The integration of Electric Vehicles (EVs) into power grids introduces several critical challenges, such as limited scalability, inefficiencies in real-time demand management, and significant data privacy and security vulnerabilities within centralized architectures. Furthermore, the increasing demand for decentralized systems necessitates robust solutions to handle the growing volume of EVs while ensuring grid stability and optimizing energy utilization. To address these challenges, this paper presents the Demand Response and Load Balancing using Artificial intelligence (DR-LB-AI) framework. The proposed framework leverages Artificial intelligence (AI) for predictive demand forecasting and dynamic load distribution, enabling real-time optimization of EV charging infrastructure. Furthermore, Blockchain technology is employed to facilitate decentralized, secure communication, ensuring tamper-proof energy transactions while enhancing transparency and trust among stakeholders. The DR-LB-AI framework significantly enhances energy distribution efficiency, reducing grid overload during peak periods by 20%. Through advanced demand forecasting and autonomous load adjustments, the system improves grid stability and optimizes overall energy utilization. Blockchain integration further strengthens security and privacy, delivering a 97.71% improvement in data protection via its decentralized framework. Additionally, the system achieves a 98.43% scalability improvement, effectively managing the growing volume of EVs, and boosts transparency and trust by 96.24% through the use of immutable transaction records. Overall, the findings demonstrate that DR-LB-AI not only mitigates peak demand stress but also accelerates response times for Load Balancing, contributing to a more resilient, scalable, and sustainable EV charging infrastructure. These advancements are critical to the long-term viability of smart grids and the continued expansion of electric mobility.
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http://dx.doi.org/10.1038/s41598-024-82257-2 | DOI Listing |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11685539 | PMC |
Sci Rep
January 2025
Electrical Engineering Department, Kerman Branch, Islamic Azad University, Kerman, Iran.
In this paper, a robust fuzzy multi-objective framework is performed to optimize the dispersed and hybrid renewable photovoltaic-wind energy resources in a radial distribution network considering uncertainties of renewable generation and network demand. A novel multi-objective improved gradient-based optimizer (MOIGBO) enhanced with Rosenbrock's direct rotational technique to overcome premature convergence is proposed to determine the problem optimal decision variables. The deterministic optimization framework without uncertainty minimizes active energy loss, unmet customer energy, and renewable generation costs.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Biological and Medical Sciences, Faculty of Physical Education and Sport, Comenius University in Bratislava, Bratislava, Slovakia.
Background: Although a lot of attention is paid to the flaws of balance training research in older adults, the low methodological quality and incomplete reporting of studies still limit the knowledge transfer between research and practice. These known shortcomings are considered also as barriers for creating recommendations for balance training in older adults. Despite the considerable efforts to improve the scientific quality of studies, such recommendations have not yet been formulated to date.
View Article and Find Full Text PDFNoncontact injuries are prevalent among professional football players. Yet, most research on this topic is retrospective, focusing solely on statistical correlations between Global Positioning System (GPS) metrics and injury occurrence, overlooking the multifactorial nature of injuries. This study introduces an automated injury identification and prediction approach using machine learning, leveraging GPS data and player-specific parameters.
View Article and Find Full Text PDFInt J Sports Physiol Perform
December 2024
Department of Physical Education and Sport, Faculty of Sport Sciences, University of Granada, Granada, Spain.
Purpose: Although previous studies have compared strength-training adaptations between free weights (FW) and machine-guided exercises, those studies did not use a Smith machine (SM), which most closely replicates the exercises performed with FW. Thus, the aim of the present study was to investigate the chronic effects of strength-focused, velocity-based training regimens using FW versus SM.
Methods: Thirty-seven sport-science students (14 female) were assigned, balanced by sex and relative strength, to either an FW or SM training group.
Sci Rep
December 2024
Department of Computer Science, College of Science, Northern Border University, 73213, Arar, Saudi Arabia.
The best layout design related to the sensor node distribution represents one among the major research questions in Wireless Sensor Networks (WSNs). It has a direct impact on WSNs' cost, detection capabilities, and monitoring quality. The optimization of several conflicting objectives, including as load balancing, coverage, cost, lifetime, connection, and energy consumption of sensor nodes, is necessary for layout optimization.
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