Enhancing Smart Grid Reliability Through Data-Driven Optimisation and Cyber-Resilient EV Integration
Yazarlar (6)
Durham University, İngiltere
Newcastle University, İngiltere
Dr. Öğr. Üyesi Mahmut SARI
Kırşehir Ahi Evran Üniversitesi, Türkiye
Duzce University, Türkiye
University of Birmingham, İngiltere
Newcastle University, İngiltere
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
|
||
| Dergi Adı | ENERGIES (Q3) | ||
| Dergi ISSN | 1996-1073 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 08-2025 |
| Cilt / Sayı / Sayfa | 18 / 17 / – | DOI | 10.3390/en18174510 |
| Makale Linki | https://doi.org/10.3390/en18174510 | ||
| Özet |
| This study presents a novel cyber-resilient, data-driven optimisation framework for real-time energy management in electric vehicle (EV)-integrated smart grids. The proposed framework integrates a hybrid optimisation engine--combining genetic algorithms and reinforcement learning--with a real-time analytics module to enable adaptive scheduling under uncertainty. It accounts for dynamic electricity pricing, EV mobility patterns, and grid load fluctuations, dynamically reallocating charging demand in response to evolving grid conditions. Unlike existing GA/RL schedulers, this framework uniquely integrates adaptive optimisation with resilient forecasting under incomplete data and lightweight blockchain-inspired cyber-defence, thereby addressing efficiency, accuracy, and security simultaneously. To ensure secure and trustworthy EV--grid communication, a lightweight blockchain-inspired protocol is incorporated, supported by an intrusion detection system (IDS) for cyber-attack mitigation. Empirical evaluation using European smart grid datasets demonstrates a daily peak demand reduction of 9.6% (from 33 kWh to 29.8 kWh), with a 27% decrease in energy delivered at the original peak hour and a redistribution of demand that increases delivery at 19:00 h by nearly 25%. Station utilisation became more balanced, with weekly peak normalised utilisation falling from 1.0 to 0.7. The forecasting module achieved a mean absolute error (MAE) of 0.25 kWh and a mean absolute percentage error (MAPE) below 20% even with up to 25% missing data. Among tested models, CatBoost outperformed LightGBM and XGBoost with an RMSE of 0.853 kWh and ... |
| Anahtar Kelimeler |
| electric vehicles | smart grid | energy management | cyber-resilience | load forecasting | optimisation | intrusion detection system | blockchain-inspired security |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| WoS | 2 |
| Google Scholar | 3 |