Optimizing demand response and load balancing in smart EV charging networks using AI integrated blockchain framework
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 increa...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-82257-2 |
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| author | Arvind R. Singh R. Seshu Kumar K. Reddy Madhavi Faisal Alsaif Mohit Bajaj Ievgen Zaitsev |
| author_facet | Arvind R. Singh R. Seshu Kumar K. Reddy Madhavi Faisal Alsaif Mohit Bajaj Ievgen Zaitsev |
| author_sort | Arvind R. Singh |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b74569d0283841c280affd77f44c5cb3 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-b74569d0283841c280affd77f44c5cb32025-01-05T12:28:00ZengNature PortfolioScientific Reports2045-23222024-12-0114112210.1038/s41598-024-82257-2Optimizing demand response and load balancing in smart EV charging networks using AI integrated blockchain frameworkArvind R. Singh0R. Seshu Kumar1K. Reddy Madhavi2Faisal Alsaif3Mohit Bajaj4Ievgen Zaitsev5School of Physics and Electronic Engineering, Hanjiang Normal UniversityDepartment of EEE, Vignan’s Foundation for Science Technology and Research (Deemed to be University)Department of Artificial Intelligence and Machine Learning, Mohan Babu UniversityDepartment of Electrical Engineering, College of Engineering, King Saud UniversityDepartment of Electrical Engineering, Graphic Era (Deemed to be University)Department of Theoretical Electrical Engineering and Diagnostics of Electrical Equipment, Institute of Electrodynamics, National Academy of Sciences of UkraineAbstract 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.https://doi.org/10.1038/s41598-024-82257-2BlockchainArtificial intelligenceDemand responseEV charging stationsLoad balancing |
| spellingShingle | Arvind R. Singh R. Seshu Kumar K. Reddy Madhavi Faisal Alsaif Mohit Bajaj Ievgen Zaitsev Optimizing demand response and load balancing in smart EV charging networks using AI integrated blockchain framework Scientific Reports Blockchain Artificial intelligence Demand response EV charging stations Load balancing |
| title | Optimizing demand response and load balancing in smart EV charging networks using AI integrated blockchain framework |
| title_full | Optimizing demand response and load balancing in smart EV charging networks using AI integrated blockchain framework |
| title_fullStr | Optimizing demand response and load balancing in smart EV charging networks using AI integrated blockchain framework |
| title_full_unstemmed | Optimizing demand response and load balancing in smart EV charging networks using AI integrated blockchain framework |
| title_short | Optimizing demand response and load balancing in smart EV charging networks using AI integrated blockchain framework |
| title_sort | optimizing demand response and load balancing in smart ev charging networks using ai integrated blockchain framework |
| topic | Blockchain Artificial intelligence Demand response EV charging stations Load balancing |
| url | https://doi.org/10.1038/s41598-024-82257-2 |
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