Energy Informatics最新文献

{"title":"A novel framework for optimizing residential load response planning with consideration of user satisfaction","authors":"Mohammad Hossein Erfani Majd,&nbsp;Gholam-Reza Kamyab,&nbsp;Saeed Balochian","doi":"10.1186/s42162-025-00504-w","DOIUrl":"10.1186/s42162-025-00504-w","url":null,"abstract":"<div><p>This study presents an optimization framework for residential energy management that integrates photovoltaic (PV) systems, battery storage, and demand response strategies. The primary objective is to minimize electricity costs while ensuring efficient use of renewable energy resources. The proposed method utilizes the Meerkat Optimization Algorithm (MOA), which is compared against other optimization algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Teaching-Learning-Based Optimization (TLBO). The results show that the proposed MOA achieves significant cost reductions. For example, under Time-of-Use (TOU) tariffs, the total electricity cost is reduced by 14% compared to the base case, while under Real-Time Pricing (RTP), the reduction is 16%. The optimized system also yields a 5 kW PV system and a 10 kWh battery, compared to 3 kW PV and 6 kWh battery in the GA and PSO cases. Additionally, the MOA provides a more computationally efficient solution, with a calculation time of 73 s, compared to 91 s for GA and 102 s for PSO. This study demonstrates the effectiveness of the MOA in optimizing residential energy systems, providing a robust solution for reducing electricity costs while integrating renewable energy sources. The approach is generalizable to other energy management applications and can be adapted for various regions and household configurations.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00504-w","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809136","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 metric study of distributed PV consumption capacity considering multiple uncertainties","authors":"Chengmin Wang,&nbsp;Yangzi Wang,&nbsp;Fulong Song","doi":"10.1186/s42162-025-00501-z","DOIUrl":"10.1186/s42162-025-00501-z","url":null,"abstract":"<div><p>With the transformation of the energy structure, distributed photovoltaic (PV) power generation has become increasingly important. However, due to uncertain factors such as weather, equipment, and load demand, the consumption problem is prominent, which restricts the healthy development of the system. It is important to accurately measure the absorptive capacity of distributed PVs, but there are many shortcomings in existing research methods. This paper proposes a comprehensive measurement method to solve this problem and thus conducts a comprehensive metric study of the distributed PV Consumption Capacity considering multiple uncertainties. Based on the output uncertainty and load uncertainty of the distributed PV power generation, a mathematical model of the distributed PV power generation uncertainty is constructed. Based on the distributed PV operation data under various uncertain factors, considering the PV capacity and active power loss connected to the distribution network as objective functions, and setting constraints such as power balance, node voltage, line power flow, and distributed PV output, a comprehensive measurement model of the distributed PV absorption capacity is constructed. A local chaotic search is introduced to improve the firefly algorithm, and the improved firefly algorithm is used to solve the comprehensive measurement model and output the comprehensive measurement results of the absorption capacity. The experimental results show that this method can effectively evaluate the absorptive capacity. In a typical IEEE 32 - node distribution network, the network loss is 30 kW when PV access reaches 534 kW. This method is better than other methods in terms of maximum absorptive capacity, annual PV absorption, and annual network loss, and provides a scientific basis for the planning, operation, and management of distributed PV systems.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00501-z","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143801219","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":"An equivalent modeling method for integrated water-wind-solar systems based on sparrow search algorithm","authors":"Yuanhong Lu,&nbsp;Jie Zhang,&nbsp;Jingyue Zhang,&nbsp;Libin Huang,&nbsp;Haiping Guo,&nbsp;Binjiang Hu,&nbsp;Tianyu Guo","doi":"10.1186/s42162-025-00512-w","DOIUrl":"10.1186/s42162-025-00512-w","url":null,"abstract":"<div><p>In the context of extensive integration of renewable energy sources into the electrical grid, the grid's fault transient behaviors have undergone significant changes. However, conventional single-unit equivalent models fail to accurately capture the fault transient responses of combined wind-solar-hydro power stations and often require substantial computational resources, leading to reduced simulation efficiency. This study proposes a cluster-based equivalent modeling approach for hybrid wind-solar-hydro power plants using the Sparrow Search Algorithm. Key factors influencing fault characteristics, including the distance to the Point of Common Coupling, DC-side current-limiting measures, irradiance levels, water flow rates, wind speeds, and reactive power at the outlet, are identified and used to construct a transient model. Euclidean distances are computed for these factors, and initial clustering centers for wind turbines are determined using an improved max–min distance technique. These factors and clustering centers serve as the training dataset to establish the clustering equivalent model. Simulation results, conducted on the MATLAB2022 platform, demonstrate that the SSA-based model outperforms the single-unit equivalent model by over 150 times in terms of accuracy. Additionally, the SSA-based model achieves a delay time, defined as the time required to compute the system's transient response after a fault, of less than 5 ms, which is less than one-twentieth of the delay time of the single-unit equivalent model. These improvements highlight the model's ability to accurately capture dynamic power responses under various disturbances, making it highly suitable for real-time applications in hybrid renewable energy systems.