Energy Informatics最新文献

Economic optimization scheduling of microgrid group based on chaotic mapping optimization BOA algorithm

Energy Informatics Pub Date : 2024-12-02 DOI: 10.1186/s42162-024-00422-3

Milu Zhou, Yu Wang, Tingting Li, Tian Yang, Xi Luo

{"title":"Economic optimization scheduling of microgrid group based on chaotic mapping optimization BOA algorithm","authors":"Milu Zhou, Yu Wang, Tingting Li, Tian Yang, Xi Luo","doi":"10.1186/s42162-024-00422-3","DOIUrl":"10.1186/s42162-024-00422-3","url":null,"abstract":"<div><p>Due to the intermittency and volatility of distributed power sources, the microgrid system has poor stability and high operation cost. Therefore, the study proposes an economic optimization scheduling strategy based on the chaotic mapping butterfly optimization algorithm and the mathematical model of microgrid group system. The study creates simulation trials of function poles and microgrid group operation to confirm the strategy’s efficacy. According to the experimental findings, the multimodal function of the enhanced butterfly optimization method had a variance of 0.0000E + 00, and the function’s optimal value was less than 10–30, and the calculation time is 4.5s. The variance on the fixed dimensional function was 0.0000E + 00 and the optimal value of the function was 10 − 3.5,and the calculation time is 4.7s. The algorithmic curve all digging depth was maximum and convergence speed was fastest. The microgrid group system had the lowest economic cost of 4029.32 yuan in the grid-connected mode and 3343.39 yuan in the off-grid mode. The study proves that the energy coordination and economic management of this strategy are greatly optimized, which can effectively protect the energy storage equipment and guarantee the smooth power consumption of the system. This provides an innovative theoretical basis for optimization scheduling of microgrid group.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-024-00422-3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142757899","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}

引用次数: 0

Loss reduction optimization strategies for medium and low-voltage distribution networks based on Intelligent optimization algorithms 基于智能优化算法的中低压配电网降低损耗优化策略

Energy Informatics Pub Date : 2024-11-29 DOI: 10.1186/s42162-024-00442-z

Nian Liu, Yuehan Zhao

{"title":"Loss reduction optimization strategies for medium and low-voltage distribution networks based on Intelligent optimization algorithms","authors":"Nian Liu, Yuehan Zhao","doi":"10.1186/s42162-024-00442-z","DOIUrl":"10.1186/s42162-024-00442-z","url":null,"abstract":"<div><h3>Problem</h3><p>With the rapid development of social economy, the problem of line losses in distribution networks gradually becomes prominent, which directly affects the efficiency and economy of power systems.</p><h3>Methodology</h3><p>In order to reduce line losses, a loss optimization model for low and medium voltage distribution networks based on an improved Gray Wolf optimization support vector machine is proposed. The optimization model introduces a dimensional learning strategy based on the original model to enhance the adaptability and robustness of the model.</p><h3>Results</h3><p>The experimental results show that the Mean Absolute Percent Error (MAPE) of the proposed algorithm is 8.62%, the Mean Absolute Error (MAE) is 1.30% and the Root Mean Square Error (RMSE) is 2.26%. Compared with the traditional Gray Wolf Optimized Support Vector Machine, the errors of the improved model are reduced by 15.27%, 3.33% and 4.70%, respectively.</p><h3>Contributions</h3><p>Our study demonstrates that extracellular vesicles secreted by the gut microbiota can influence the nervous system via the microbial-gut-brain axis. Furthermore, we found that the extracellular vesicles secreted by the gut microbiota from the probiotic group exert a beneficial therapeutic effect on anxiety and hippocampal neuroinflammation.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-024-00442-z","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142737364","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}

引用次数: 0

Multiobjective optimization for sizing and placing electric vehicle charging stations considering comprehensive uncertainties 考虑综合不确定性的电动汽车充电站规模和布局的多目标优化方法

Energy Informatics Pub Date : 2024-11-28 DOI: 10.1186/s42162-024-00428-x

Abdallah Mohammed, Omar Saif, Maged A Abo‑Adma, Rasha Elazab

引用次数: 0

Multistep Brent oil price forecasting with a multi-aspect aeta-heuristic optimization and ensemble deep learning model 利用多视角 Aeta 启发式优化和集合深度学习模型进行多步骤布伦特油价预测

Energy Informatics Pub Date : 2024-11-27 DOI: 10.1186/s42162-024-00421-4

Mohammed Alruqimi, Luca Di Persio

{"title":"Multistep Brent oil price forecasting with a multi-aspect aeta-heuristic optimization and ensemble deep learning model","authors":"Mohammed Alruqimi, Luca Di Persio","doi":"10.1186/s42162-024-00421-4","DOIUrl":"10.1186/s42162-024-00421-4","url":null,"abstract":"<div><p>Accurate crude oil price forecasting is crucial for various economic activities, including energy trading, risk management, and investment planning. Although deep learning models have emerged as powerful tools for crude oil price forecasting, achieving accurate forecasts remains challenging. Deep learning models’ performance is heavily influenced by hyperparameters tuning, and they are expected to perform differently under various circumstances. Furthermore, price volatility is also sensitive to external factors such as world events. To address these limitations, we propose a hybrid approach that integrates metaheuristic optimisation with an ensemble of five widely used neural network architectures for time series forecasting. Unlike existing methods that apply metaheuristics to optimise hyperparameters within the neural network architecture, we exploit the GWO metaheuristic optimiser at four levels: feature selection, data preparation, model training, and forecast blending. The proposed approach has been evaluated for forecasting three-ahead days using real-world Brent crude oil price data, and the obtained results demonstrate that the proposed approach improves the forecasting performance measured using various benchmarks, achieving 0.000127 of MSE.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-024-00421-4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142737012","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}

