Energy Informatics最新文献

{"title":"Quantitative assessment and optimization strategy of flexibility supply and demand based on renewable energy high-penetration power system","authors":"Liangliang Zhang,&nbsp;Yimin Chu,&nbsp;Yanhua Xu,&nbsp;Wei Guo","doi":"10.1186/s42162-024-00431-2","DOIUrl":"10.1186/s42162-024-00431-2","url":null,"abstract":"<div><p>With the transformation of the global energy structure, the high penetration rate of renewable energy in power systems has become a trend. This article focuses on the quantitative evaluation and optimization strategies for the flexible supply and demand of renewable energy high-p penetration power systems. Using a combination of data-driven and model simulation methods, the flexibility requirements of the power system after integrating renewable energy are accurately quantified. The impact of uncertainty in renewable energy output on system flexibility was evaluated through system flexibility analysis and scenario construction techniques, and effective flexibility improvement strategies were proposed in combination with optimized scheduling design. The research results show that under high penetration of renewable energy, there is an imbalance between the supply and demand of flexibility in the power system. When the proportion of renewable energy installed capacity reaches 40%, the system flexibility gap reaches 10%. A comprehensive optimization strategy has been proposed to address this issue, including constructing energy storage facilities, demand side response, and virtual power plants. After implementing these measures, the flexibility gap of the system can be reduced to less than 5%, which can effectively ensure the stable operation of the power system.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-024-00431-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636821","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":"Distribution grid monitoring based on feature propagation using smart plugs","authors":"Simon Grafenhorst,&nbsp;Kevin Förderer,&nbsp;Veit Hagenmeyer","doi":"10.1186/s42162-024-00427-y","DOIUrl":"10.1186/s42162-024-00427-y","url":null,"abstract":"<div><p>Smart home power hardware makes it possible to collect a large number of measurements from the distribution grid with low latency. However, the measurements are imprecise, and not every node is instrumented. Therefore, the measured data must be corrected and augmented with pseudo-measurements to obtain an accurate and complete picture of the distribution grid. Hence, we present and evaluate a novel method for distribution grid monitoring. This method uses smart plugs as measuring devices and a feature propagation algorithm to generate pseudo-measurements for each grid node. The feature propagation algorithm exploits the homophily of buses in the distribution grid and diffuses known voltage values throughout the grid. This novel approach to deriving pseudo-measurement values is evaluated using a simulation of SimBench benchmark grids and the IEEE 37 bus system. In comparison to the established GINN algorithm, the presented approach generates more accurate voltage pseudo-measurements with less computational effort. This enables frequent updates of the distribution grid monitoring with low latency whenever a measurement occurs.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-024-00427-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600787","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":"New energy vehicle battery state of charge prediction based on XGBoost algorithm and RF fusion","authors":"Changyou Lei","doi":"10.1186/s42162-024-00424-1","DOIUrl":"10.1186/s42162-024-00424-1","url":null,"abstract":"<div><p>As the most important component of new energy electric vehicles, lithium-ion batteries may suffer irreversible damage to the battery due to an abnormal state of charge. Nevertheless, the extant research on charge prediction predominantly employs a single model or an enhanced single model. However, these approaches do not fully account for the intricacies and variability of the actual driving road conditions of the vehicle. Furthermore, the prediction accuracy of the charge state in the latter phase of discharge remains suboptimal. To further improve the accuracy of predicting the state of charge, the study utilizes actual operating data of new energy vehicles and combines two proposed algorithms to build a first layer learner of a fusion prediction model. The second layer learner integrates various prediction results. The fusion model can enhance its adaptability to complex data structures by combining the gradient boosting ability of XGBoost algorithm and the diversity of Random Forest when dealing with nonlinear problems. This fusion method modifies the input features of the second layer of the fusion model, enhances the complexity of the second layer learner, effectively circumvents overfitting, and exhibits reduced error rates relative to traditional single-chip prediction models. As a result, the performance of the prediction model is significantly enhanced. The tests showed that when using the fusion model for state of charge prediction, the prediction accuracy could reach 97.6%, and the prediction accuracy was higher than the other four comparison models. When the car was driving in a 25 ℃ highway environment, the root mean square error of the fusion model was 1.3%, and the average absolute error was 1.5%. In urban road environments, the root mean square error of the fusion model was 1.5%, and the average absolute error was 1%. The experiment demonstrates that the proposed fusion prediction model can accurately predict the charging status, thereby enabling the battery to be fully utilized while simultaneously reducing energy consumption. In comparison to the traditional single model or enhanced single model, the proposed fusion model has demonstrated a notable enhancement in both prediction accuracy and computational efficiency.