Energy Informatics最新文献

{"title":"Construction of source load uncertainty economic dispatch model based on distributed robust opportunity constraints","authors":"Jinjian Li","doi":"10.1186/s42162-025-00503-x","DOIUrl":"10.1186/s42162-025-00503-x","url":null,"abstract":"<div><p>With the increasing demand for electricity, the power system is facing enormous challenges. To ensure the equilibrium between supply and demand in the electricity market and the safety and stability of the power grid, a source load uncertainty economic dispatch model based on distributed robust opportunity constraints is proposed to cope with the uncertainty of sustainable energy resources such as wind power and photovoltaics. By introducing an improved Elman network and grey wolf optimization algorithm, high-precision prediction of short-term loads is achieved, providing data support for scheduling models. The experiment outcomes indicate that the prediction model grounded on the improved Elman network and grey wolf optimization algorithm performs the best in scheduling performance on both the training and testing sets, with the lowest cost, the highest utilization rates of wind and solar power, and the lowest probability of constraint default. In addition, the economic dispatch model proposed by the research has significant advantages in reducing total dispatch costs, improving wind and photovoltaic utilization rates, and constraining default probability control. In typical load scenarios, the total scheduling cost of the model is $1,308,469, with wind and photovoltaic utilization rates reaching 90.5% and 86.1% respectively, and a default probability of only 0.9%. The research results indicate that the model exhibits superiority in real-time response time, especially suitable for scenarios with high load fluctuations. The research provides important theoretical basis and application value for the economic dispatch of power systems.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00503-x","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143688589","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 control method of regional power grid based on elastic carrying capacity analysis and day-ahead evaluation","authors":"Yu Zhang,&nbsp;Qingsheng Li","doi":"10.1186/s42162-025-00506-8","DOIUrl":"10.1186/s42162-025-00506-8","url":null,"abstract":"<div><p>To achieve the coordinated consumption and control of a high proportion of renewable energy in the current regional power grid while ensuring sufficient safety margins, this paper proposes an optimization control method based on elastic carrying capacity analysis and recent evaluation. Firstly, a cloud-edge-based sub-provincial collaborative intelligent control model is adopted, integrating power industry and Internet of Things (IoT) technology to realize grid state data perception through multiple sensors. Secondly, based on these data, grid assessment, grid vulnerability assessment, and grid mapping elastic potential analysis are completed. On this basis, a multi-scale collaborative intelligent control method for sub-provincial power grid transmission and distribution is then constructed. Finally, taking the Xingyi power grid as the research object, this paper applies the proposed method to improve the safety margin. The experimental results show that after applying the method, with an installed energy penetration rate close to 180%, reaches more than 95%. This indicates that the method proposed in this paper not only improves the consumption efficiency of new energy, but also significantly enhances the security and stability of the regional power grid, providing new ideas and practices for the sustainable development of regional power grids.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00506-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143676318","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 novel LSTPA methodology for managing energy in electrical/thermal microgrids through CHP, battery resources, thermal storage, and demand-side strategies","authors":"Elmira Akhavan Maroofi,&nbsp;Mahmoud Samiei Moghaddam,&nbsp;Azita Azarfar,&nbsp;Reza Davarzani,&nbsp;Mojtaba Vahedi","doi":"10.1186/s42162-025-00507-7","DOIUrl":"10.1186/s42162-025-00507-7","url":null,"abstract":"<div><p>This paper presents a stochastic optimization model for integrated energy management in electrical and thermal microgrids, addressing uncertainties in renewable energy resources. The model optimizes the placement of combined heat and power (CHP) systems, energy storage, and demand-side management for both islanded and grid-connected operations. A multi-objective function is formulated to minimize energy losses, voltage deviations, costs, and renewable supply interruptions. The Large-Scale Two-Population Algorithm (LSTPA) is employed to solve the problem, with the IEEE 69-bus network as a case study. Results indicate that the proposed approach reduces energy losses to 3634 kWh, improves voltage stability to 0.9828 p.u., and lowers operational costs to $2845 in islanded mode. The findings demonstrate that increasing CHP units enhances system performance, reducing losses from 4280 kWh to 3634 kWh. This study offers valuable insights for policymakers and system operators in optimizing microgrid energy management while balancing efficiency, cost, and reliability. Future work will explore grid integration challenges and advanced control techniques to further optimize microgrid performance.