ArticleScientific reports2024
Optimizing demand response and load balancing in smart EV charging networks using AI integrated blockchain framework.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Sustainable EV adoption with clustering and predictive modelling for optimal charging infrastructure in the West Midlands and North East UK.Scientific reports · 2026Article
- Integrated energy scheduling for grid-connected microgrids using battery degradation-aware optimization and coordinated control strategies.Scientific reports · 2025Article
- Lightweight machine learning framework using temporal features for electric vehicle demand response forecasting on edge devices.Scientific reports · 2025Article
- An optimization method for integrated demand response strategies for electricity and heat considering the uncertainty of user-side loads.Scientific reports · 2025Article
- A scalable cloud-integrated AI platform for real-time optimization of EV charging and resilient microgrid energy management.Scientific reports · 2025Article
- Comprehensive framework for smart residential demand side management with electric vehicle integration and advanced optimization techniques.Scientific reports · 2025Article
- Optimal scheduling of solar powered EV charging stations in a radial distribution system using opposition-based competitive swarm optimization.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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