引用次数: 0

Local scour of composite cylindrical wind turbine foundation on fine sand seabed under combined waves and current 波浪和海流共同作用下细沙海床上复合圆柱形风力涡轮机基础的局部冲刷

Energy Informatics Pub Date : 2024-11-26 DOI: 10.1186/s42162-024-00420-5

Can Tang, Chunguang Yuan, Wei Tang, Na Zhang

{"title":"Local scour of composite cylindrical wind turbine foundation on fine sand seabed under combined waves and current","authors":"Can Tang, Chunguang Yuan, Wei Tang, Na Zhang","doi":"10.1186/s42162-024-00420-5","DOIUrl":"10.1186/s42162-024-00420-5","url":null,"abstract":"<div><p>The composite cylindrical wind turbine foundation is characterized by its large-diameter cylindrical base, which offers superior anti-overturning capability, and it is widely used in the soft soil seabed of Jiangsu, China. Due to its complex structural form, the local scour under combined waves and current significantly differs from that of monopile foundations. However, research on the scour characteristics specific to composite cylindrical wind turbine foundations remains scarce. A numerical model for local scour of wind turbine foundations was established in this study, which was verified with the field-measured scour data. A series of numerical simulations of local scour depths for composite cylindrical wind turbine foundations under various water depths and wave-current combinations were conducted. The simulation results indicate that the wake vortex shedding caused by the complex structural form leads to the local scour around the composite cylindrical wind turbine foundation; the normalized scour depth increases with the Keulegan-Carpenter number and the relative current strength; when the relative current strength is greater than 0.6, the influence of the Keulegan-Carpenter number on scour depth tends to be weakened; similarly, as the Keulegan-Carpenter number increases, the effect of the relative current strength on scour depth gradually diminishes. A scour equation of the composite cylindrical wind turbine foundation is suggested to predict the local scour in fine sand bed under waves and current.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-024-00420-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142714268","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}

引用次数: 0

Evaluation method of distribution network operation status based on local fuzzy measure in boundary region 基于边界区域局部模糊度量的配电网运行状态评价方法

Energy Informatics Pub Date : 2024-11-25 DOI: 10.1186/s42162-024-00432-1

Bing Yu, Peng Xie, Zhonglin Ding, Letian Li, Changan Chen, Chunfeng Jing

{"title":"Evaluation method of distribution network operation status based on local fuzzy measure in boundary region","authors":"Bing Yu, Peng Xie, Zhonglin Ding, Letian Li, Changan Chen, 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}

引用次数: 0

Two-stage optimization strategy for the active distribution network considering source-load uncertainty 考虑源负载不确定性的主动配电网络两阶段优化策略

Energy Informatics Pub Date : 2024-11-25 DOI: 10.1186/s42162-024-00435-y

Yong Fang, Yi Mu, Chun Liu, Xiaodong Yang

{"title":"Two-stage optimization strategy for the active distribution network considering source-load uncertainty","authors":"Yong Fang, Yi Mu, Chun Liu, 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}

引用次数: 0

Combinatorial chance-constrained economic optimization of distributed energy resources 分布式能源资源的组合机会约束经济优化

Energy Informatics Pub Date : 2024-11-25 DOI: 10.1186/s42162-024-00430-3

Jens Sager, Astrid Nieße

{"title":"Combinatorial chance-constrained economic optimization of distributed energy resources","authors":"Jens Sager, 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}

引用次数: 0

Micro-grid source-load storage energy minimization method based on improved competitive depth Q - network algorithm and digital twinning 基于改进的竞争深度 Q - 网络算法和数字孪生的微电网源负载储能最小化方法

Energy Informatics Pub Date : 2024-11-25 DOI: 10.1186/s42162-024-00416-1

Yibo Lai, Weiyan Zheng, Zhiqing Sun, Yan Zhou, Yuling Chen

{"title":"Micro-grid source-load storage energy minimization method based on improved competitive depth Q - network algorithm and digital twinning","authors":"Yibo Lai, Weiyan Zheng, Zhiqing Sun, Yan Zhou, 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}

引用次数: 0

Fault detection of key parts of wind turbine based on BP neural network combination prediction model 基于 BP 神经网络组合预测模型的风机关键部件故障检测

Energy Informatics Pub Date : 2024-11-20 DOI: 10.1186/s42162-024-00436-x

Jingjing Zhang, Liming Liu, Lei Wang, Wei Xi

{"title":"Fault detection of key parts of wind turbine based on BP neural network combination prediction model","authors":"Jingjing Zhang, Liming Liu, Lei Wang, 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}

引用次数: 0