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00507-7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143688426","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":"From balance to breach: cyber threats to battery energy storage systems","authors":"Frans Öhrström,&nbsp;Joakim Oscarsson,&nbsp;Zeeshan Afzal,&nbsp;János Dani,&nbsp;Mikael Asplund","doi":"10.1186/s42162-025-00499-4","DOIUrl":"10.1186/s42162-025-00499-4","url":null,"abstract":"<div><p>Battery energy storage systems are an important part of modern power systems as a solution to maintain grid balance. However, such systems are often remotely managed using cloud-based control systems. This exposes them to cyberattacks that could result in catastrophic consequences for the electrical grid and the connected infrastructure. This paper takes a step towards advancing understanding of these systems and investigates the effects of cyberattacks targeting them. We propose a reference model for an electrical grid cloud-controlled load-balancing system connected to remote battery energy storage systems. The reference model is evaluated from a cybersecurity perspective by implementing and simulating various cyberattacks. The results reveal the system’s attack surface and demonstrate the impact of cyberattacks that can critically threaten the security and stability of the electrical grid.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00499-4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143668261","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":"Soft-switching predictive Type-3 fuzzy control for microgrid energy management","authors":"Walid Ayadi,&nbsp;Jafar Tavoosi,&nbsp;Amirhossein Khosravi Sarvenoee,&nbsp;Ardashir Mohammadzadeh","doi":"10.1186/s42162-025-00508-6","DOIUrl":"10.1186/s42162-025-00508-6","url":null,"abstract":"<div><p>Because each mode has distinct optimization requirements, optimizing the economic performance of microgrids (MGs) in grid-connected and islanded modes presents unique issues. This research offers a novel methodology to overcome this difficulty and better use renewable resources while satisfying the growing needs of the energy market. To maximize the MG’s performance, this approach combines fuzzy control, machine learning, and artificial intelligence with soft switching technologies. Two predictive rules are used in the design of the control system to handle the particular requirements of either the grid-connected or islanded mode depending on the MG’s operational condition. The study’s findings demonstrate that, in addition to lowering energy expenses in large commercial buildings, the suggested strategy optimizes the use of renewable energy sources and storage capacity in a range of network scenarios. This method offers more flexibility in response to network changes and greatly improves energy efficiency.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00508-6","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143655324","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":"Full life-cycle cost model for practical application of solar energy system","authors":"Ang Sha,&nbsp;Zhen Xiong,&nbsp;Xiaolin Zang,&nbsp;Wei Zhao,&nbsp;Ruibang Ge,&nbsp;Wanxiang Yao,&nbsp;Marco Aiello","doi":"10.1186/s42162-025-00505-9","DOIUrl":"10.1186/s42162-025-00505-9","url":null,"abstract":"<div><p>In pursuit of carbon neutrality, a swift transformation is underway in the global energy structure, marked by a consistent rise in the installed capacity of solar energy systems. Meanwhile, the substantial reduction of government subsidies in the solar industry intensifies focus on the economic viability of solar energy installations. In this study, we propose a full life-cycle cost model, named the F-LCC model, for calculating the cost of the solar energy system on the long term, e.g., 20–30 years. This model integrates replacement costs, residual value calculation, interest rate, and inflation impacts while supporting market price estimation for individual components, thereby aiding feasibility analysis in the early project phase. We design an investment cost recovery algorithm based on the F-LCC model to calculate the break-even electricity price for solar energy system. Moreover, we analyze component cost distributions, Net Present Value (NPV), and Discounted Payback Period (DPP) for grid-connected and off-grid solar energy systems with capacities of 10 kWp and 100 kWp in the Chinese market. The results show that the proposed model, compared to other models, captures the fact that payback times are longer. In a solar energy system without storage, solar panels have the highest component cost share at 28.8%. With battery storage, batteries dominate the total cost, reaching up to 74.6%. And the the grid-connected systems DPP ranging from a minimum of 5.5 to a maximum of 7.0 years by grid-connected electricity price, while off-grid systems require at least 19.9 years. The 10 kWp off-grid fixed mounting system’s break-even price being 137.1% higher than its grid-connected counterpart. In addition, tracking-mount systems offer greater cost-reduction potential than fixed installations, with the payback period reduced by 20% for 100 kWp grid-connected systems and 15% for off-grid systems. Finally, we develop a plugin based on the F-LCC model. These findings deepen understanding of solar energy economics and inform policy and investment.