State-Triggered Adaptive Microgrid Dispatch for EV Charging: A Hybrid Deep Learning and Multi-Objective Optimization Framework

The rapid growth of electric vehicle (EV) charging demand requires accurate short-term load forecasts and dispatch strategies that can respond to changing microgrid operating conditions. This study proposes a hybrid framework that combines a VMD-CNN-ABiLSTM-IGCRA forecasting model with a state-triggered adaptive scheduling strategy. Variational Mode Decomposition extracts multi-scale components from volatile charging profiles; the CNN and attention-based bidirectional LSTM learn local and long-range temporal features; and the Improved Giant Cane Rat Algorithm regulates the iterative search step and tunes the principal model hyperparameters. The scheduling layer constructs a five-dimensional state vector from electricity price, renewable-energy volatility, predicted EV load, grid carbon pressure, and available battery capacity. Historical thresholds activate four candidate operating cases: carbon-priority, price-driven, fluctuation-stabilized, and economic–environmental balanced scheduling. When several cases are triggered, the best feasible candidate is selected according to the current dispatch objective under common equipment and power-balance constraints. Three confidential real-world charging datasets from Shenzhen, each containing 744 hourly observations over 31 days, are evaluated using a chronological 70% training and 30% testing split. The proposed forecasting model obtains RMSE values of 1.7719–5.1042, MAE values of 0.8963–3.7562, and R2 values of 85.67–95.09% across the three datasets. In the station-level dispatch simulations, the balanced Case 4 reduces total cost relative to Case 1 by 16.95%, 54.53%, and 31.99% for Charging Stations 1–3, respectively. These results support the operational value of linking load forecasting with transparent state-dependent mode selection.