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.

Center for Information Analytical and Technical Support of Nuclear Power Facilities Monitoring National Academy of Sciences of Ukraine Akademika Palladina Avenue 34 A Kyiv Ukraine

College of Engineering University of Business and Technology Jeddah 21448 Saudi Arabia

Department of Artificial Intelligence and Machine Learning Mohan Babu University Tirupati 517102 India

Department of EEE Vignan's Foundation for Science Technology and Research Guntur Andhra Pradesh 522213 India

Department of Electrical Engineering College of Engineering King Saud University Riyadh 11421 Saudi Arabia

Department of Electrical Engineering Graphic Era Dehradun 248002 India

Department of Theoretical Electrical Engineering and Diagnostics of Electrical Equipment Institute of Electrodynamics National Academy of Sciences of Ukraine Beresteyskiy 56 Kyiv 57 03680 Kyiv Ukraine

Hourani Center for Applied Scientific Research Al Ahliyya Amman University Amman Jordan

School of Physics and Electronic Engineering Hanjiang Normal University Shiyan P R China

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