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-024-00424-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598873","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 user behavior and energy-saving potential of electric water heaters","authors":"Jialin Liu,&nbsp;Xue Bai,&nbsp;Yujuan Xia,&nbsp;Yan Bai,&nbsp;Yanrong Kong","doi":"10.1186/s42162-024-00423-2","DOIUrl":"10.1186/s42162-024-00423-2","url":null,"abstract":"<div><p>As global energy resources get more limited and environmental problems worsen, it is crucial to enhance energy efficiency and reduce energy consumption in end-use products. This research focuses on electric water heaters, a significant household energy consumer, and collects a large amount of data through questionnaires and analyzes the current usage patterns of water heater use, as well as the impact of the users’ personal characteristics and energy-saving consciousness on usage behaviors. It also evaluates the energy-saving potential under different scenarios, considering both consumer behaviors and product efficiency levels. Results indicate that a substantial number of users still purchase high-energy-consuming water heaters and fail to adjust temperatures according to their specific needs, resulting in considerable energy waste. Electric water heaters exhibit significant potential for energy savings, with the efficiency of the product and user behaviors identified as key factors influencing overall energy consumption. The study provides important insights into the usage behavior of electric water heaters and offers actionable recommendations for manufacturers and government agencies: advocating the use of certified energy-efficient water heaters, raising public awareness of energy efficiency in appliance use, etc., which is in line with the country’s goals of energy conservation and environmental sustainability.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-024-00423-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595615","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 a digital twin model for incremental aggregation of multi type load information in hybrid microgrids under integrity constraints","authors":"Yibo Lai,&nbsp;Libo Fan,&nbsp;Weiyan Zheng,&nbsp;Rongjie Han,&nbsp;Kai Liu","doi":"10.1186/s42162-024-00404-5","DOIUrl":"10.1186/s42162-024-00404-5","url":null,"abstract":"<div><p>In the multi type load information of hybrid microgrids, data loss or incompleteness may occur due to network congestion, signal interference, equipment failures, and other reasons. Especially with the continuous generation of new load data, gradually incorporating these new data into the existing aggregation process to achieve continuous updating and optimization of load information. Therefore, this article proposes a digital twin model construction method for incremental aggregation of multi type load information in hybrid microgrids under integrity constraints. The Leida criterion and cubic exponential smoothing method are used to preprocess various load data of hybrid microgrids, remove abnormal data, reduce data fluctuations, and make the data more interpretable. Establish integrity constraints for multiple load data of hybrid microgrids and extract load characteristics of hybrid microgrids. Based on these, establish a digital twin model for the incremental aggregation of multiple load information in a hybrid microgrid, and solve the model using an improved K-means algorithm to achieve continuous updating and optimization of load information. The experimental results show that the data sharing delay of this method is 0.12 s, the load is basically consistent with the actual value, and the relative error of the load data is 4%.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-024-00404-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587798","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":"Multi-objective optimal configuration of CCHP system containing hybrid electric-hydrogen energy storage system","authors":"Jian Ye,&nbsp;Qiang Dong,&nbsp;Gelin Yang,&nbsp;Yang Qiu,&nbsp;Peng Zhu,&nbsp;Yingjie Wang,&nbsp;Liang Sun","doi":"10.1186/s42162-024-00413-4","DOIUrl":"10.1186/s42162-024-00413-4","url":null,"abstract":"<div><p>In order to cope with the increasing energy demand and achieve the “double carbon “goal of China’s 14th Five-Year Plan,” combined with hydrogen energy storage technology, it has the characteristics of zero pollution, high efficiency and rich source. In the context of reducing energy consumption and the vigorous development of hydrogen energy storage technology, a multi-objective optimization configuration model with economy, energy consumption index and carbon emission index is proposed, which takes into account the working characteristics of the hydrogen energy storage system, and the exothermic heat release from the electrolysis tanks and fuel cells when they are working to provide the loads with an additional heat source of the Combined Cooling, Heating and Power (CCHP) system, to reduce energy consumption and carbon emission. Finally, taking a region as an example, a multi-objective optimization algorithm based on decomposition is used to solve the model, so as to obtain a series of alternatives with better optimization effect. At the same time, the two-way projection method based on interval intuitionistic fuzzy information is used to make decisions, and the scheme that optimizes the economy, energy consumption index and carbon emission index is obtained, which verifies the feasibility of the system proposed in this paper.