Carbon management in massive electric vehicle temporal and spatial scheduling with automotive electronic forensics.

Electric vehicles (EVs) provide an environmentally friendly solution to reduce reliance on fossil fuels. However, increasing EV adoption and distributed solar generation pose new challenges for power systems, such as increased load variability and forecasting complexity. High penetration levels of EVs and rooftop photovoltaics introduce operational challenges that traditional power systems were not designed to handle. This study proposes a novel two-layer optimization model to address these issues by coordinating the scheduling of EVs, generators, and solar power. The upper-layer optimization focuses on coordinating EV charging and discharging schedules with thermal generators and base load in the transmission grid, considering solar power availability. The lower-layer optimization addresses spatial scheduling of EV loads in the distribution grid. To evaluate the proposed strategy, simulations were conducted on a benchmark system comprising an 8-unit transmission network interconnected with an IEEE 33-bus distribution feeder. The results demonstrate that the proposed two-layer optimization model reduces total operational costs by 22.8% compared to the ACM-PSO model, while achieving a 4.3% improvement in peak-valley difference reduction, effectively enhancing load balancing. These results highlight the economic and operational advantages of the proposed strategy, particularly in addressing challenges like peak load management and network efficiency. Additionally, the model integrates solar power efficiently and incorporates location-specific forensic data collection from EVs, providing valuable insights for distribution network planning and operation. These findings emphasize the importance of optimizing EV load placement and scheduling to improve grid performance and support sustainable energy adoption.