Dynamic Tariff Adjustment for Electric Vehicle Charging in Renewable-Rich Smart Grids: A Multi-Factor Optimization Approach to Load Balancing and Cost Efficiency

Dawei Wang, Xi Chen, Xiulan Liu, Yongda Li, Zhengguo Piao () and Haoxuan Li
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Dawei Wang: State Grid Beijing Electric Power Research Institute, Beijing 100075, China
Xi Chen: State Grid Beijing Electric Power Research Institute, Beijing 100075, China
Xiulan Liu: State Grid Beijing Electric Power Research Institute, Beijing 100075, China
Yongda Li: State Grid Beijing Electric Power Research Institute, Beijing 100075, China
Zhengguo Piao: School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China
Haoxuan Li: School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China

Energies, 2025, vol. 18, issue 16, 1-29

Abstract: The widespread deployment of electric vehicles (EVs) has introduced substantial challenges to electricity pricing, grid stability, and renewable energy integration. This paper proposes a real-time pricing optimization framework for large-scale EV charging networks incorporating renewable intermittency, demand elasticity, and infrastructure constraints within a high-dimensional optimization model. The core objective is to dynamically determine spatiotemporal electricity prices that simultaneously reduce system peak load, improve renewable energy utilization, and minimize user charging costs. A rigorous mathematical formulation is developed integrating over 40 system-level constraints, including power balance, transmission capacity, 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 charging station utilization, elasticity response curves, and the marginal cost of renewable and grid-supplied electricity. The problem is solved over 96 time intervals using a hybrid solution approach, with benchmark comparisons against mixed-integer programming (MILP) and deep reinforcement learning (DRL)-based baselines. A comprehensive case study is conducted on a 500-station EV charging network serving 10,000 vehicles integrated with a modified IEEE 118-bus grid model and 800 MW of variable renewable energy. Historical charging data with ±12% stochastic demand variation and real-world solar and wind profiles are used to simulate realistic operational conditions. Results demonstrate that the proposed framework achieves a 23.4% average peak load reduction per station, a 17.9% improvement in renewable energy utilization, and user cost savings of up to 30% compared to baseline flat-rate pricing. Utilization imbalances across the network are reduced, with congestion mitigation observed at over 90% of high-traffic stations. The real-time pricing model successfully aligns low-price windows with high-renewable periods and off-peak hours, achieving time-synchronized load shifting and system-wide flexibility. Visual analytics including high-resolution 3D surface plots and disaggregated bar charts reveal structured patterns in demand–price interactions, confirming the model’s ability to generate smooth, non-disruptive pricing trajectories. The results underscore the viability of advanced optimization-based pricing strategies for scalable, clean, and responsive EV charging infrastructure management 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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