-bandwidth communication networks facilitate efficient data transmission between physical assets and digital twins with reduced latency.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-024-00373-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141946307","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":"Design and optimization strategy of electricity marketing information system supported by cloud computing platform","authors":"Bo Chen,&nbsp;Wei Ge","doi":"10.1186/s42162-024-00366-8","DOIUrl":"10.1186/s42162-024-00366-8","url":null,"abstract":"<div><p>This paper provides an in-depth discussion on the comprehensive requirements analysis, design implementation, algorithm optimization, and experimental evaluation of an electric power marketing information system, aiming to build a modern information system that is efficient, secure, and user-friendly. In the requirements analysis phase, the importance of business process optimization, data management analysis, security compliance, system integration and scalability is emphasized, while the diversified needs of end customers are considered. For the design and implementation part, the system architecture is based on microservices and cloud-native technologies to ensure high performance and security; and modularized development is achieved through Spring Boot, Vue.js and other technology stacks. For algorithm optimization, LSTM is used for power demand prediction and anomaly detection by combining integrated learning and self-encoder, which improves the prediction accuracy and anomaly identification capability. Experimental evaluation shows that the system demonstrates good performance, security and scalability in cloud computing environment, and the cost-effectiveness is significantly better than traditional deployment.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-024-00366-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141946308","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":"The influencing factors of green technology innovation in renewable energy companies based on hyper-network","authors":"Hui Sun,&nbsp;Yan Yan,&nbsp;Yonghua Han","doi":"10.1186/s42162-024-00361-z","DOIUrl":"10.1186/s42162-024-00361-z","url":null,"abstract":"<div><p>Green technology innovation is a critical factor in ensuring the long-term stable development of renewable energy enterprises. Based on the super network theory, this paper constructs a network model of green technology innovation influencing factors of renewable energy enterprises, which includes the knowledge sub-network of green technology innovation of renewable energy enterprises, the research and development member sub-network of green technology innovation team of renewable energy enterprises and the policy sub-network of green technology innovation of renewable energy enterprises. It explores the mechanism of its influence on innovation in the preparation stage. Simulation analysis by Netlogo software concludes that innovation knowledge sharing, R&amp;D membership, and innovation policy all have a significant positive impact on green technology innovation in renewable energy companies.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-024-00361-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141946309","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 KNN algorithm incorporating Gaussian functions in green and high-quality development of cities empowered by circular economy","authors":"Zhezhou Li,&nbsp;Hexiang Huang","doi":"10.1186/s42162-024-00372-w","DOIUrl":"10.1186/s42162-024-00372-w","url":null,"abstract":"<div><p>A growing number of industries have started to adapt to the circular economy since the concept's introduction. Therefore, in order to accurately evaluate the development level of circular economy, the circular economy prediction model based on support vector machine-Gaussian K-nearest neighbor is proposed. This model first uses the improved K-nearest neighbor algorithm based on Gaussian function to classify the index data of various levels, and then uses Support Vector Machine to make predictions based on relevant data. According to the experimental findings, the model's average prediction accuracy for each level of indicator was approximately 98.1%, 98.8%, 94.9%, and 95.9% for the levels of industrial development, resource consumption, ecological protection, and resource recycling and reuse, respectively. This prediction accuracy was higher than that of the multi-vector autoregressive model and the grey prediction model. The average prediction accuracy of the multi-vector autoregressive model, the grey prediction model, and the support vector machine-Gaussian K-nearest neighbor-based model in predicting the overall development level of the circular economy were about 94.3%, 96.2%, and 99.3%, respectively, with average recalls of about 86.6%, 87.7%, and 89.1%, and the average F1-measure was about 0.88, 0.89, and 0.92. Moreover, the average relative error based on the support vector machine-Gaussian K-nearest neighbour model was only approximately 0.6%, which was lower than the 3.7% and 2.8% for the multi-vector autoregressive model and the grey prediction model, respectively. Meanwhile, compared with the existing time series analysis techniques, the proposed SVM-Gaussian K nearest neighbor model fitted up to 0.95, which achieved good prediction performance. According to the above data, the support vector machine-Gaussian K-nearest neighbour model has the highest accuracy in predicting the amount of development of the circular economy.