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00505-9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143668260","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":"Dual-layer scheduling coordination algorithm for power supply guarantee using multi-objective optimization in spot market environment","authors":"Xuanyuan Wang,&nbsp;Xu Gao,&nbsp;Zhen Ji,&nbsp;Wei Sun,&nbsp;Bo Yan,&nbsp;Bohao Sun","doi":"10.1186/s42162-025-00485-w","DOIUrl":"10.1186/s42162-025-00485-w","url":null,"abstract":"<div><p>As the global electricity market continues to evolve, power dispatch in the spot market environment faces unprecedented challenges. Price fluctuations, the intermittency and uncertainty of renewable energy sources, and stringent environmental constraints make traditional dispatch methods inadequate. To address this, this work proposes a two-layer scheduling strategy based on a multi-objective enhanced genetic algorithm. This strategy aims at balancing multiple objectives such as cost efficiency, environmental impact, and system stability to optimize power dispatch in the spot market. The upper-layer scheduling of this strategy focuses on strategic decisions at the macro level, including generation planning and electricity market transactions. Its lower-layer scheduling concentrates on operational execution at the micro level, specifically power transmission and distribution. To validate the model’s effectiveness, this work designs a regional grid model that includes wind, solar, and several conventional generation units. The experimental results show that, compared to the benchmark strategy, the proposed algorithm achieves a cost savings of 8.33% while ensuring a reliable power supply. Additionally, the algorithm reduces carbon dioxide emissions by approximately 15.1% and significantly increases the average utilization rate of renewable energy to 93.4%. The algorithm is iterated 100 times, each simulating a 24-hour scheduling cycle. The experiment demonstrates its excellent performance in high-dimensional decision spaces and multi-objective optimization problems. This work not only provides an innovative multi-objective optimization solution for power dispatch in the spot market environment but also achieves significant improvements in terms of economic efficiency, environmental sustainability, and long-term viability. Through this two-layer scheduling strategy, the dispatch efficiency of the power system is significantly enhanced, and this provides strong support for the development of a green, low-carbon power supply system.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00485-w","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645557","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":"Selection method for hybrid energy storage schemes for supply reliability improvement in distribution networks","authors":"Qian Li,&nbsp;Yuanbao Zhou,&nbsp;Yunxiao Zhang,&nbsp;Yuxin Fu","doi":"10.1186/s42162-025-00495-8","DOIUrl":"10.1186/s42162-025-00495-8","url":null,"abstract":"<div><p>Hybrid energy storage (HES) plays a crucial role in enhancing the reliability of distribution networks. However, the distinct charging and discharging characteristics among different energy storage technologies pose challenges to the evaluation of HES technical features. This paper focuses on addressing two main issues in HES pre-selection. Firstly, regarding the influence of the number of energy storage types on the utility value in HES pre-selection evaluation, we employ the integrated evaluation method of analytic hierarchy process (AHP)-criteria importance through intercriteria correlation (CRITIC)-technique for order preference by similarity to ideal solution (TOPSIS). We propose an improved utility value calculation method based on enhanced utility combination rules. These rules include distance, replacement, addition, and multiplication rules. By establishing these rules, we can effectively eliminate the impact of the number of energy storage types on the combination result. This enables us to accurately calculate the technical characteristics of HES schemes with varying numbers of energy storage technologies, providing a more reliable basis for scheme comparison and selection. Secondly, to eliminate the interference of utility value improvement processing on evaluation results, we introduce a secondary screening method based on TOPSIS evaluation results. This method mitigates the influence of subjective coefficients by