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

被引:7
作者
Singh, Arvind R. [1 ]
Kumar, R. Seshu [2 ]
Madhavi, K. Reddy [3 ]
Alsaif, Faisal [4 ]
Bajaj, Mohit [5 ,6 ,7 ]
Zaitsev, Ievgen [8 ,9 ]
机构
[1] Hanjiang Normal Univ, Sch Phys & Elect Engn, Shiyan, Peoples R China
[2] Deemed Univ, Vignans Fdn Sci Technol & Res, Dept EEE, Guntur 522213, Andhra Prades, India
[3] Mohan Babu Univ, Dept Artificial Intelligence & Machine Learning, Tirupati 517102, India
[4] King Saud Univ, Coll Engn, Dept Elect Engn, Riyadh 11421, Saudi Arabia
[5] Graph Era Deemed Univ, Dept Elect Engn, Dehra Dun 248002, India
[6] AL Ahliyya Amman Univ, Hourani Ctr Appl Sci Res, Amman, Jordan
[7] Univ Business & Technol, Coll Engn, Jeddah 21448, Saudi Arabia
[8] Natl Acad Sci Ukraine, Inst Electrodynam, Dept Theoret Elect Engn & Diagnost Elect Equipment, Beresteyskiy 56, Kyiv, Ukraine
[9] Natl Acad Sci Ukraine, Ctr Informat Analyt & Tech Support Nucl Power Faci, Akad Palladina Ave 34-A, Kyiv, Ukraine
来源
SCIENTIFIC REPORTS | 2024年 / 14卷 / 01期
关键词
Blockchain; Artificial intelligence; Demand response; EV charging stations; Load balancing; ELECTRIC VEHICLES; MANAGEMENT;
D O I
10.1038/s41598-024-82257-2
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
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页数:22
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