Employing Quantum Entanglement for Real-Time Coordination of Distributed Electric Vehicle Charging Stations: Advancing Grid Efficiency and Stability

Dawei Wang, Hanqi Dai, Yuan Jin, Zhuoqun Li, Shanna Luo () and Xuebin Li
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Dawei Wang: State Grid Beijing Electric Power Research Institute, Beijing 100075, China
Hanqi Dai: State Grid Beijing Electric Power Research Institute, Beijing 100075, China
Yuan Jin: State Grid Beijing Electric Power Research Institute, Beijing 100075, China
Zhuoqun Li: State Grid Beijing Electric Power Research Institute, Beijing 100075, China
Shanna Luo: School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China
Xuebin Li: School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China

Energies, 2025, vol. 18, issue 11, 1-27

Abstract: The widespread deployment of electric vehicles (EVs) has introduced substantial challenges to electricity pricing, grid stability, and renewable energy integration. This paper presents the first real-time quantum-enhanced electricity pricing framework for large-scale EV charging networks, marking a significant departure from existing approaches based on mixed-integer programming (MILP) and deep reinforcement learning (DRL). The proposed framework incorporates renewable intermittency, demand elasticity, and infrastructure constraints within a high-dimensional optimization model. The objective is to dynamically determine spatiotemporal electricity prices that reduce system peak load, improve renewable utilization, and minimize user charging costs. A rigorous mathematical formulation is developed, integrating over 40 system-level constraints, including power balance, transmission limits, renewable curtailment, carbon targets, voltage regulation, demand-side flexibility, social participation, and cyber-resilience. Real-time electricity prices are treated as dynamic decision variables influenced by station utilization, elasticity response curves, and the marginal cost of renewable and grid electricity. The model is solved across 96 time intervals using a quantum-classical hybrid method, with benchmark comparisons against MILP and DRL baselines. A comprehensive case study is conducted on a 500-station EV network serving 10,000 vehicles, coupled with a modified IEEE 118-bus grid and 800 MW of variable renewable energy. Historical charging data with ±12% stochastic demand variation and real-world solar/wind profiles are used to simulate realistic conditions. Results show that the proposed framework achieves a 23.4% average peak load reduction per station, a 17.9% gain in renewable utilization, and up to 30% user cost savings compared to flat-rate pricing. Network congestion is mitigated at over 90% of high-traffic stations. Pricing trajectories align low-price windows with high-renewable periods and off-peak hours, enabling synchronized load shifting and enhanced flexibility. Visual analytics using 3D surface plots and disaggregated bar charts confirm structured demand-price interactions and smooth, stable price evolution. These findings validate the potential of quantum-enhanced optimization for scalable, clean, and adaptive EV charging coordination in renewable-rich grid environments.

Keywords: real-time pricing optimization; electric vehicle charging; renewable energy integration; demand-side management; grid congestion mitigation; elasticity-aware modeling; spatiotemporal energy scheduling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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