evaluating HES schemes under different subjective coefficients and selecting optimal and sub-optimal schemes. We also establish an evaluation system with 11 indices and 10 energy storage technologies, which can efficiently evaluate up to 1785 HES schemes, significantly expanding the scope of evaluation. Finally, we develop a reliability simulation method for distribution networks based on sequential Monte Carlo. Using the IEEE-33 node as an example, we configure the optimal scheme. The results show that the evaluation process has high reliability. The proposed method not only improves the accuracy and rationality of HES pre-selection but also has important practical significance. In actual power grid operation, it can help decision-makers and utility companies to select the most suitable HES schemes more scientifically. This can effectively improve the reliability of power supply, reduce construction costs, and promote the efficient operation of distribution networks. It provides a valuable reference for the wide-scale application of HES in the power industry.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00495-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143612241","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":"Wind power generation prediction using LSTM model optimized by sparrow search algorithm and firefly algorithm","authors":"Wenjing Zhang,&nbsp;Hongjing Yan,&nbsp;Lili Xiang,&nbsp;Linling Shao","doi":"10.1186/s42162-025-00492-x","DOIUrl":"10.1186/s42162-025-00492-x","url":null,"abstract":"<div><p>As an important renewable energy source, wind power generation is highly stochastic and uncertain due to various environmental factors affecting its output. To raise the accuracy of wind power generation prediction, a bidirectional long short-term memory network combination model based on sparrow search algorithm and firefly algorithm optimization is designed. The model first employs a bidirectional long short-term memory network to capture the long-term dependency features of time series, and uses random forests for nonlinear modeling and feature selection. Then, the sparrow search algorithm and firefly algorithm are combined to optimize the hyperparameter configuration, improving the predictive performance and global search ability of the model. The findings denote that the accuracy of the designed model reaches 98.5%, with a mean square error as low as 0.005 and a prediction time as short as 0.18 s. The simulation analysis results show that the predicted values of the developed model almost coincide with the actual values, with small errors. The research outcomes denote that the optimized model greatly raises the accuracy and efficiency of wind power generation prediction, and has good application prospects.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00492-x","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583611","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":"Real-time monitoring and energy consumption management strategy of cold chain logistics based on the internet of things","authors":"Kang Wang,&nbsp;Ning Du","doi":"10.1186/s42162-025-00493-w","DOIUrl":"10.1186/s42162-025-00493-w","url":null,"abstract":"<div><p>With the rapid development of the cold chain logistics industry, its high energy consumption and low operational efficiency have become increasingly prominent, seriously restricting the sustainable development of the industry. This study focuses on this and proposes a real-time monitoring system for cold chain logistics based on the Internet of Things. It combines the long short-term memory network (LSTM) and the particle swarm optimization (PSO) algorithm to build an energy consumption management strategy. Through the distributed system architecture design, a variety of data transmission protocols are used to ensure real-time and stable data collection and transmission, and to achieve accurate monitoring of key environmental factors in the transportation and storage of cold chain logistics. The experiment was carried out in a simulated cold chain logistics scenario. The data set covers multiple types of sensor data and is compared with multiple baseline models. The results show that compared with the traditional cold chain logistics system, this system significantly improves energy efficiency, reduces energy consumption by about 20%, increases temperature and humidity control accuracy to 94% respectively, improves transportation efficiency, and shortens transportation time by 8.33%. At the same time, the combination of LSTM and PSO algorithms optimizes energy consumption prediction and equipment scheduling, and the equipment group collaborative optimization strategy enhances system stability. This study confirms that the real-time monitoring and energy consumption management strategy based on the Internet of Things can effectively improve the economic and environmental benefits of the cold chain logistics system.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00493-w","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583610","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}