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-024-00413-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587800","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":"Prediction of building HVAC energy consumption based on least squares support vector machines","authors":"Xin Wan,&nbsp;Xiaoling Cai,&nbsp;Lele Dai","doi":"10.1186/s42162-024-00417-0","DOIUrl":"10.1186/s42162-024-00417-0","url":null,"abstract":"<div><p>Air conditioning, as an essential appliance in daily life, has the function of ensuring comfortable room temperature, but it is also accompanied by a large amount of power consumption. Consequently, the study suggests an energy consumption prediction model based on improved genetic algorithm—least squares support vector machine—to accurately predict the energy consumption of building heating, ventilation, and air conditioning. This model uses the improved genetic algorithm for regularization parameter and kernel parameter optimization to prevent overfitting and underfitting issues. According to the testing results, the least squares support vector machine, an upgraded genetic algorithm, may accomplish convergence faster than other algorithms, taking only 0.2 milliseconds to finish. In addition, the average relative error of the improved genetic algorithm- least squares support vector machine did not exceed 0.6%. In the energy consumption prediction for the whole year of 2022, the average error of the improved genetic algorithm-least squares support vector machine was only 2.0 × 10⁶ kWh, and the prediction accuracy could reach up to 97.2%. The above outcomes revealed that the energy consumption prediction model can accurately predict the air conditioning energy consumption, which provides a strong support for the control and optimization of the air conditioning system.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-024-00417-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595327","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":"Backup subscription scheme for differential protection using IEC61850-9-2 sampled values","authors":"Mohammad Khalili Katoulaei,&nbsp;Aamir Rahmani,&nbsp;Hans Kristian Høidalen,&nbsp;Irina Oleinikova,&nbsp;Bruce Mork","doi":"10.1186/s42162-024-00409-0","DOIUrl":"10.1186/s42162-024-00409-0","url":null,"abstract":"<div><p>In IEC-61850-based digital substations, the protection IED’s performance is dependent on merging unit’s vendor implementation, communication networks, and measurement circuit’s health conditions. As the process bus Sampled Value(SV) enables the availability of all sensor data on a communication network, this paper proposes a Backup Subscription scheme (BSS) for a transformer differential protection (87T, PDIF) function. BSS utilizes sensor data in digital substations to achieve a flexible protection scheme using a dynamic subscription feature. Thus, in case of failure of one sensor, differential protection would be maintained. The paper presents the implementation and verification of a prototyped scheme using a Hardware-in-the-loop simulation test bed. The main result is that BSS integration into differential protection ensures its dependability and security. Moreover, delay compensation and seamless switching feature increases the availability of differential protection.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-024-00409-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579497","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 safety and stability optimization algorithms for charging connection devices in high-power charging systems","authors":"Baoqiang Zhang,&nbsp;Yuan Ma,&nbsp;Fang Wang,&nbsp;Zizhang Xue,&nbsp;Shanming Liu,&nbsp;Bin Fan","doi":"10.1186/s42162-024-00398-0","DOIUrl":"10.1186/s42162-024-00398-0","url":null,"abstract":"<div><p>In response to the safety and stability issues of current electric vehicle charging connection devices, this study proposes a charging system planning for electric vehicles with different capacity charging piles based on the user behavior characteristics of electric vehicles and Monte Carlo methods. It is found that the predicted results under the set management strategy are most consistent with the trend of actual load changes. Moreover, in the prediction of weekly load, the research strategy has better performance than traditional unmanaged strategies. Under the research scheme, the average charging speed of charging piles with capacity of A and B in the peak period was 41.4 min/ and 18.8 min/, respectively, which increased by 29.3% and 11.7% respectively compared with 58.6 min/ and 21.3 min/ in the normal period. The total economic cost of the research plan was 4.871 million yuan, which was 67.0 million yuan and 3.833 million yuan lower than the control methods 1 and 2, respectively. The total number of charging stations of types a and b that need to be purchased for the research method decreased by 18.47% and 63.24% compared to the comparative method 3. The results indicate that the research method significantly improves the utilization rate of charging stations in the electric vehicle charging system. This study has important application value in the intelligent management of electric vehicle charging systems.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-024-00398-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524445","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":"Optimal management in island microgrids using D-FACTS devices with large-scale two-population algorithm","authors":"Mohamad Mehdi Khademi,&nbsp;Mahmoud Samiei Moghaddam,&nbsp;Reza Davarzani,&nbsp;Azita Azarfar,&nbsp;Mohamad Mehdi Hoseini","doi":"10.1186/s42162-024-00410-7","DOIUrl":"10.1186/s42162-024-00410-7","url":null,"abstract":"<div><p>Amidst the increasing complexity of microgrid optimization, characterized by numerous decision variables and intricate non-linear relationships, there is a pressing need for highly efficient algorithms. This study introduces a tailored Mixed Integer Nonlinear Programming (MINLP) model that optimizes the charging and discharging schedules of electric vehicles (EVs) and energy storage systems (ESS) while incorporating Distributed Flexible AC Transmission System (D-FACTS) devices. To address these challenges, a novel approach based on the Large-Scale Two-Population Algorithm (LSTPA) is proposed. The model's effectiveness was evaluated using a 33-node microgrid, where the proposed method achieved a total purchased energy of 1.2 MWh, a voltage deviation of 0.0357 p.u, and a CPU time of 551 s, outperforming traditional methods like NSGA-II, PSO, and JAYA. Additionally, in a 69-node microgrid, the approach resulted in a total purchased energy of 0.3 MWh and a voltage deviation of 0.0078 p.u. These results demonstrate the superior performance of the proposed method in terms of energy efficiency, voltage stability, and computational time, advancing the efficiency of microgrid management.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-024-00410-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524372","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}