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

Zobrazit více v PubMed

Shayeghi, H., Shahryari, E., Moradzadeh, M. & Pierluigi Siano A Survey on Microgrid Energy Management Considering Flexible Energy Sources

Sun, G. et al. V2V routing in a VANET based on the Autoregressive Integrated moving average model. DOI

Sun, G. et al. Bus-Trajectory-Based Street-Centric Routing for Message Delivery in Urban Vehicular Ad Hoc Networks. DOI

Rong, Y. et al. Du-Bus: a Realtime Bus Waiting Time Estimation System based on Multi-source Data. DOI

Jamil, F., Iqbal, N., Imran, S., Ahmad & Kim, D. Peer-to-Peer Energy Trading Mechanism Based on Blockchain and Machine Learning for Sustainable Electrical Power Supply in Smart Grid, in

Ma, K., Yang, J. & Liu, P. Relaying-assisted communications for demand response in Smart Grid: cost modeling, game strategies, and algorithms. DOI

Meng, Q. et al. Enhancing distribution system stability and efficiency through multi-power supply startup optimization for new energy integration. DOI

Liu, Y. & Zhao, Y. A blockchain-enabled Framework for Vehicular Data sensing: enhancing information freshness. DOI

Feng, J., Yao, Y. & Liu, Z. Developing an optimal building strategy for electric vehicle charging stations: automaker role. DOI

Fu, Y., Li, C., Yu, F. R., Luan, T. H. & Zhao, P. An incentive mechanism of incorporating Supervision Game for Federated Learning in Autonomous Driving. DOI

Ravindran, M. A. et al. A Novel Technological Review on fast charging infrastructure for Electrical vehicles: challenges, solutions, and future research directions.

Kumar, B. A. et al. A novel strategy towards efficient and reliable electric vehicle charging for the realisation of a true sustainable transportation landscape. PubMed DOI PMC

Kumar, B. A. et al. Hybrid genetic algorithm-simulated annealing based electric vehicle charging station placement for optimizing distribution network resilience. PubMed DOI PMC

Sidharth Sabyasachi, A. R. et al. Reimagining E-mobility: a holistic business model for the electric vehicle charging ecosystem.

Surbhi Aggarwal, A. K., Singh, R. S., Rathore, M., Bajaj, D. & Gupta Revolutionizing load management: a novel technique to diminish the impact of electric vehicle charging stations on the electricity grid.

Kumar, B. A. et al. Enhancing EV charging predictions: a comprehensive analysis using K-nearest neighbours and ensemble stack generalization. DOI

Patnam, B. S. K. & Pindoriya, N. M. Demand response in consumer-centric electricity market: Mathematical models and optimization problems. DOI

Lin, Y. J. et al. Blockchain-based intelligent charging station management system platform. DOI

Wang, Z. et al. AEBIS: AI-enabled Blockchain-based electric vehicle integration system for power management in smart grid platform. DOI

Kakkar, R., Agrawal, S. & Tanwar, S. A systematic survey on demand response management schemes for electric vehicles. DOI

Lin, Y. J. et al. Blockchain power trading and energy management platform. DOI

Yahaya, A. S. et al. Blockchain-based energy trading and load balancing using contract theory and reputation in a smart community. DOI

Li, J., Herdem, M. S., Nathwani, J. & Wen, J. Z. Methods and applications for Artificial intelligence, Big Data, Internet of things, and Blockchain in smart energy management. DOI

Samadi, M., Schriemer, H., Ruj, S. & Erol-Kantarci, M. Energy Blockchain for Demand Response and distributed energy resource management. In:

JAVED, M. U. & AALSALEM, M. Y. SHAFIQ, M., Blockchain-Based Energy Trading and Load Balancing Using Contract Theory and Reputation in a Smart Community.

