Carbon-Aware DRL–MPC Framework for Real-Time Control and Multiobjective Optimization of RES-Based V2G Charging
International Journal of Energy Research, cilt.2026, sa.1, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 2026 Sayı: 1
- Basım Tarihi: 2026
- Doi Numarası: 10.1155/er/6509079
- Dergi Adı: International Journal of Energy Research
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Compendex, Environment Index, INSPEC, Directory of Open Access Journals, Academic Search Ultimate (EBSCO), Natural Science Collection (ProQuest), Earth, Atmospheric, & Aquatic Science Collection (ProQuest), Engineering Source (EBSCO), Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest)
- Anahtar Kelimeler: carbon-aware control, deep reinforcement learning, electric vehicle, model predictive control, renewable energy, vehicle-to-grid
- Atatürk Üniversitesi Adresli: Evet
Özet
The integration of renewable energy sources with electric vehicle (EV) charging infrastructures introduces new challenges and opportunities for modern power systems. High EV penetration leads to increased demand variability, which can compromise grid stability and elevate operational costs. Vehicle-to-grid (V2G) technology provides a mechanism for bidirectional energy flow, enabling EVs to not only consume electricity but also supply it back to the grid during peak periods. This paper presents a carbon-aware real-time control framework that integrates multiobjective optimization, deep reinforcement learning (DRL), and model predictive control (MPC) for renewable-energy-supported EV charging systems. The framework simultaneously minimizes operational costs, reduces carbon emissions, balances grid load, and adapts in real-time to variations in renewable generation, EV demand, electricity prices, and carbon pricing. Extensive simulation studies demonstrate superior performance compared to baseline strategies, highlighting improvements in cost efficiency, emission reduction, and grid load management.