Low-carbon economic dispatch for microgrid-integrated charging stations: A cost-oriented bi-layer optimization framework

Driven by the low-carbon economy imperative, charging stations (CSs) integrated with renewable energy microgrids (MGs) have gained significant attention as critical infrastructure for advancing transportation electrification. However, the integration combines their inherent uncertainties, leading to suboptimal operational performance. To address this challenge, a cost-oriented bi-layer dispatch framework is developed by incorporating proximal policy optimization (PPO) into a model predictive control (MPC) foundation. This framework simultaneously optimizes the microgrid-integrated charging stations' (MGCSs) low-carbon economic operating costs and the charging fulfillment of electric vehicles (EVs). The proposed framework bypasses the explicit prediction of uncertainties inherent in the traditional “predict-then-optimize” framework and reduces MPC's reliance on precise parameter settings. Additionally, a power allocation strategy based on a cooperative game model (CGM) is established, which ensures fair charging among EVs through dynamic urgency indicators and enables a closed-loop optimization for maximizing charging fulfillment through the aggregated urgency feedback. Simulations using real-world EV data demonstrate the effectiveness of the proposed framework, outperforming various MPC-based benchmarks.