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00512-w","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143793161","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":"Rolling optimization method of virtual power plant demand response based on Bayesian Stackelberg game","authors":"Binxi Huang","doi":"10.1186/s42162-025-00500-0","DOIUrl":"10.1186/s42162-025-00500-0","url":null,"abstract":"<div><p>To optimize the interaction effect between internal units and demand response of virtual power plants and enhance their transaction profit, a study on the the rolling optimization method of demand response for virtual power plants based on Bayesian Stackelberg game is conducted. Following the construction of a virtual power plant model and analysis of its operation strategy and process content, this method employs a power demand forecasting approach based on multidimensional fusion and Bayesian probability update to forecast the demand-side power requirements within the jurisdiction of the virtual power plant. Utilizing the forecast results of dynamic electricity demand, a demand response elastic matrix for virtual power plant is constructed through a rolling optimization model based on Stackelberg game. The two optimization objective functions, maximizing the supply-side income and minimizing the demand-side electricity purchase cost of virtual power plant, are transformed into maximizing the profit of power transaction for the virtual power plant. This is iteratively solved using the whale algorithm to determine the optimal power generation distribution scheme for each unit on both the supply side and demand sides. Upon testing, this method demonstrates not only the capability for peak shaving and valley filling but also improves the operating profit of the virtual power plant and optimizes user satisfaction, resulting in a relatively high comprehensive benefit index.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00500-0","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761737","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":"Hybrid Intelligence-driven decision making for green energy technology innovation in manufacturing enterprises","authors":"Huiqi Zhang,&nbsp;Qiansha Zhang","doi":"10.1186/s42162-025-00511-x","DOIUrl":"10.1186/s42162-025-00511-x","url":null,"abstract":"<div><p>In the context of today’s global environmental challenges, manufacturing enterprises are gradually taking green technology innovation as a strategy to enhance sustainable development ability. This study discusses the application of hybrid intelligent technology in promoting green technology innovation decision-making in manufacturing enterprises. Through data preprocessing and model construction, it is found that energy consumption, emissions, standard compliance, environmental quality and market response are closely related to the green technology innovation score of enterprises. The results show that efficient energy management and active compliance with environmental standards have a significant impact on improving the environmental performance and technological innovation of enterprises. The market’s positive response to green technology has significantly promoted the rapid development and application of the technology. This study not only provides manufacturing enterprises with strategy and decision support for green technology innovation, but also provides policy makers with insights for promoting sustainable development. Through in-depth analysis, this paper emphasizes the importance and effectiveness of comprehensive application of hybrid intelligent technology in the process of green technology promotion.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00511-x","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143749039","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":"Low-carbon economic optimization for flexible DC distribution networks based on the hiking optimization algorithm","authors":"Ke Wu,&nbsp;Yuefa Guo,&nbsp;Ke Wang,&nbsp;Zhenliang Chen","doi":"10.1186/s42162-025-00486-9","DOIUrl":"10.1186/s42162-025-00486-9","url":null,"abstract":"<div><p>The integration of large-scale renewable energy into the grid has significantly advanced research on flexible DC distribution networks. However, the potential of flexible loads—possessing both source and load characteristics—in supporting the low-carbon economic operation of integrated energy systems (IES) remains underexplored. Furthermore, the optimization of IES scheduling is inherently a multi-dimensional nonlinear problem, where traditional intelligent optimization methods struggle to achieve satisfactory solution accuracy. In this paper, an IES model is developed based on the concept of an energy hub, incorporating elements such as wind turbine output, photovoltaics, energy storage systems, gas turbines, and flexible loads, while considering the transferability, interruptible nature, and reverse energy flow characteristics of demand-side flexible loads. To address the current challenges in balancing environmental and economic benefits in IES, a carbon trading strategy and demand response mechanisms are applied to the optimization scheduling process, with the objective of achieving low-carbon and low-cost operations. The proposed model is solved using a novel Hiking Optimization Algorithm (HOA), and comparative analysis across different scenarios is conducted to investigate the impact of the carbon trading strategy on low-carbon operation, alongside an evaluation of the system’s economic and environmental performance under reasonable scheduling of both the carbon trading strategy and flexible loads. The results indicate that the total cost and carbon emissions of the system decreased by 8.98% and 15.13%, respectively, indicating that appropriate scheduling of the carbon trading mechanism and flexible loads effectively improves the system’s economic and environmental performance. In addition, a comparative study with traditional particle swarm and genetic algorithms demonstrates that the HOA, by incorporating adaptive mechanisms for both search space resolution and speed adjustment, enhances both global exploration and local exploitation, effectively avoiding local optima traps. This leads to improved optimization accuracy, further validating its effectiveness in IES optimization.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00486-9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716711","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 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}