Optimizing demand response and load balancing in smart EV charging networks using AI integrated blockchain framework

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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Publication Details

Journal
Scientific Reports
Published
2024-12-30
DOI
https://doi.org/10.1038/s41598-024-82257-2
Citations
61
Primary Topic
Electric Vehicles and Infrastructure
Type
article
Field-Weighted Citation Impact
12.73

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article

Optimizing demand response and load balancing in smart EV charging networks using AI integrated blockchain framework

Faisal Alsaif, K. Reddy Madhavi, Євген Зайцев, Arvind Singh et al.
61 citations
Scientific Reports
Electric Vehicles and Infrastructure
12.73
article

Optimizing demand response and load balancing in smart EV charging networks using AI integrated blockchain framework

Faisal Alsaif, K. Reddy Madhavi, Євген Зайцев, Arvind Singh, R. Seshu Kumar, Mohit Bajaj
article en
61 citations

Abstract

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.

Scientific ReportsVol. 14(1)
Al-Ahliyya Amman University (JO), Vignan's Foundation for Science, Technology & Research (IN), National Academy of Sciences of Ukraine (UA), Lingnan Normal University (CN), King Saud University (SA), Institute of Electrodynamics (UA), University of Business and Technology (SA), Graphic Era University (IN)
King Saud University
Industry, innovation and infrastructure
Openalex Percentile: Top 1%
Electric Vehicles and Infrastructure
12.73
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