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-024-00372-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141946310","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 comprehensive review of advancements and challenges in reactive power planning for microgrids","authors":"Abdallah Mohammed,&nbsp;Eman Kamal Sakr,&nbsp;Maged Abo‑Adma,&nbsp;Rasha Elazab","doi":"10.1186/s42162-024-00341-3","DOIUrl":"10.1186/s42162-024-00341-3","url":null,"abstract":"<div><p>The effective management of reactive power plays a vital role in the operation of power systems, impacting voltage stability, power quality, and energy transmission efficiency. Despite its significance, suboptimal reactive power planning (RPP) can lead to voltage instability, increased losses, and grid capacity constraints, posing risks to equipment and system reliability. Rigorous RPP methodologies can mitigate these challenges, resulting in tangible improvements in voltage profiles, system stability, and reduced losses. A comprehensive review of 20 technical articles published between 2020 and 2023 was conducted to compare and synthesize contributions to the field of RPP. The review highlighted the efficacy of strategic RPP approaches in reducing power losses, minimizing equipment malfunctions, and improving power quality, leading to substantial economic benefits—strategic planning approaches and integrating emerging technologies. For instance, examples include renewable energy sources and energy storage systems, which offer promising avenues for enhancing RPP and ensuring stability, reliability, and efficiency in power systems.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-024-00341-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141946347","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 expansion planning with a human evolutionary model","authors":"Hosein Farokhzad Rostami,&nbsp;Mahmoud Samiei Moghaddam,&nbsp;Mehdi Radmehr,&nbsp;Reza Ebrahimi","doi":"10.1186/s42162-024-00371-x","DOIUrl":"10.1186/s42162-024-00371-x","url":null,"abstract":"<div><p>This study presents a novel method for planning the expansion of transmission lines and energy storage systems while considering the interconnectedness of electricity and gas networks. We developed a two-level stochastic planning model that addresses both the expansion of transmission and battery systems in the electrical grid and the behavior of the gas network. This research explores the challenges and effects of integrating high levels of renewable energy sources while ensuring security within both networks. Our model uses a stochastic mixed-integer non-linear programming approach. To solve this complex model, we applied the Human Evolutionary Model (HEM). We tested our approach on two case studies: a simple 6-node network and the more complex IEEE RTS 24-bus network for the electricity grid, combined with 5-node and 10-node gas networks, respectively. The results demonstrate the effectiveness of our model, particularly in scenarios where connections in the power and gas networks are disrupted, preventing load shedding even when integrated network connections are cut.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-024-00371-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141946311","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 renewable energy technologies in Colombia: comparative evaluation using TOPSIS and TOPSIS fuzzy metaheuristic models","authors":"Christian Manuel Moreno Rocha,&nbsp;Daina Arenas Buelvas","doi":"10.1186/s42162-024-00348-w","DOIUrl":"10.1186/s42162-024-00348-w","url":null,"abstract":"<div><p>The study investigates the weighting and hierarchization of renewable energy sources in specific geographical regions of Colombia using the TOPSIS and Diffuse TOPSIS metaheuristic models. 5 regions were analyzed, two of them with different scenarios: Caribbean 1 and 2, Pacific 1 and 2, Andean, Amazonian and Orinoquia. The results reveal significant differences in the evaluation of technologies between the two models. In the Caribbean 1, Diffuse TOPSIS gave a higher score to Solar Photovoltaics, while TOPSIS favored Hydropower. In the Caribbean 2, Solar Photovoltaic obtained similar scores in both models, but Wind was rated better by TOPSIS. In the Pacific Region 1, Biomass and large-scale Hydropower led according to both models. In the Pacific 2, Solar Photovoltaic was better evaluated by TOPSIS, while Wind was preferred by Diffuse TOPSIS. In the Andean Region, large-scale hydroelectric and Solar photovoltaic plants obtained high scores in both models. In the Amazon, Biomass led in both models, although with differences in scores. In Orinoquia, Solar Photovoltaic was rated higher by both models. The relevance of this research lies in its ability to address not only Colombia's immediate energy demands, but also in its ability to establish a solid and replicable methodological framework. The application of metaheuristic methods such as TOPSIS and TOPSIS with fuzzy logic is presented as a promising strategy to overcome the limitations of conventional approaches, considering the complexity and uncertainty inherent in the evaluation of renewable energy sources. By achieving a more precise weighting and hierarchization, this study will significantly contribute to strategic decision-making in the implementation of sustainable energy solutions in Colombia, serving as a valuable model for other countries with similar challenges.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-024-00348-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141946348","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}