Kakkar, R., Gupta, R., Agrawal, S., Tanwar, S., Altameem, A., Altameem, T., Raboaca,M. S. Blockchain and iot-driven optimized consensus mechanism for electric vehicle scheduling at charging stations. Sustainability

Kumari, A., Gupta, R., Tanwar, S. & Kumar, N. Blockchain and AI amalgamation for energy cloud management: challenges, solutions, and future directions. DOI

Khan, H. & Masood, T. Impact of Blockchain technology on smart grids. DOI

Miglani, A., Kumar, N., Chamola, V. & Zeadally, S. Blockchain for Internet of Energy management: review, solutions, and challenges. DOI

Hua, W. et al. Applications of Blockchain and Artificial intelligence technologies for enabling prosumers in smart grids: a review. DOI

Junaidi, N., Abdullah, M. P., Alharbi, B. & Shaaban, M. Blockchain-based management of demand response in electric energy grids: a systematic review. DOI

Ma, R. et al. A blockchain-enabled demand management and control framework driven by deep reinforcement learning. DOI

Muthukrishnan, S. K. & Vijayakumar, R.

Teimoori, Z. & Yassine, A. A review on intelligent energy management systems for future electric vehicle transportation. DOI

Kumar, N. M. et al. Distributed energy resources and the application of AI, IoT, and Blockchain in smart grids. DOI

Khan, M. A., Saleh, A. M., Waseem, M. & Sajjad, I. A. Artificial intelligence enabled demand response: prospects and challenges in smart grid environment. DOI

Mololoth, V. K., Saguna, S. & Åhlund, C. Blockchain and machine learning for future smart grids: a review. DOI

Kausar, F., Al-Hamouz, R. & Hussain, S. Energy Demand Forecasting for Electric Vehicles Using Blockchain-Based Federated Learning. DOI

Bhaskar, K. B. R., Prasanth, A. & Saranya, P. An energy-efficient Blockchain approach for secure communication in IoT‐enabled electric vehicles. DOI

Ma, W. et al. New technologies for optimal scheduling of electric vehicles in renewable energy-oriented power systems: a review of deep learning, deep reinforcement learning and Blockchain technology. DOI

Said, D., Elloumi, M. & Khoukhi, L. Cyber-attack on P2P energy transaction between connected electric vehicles: a false data injection detection-based machine learning model. DOI

Yang, P. Electric vehicle based smart cloud model cyber security analysis using fuzzy machine learning with Blockchain technique. DOI

Ahmed, A. A., Alkheir, A. A., Said, D. & Mouftah, H. T. Cooperative spectrum sensing for cognitive radio vehicular ad hoc networks: An overview and open research issues,

Said, D. A Decentralized Electricity Trading Framework (DETF) for Connected EVs: A Blockchain and Machine Learning for Profit Margin Optimization. In:

Said, D. & Elloumi, M. A New False Data Injection Detection Protocol based Machine Learning for P2P Energy Transaction between CEVs.

Said, D. A Survey on Information Communication Technologies in Modern demand-side management for Smart grids: challenges, solutions, and opportunities. In:

Said, D. Intelligent Photovoltaic Power Forecasting Methods for a Sustainable Electricity Market of Smart Micro-Grid, in

Alhija, M. A., Al-Baik, O., Hussein, A. & Abdeljaber, H. Optimizing blockchain for healthcare IoT: a practical guide to navigating scalability, privacy, and efficiency trade-offs. DOI

Nidal Turab, H. A., Owida, Jamal, I. & Al-Nabulsi Harnessing the power of blockchain to strengthen cybersecurity measures: a review,

Ullah, N., Al-Rahmi, W. M., Alblehai, F., Fernando, Y. & Alharbi, Z. H. Rinat Zhanbayev, Ahmad Samed Al-Adwan, and Mohammed Habes. Blockchain-powered grids: Paving the way for a sustainable and efficient future. PubMed PMC

Al-Sarayrah, N., Turab, N. & Hussien, A. A randomized blockchain consensus algorithm for enhancing security in health insurance. DOI

Abu-Rumman, A. et al. Impact of Blockchain Strategy and Information sharing on Digital Operations: empirical evidence from the UAE Banking Industry. In: Cyber Security Impact on Digitalization and Business Intelligence: Big Cyber Security for Information Management: Opportunities and Challenges (475–493). (Springer International Publishing, 2024).

Michael Bryant. (n.d.). Electric vehicle charging dataset. Kaggle. Retrieved November 23. https://www.kaggle.com/datasets/michaelbryantds/electric-vehicle-charging-dataset (2024).

Comprehensive framework for smart residential demand side management with electric vehicle integration and advanced optimization techniques