Quantum-Causal Optimization for Decentralized EV Charging Management in Smart Grids

The rapid adoption of electric vehicles (EVs) poses significant challenges to smart grids, including grid instability, inequitable resource allocation, and inefficiency in real-time scheduling under high renewable energy penetration. To address these limitations, this article proposes a quantum-causal adaptive optimization framework. First, a quantum-driven hierarchical Q-learning framework is designed to optimize the local scheduling and global coordination of charging stations (CSs), thereby improving decision-making efficiency. Second, a multiobjective optimization based on dynamic weights is developed, which adjusts the weights of the objective function in real-time to flexibly respond to environmental factors such as system load changes and charging demand fluctuations to achieve load balance and fairness. Finally, a causal quantum variable strategy is proposed to enhance adaptability and global optimality by identifying state variables with direct causal influence on decision outcomes. Experimental results demonstrate lower peak-to-valley ratio, improved load distribution balance, and faster convergence compared to conventional methods. The proposed framework provides a scalable and highly fair solution for large-scale EV comanagement in highly volatile grids, enhancing operational reliability and promoting the deep integration of quantum intelligence with energy systems.