Edge-intelligent electric vehicle charging coordination for grid load balancing and renewable integration
Yazarlar (2)
Dr. Öğr. Üyesi Abdulkadir GÖZÜOĞLU Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Doç. Dr. Zafer DOĞAN Tokat Gaziosmanpaşa Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS (Q1) | ||
| Dergi ISSN | 1386-7857 Dergi Bilgileri (2026) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 06-2026 |
| Kabul Tarihi | 21-04-2026 | Yayınlanma Tarihi | 29-06-2026 |
| Cilt / Sayı / Sayfa | 29 / 7 / 27– | DOI | 10.1007/s10586-026-06174-x |
| Makale Linki | https://doi.org/10.1007/s10586-026-06174-x | ||
| UAK Araştırma Alanları |
Akıllı Elektrik Şebekeleri
Elektrik Enerjisi ve Güç Sistemleri
Yapay Zeka
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| Özet |
| The rising adoption of electric vehicles (EVs) presents significant challenges for grid balancing and renewable integration. Traditional cloud-based EV charging systems suffer from latency and limited adaptability. This study introduces an edge-intelligent framework leveraging IoT-enabled sensing and on-site AI inference to coordinate real-time EV charging and optimize grid performance. Lightweight models (CNN+ LSTM, XGBoost, Random Forest) are deployed on Jetson Orin Nano and Raspberry Pi 5, enabling behavior-aware scheduling and dynamic pricing at the edge. Trained on a US Department of Energy dataset, the system achieves a 27.6% improvement in station utilization, 24.5% reduction in peak load, and 29.8% cost savings while maintaining high prediction accuracy (R2> 0.92). Communication overhead is reduced by 80%, supporting faster, decentralized decision-making. The proposed framework offers a scalable, practical solution for intelligent EV charging in sustainable smart grids. |
| Anahtar Kelimeler |
| Electric vehicle (EV) charging coordination | Edge computing in smart grids | Behavior-aware load management | Distributed energy optimization | Renewable energy integration in EV charging | Real-time scheduling in power systems |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Google Scholar | 3 |