How Multi-Period Dynamic Electricity Pricing Reshapes Power Supply Chain Risk and Green Performance: A Study of Electric Vehicle Charging Strategy Optimization

Background:
Against the backdrop of global energy transition and tightening environmental policies, electric vehicles (EVs) have emerged as a key decarbonization solution in the transportation sector. However, their rapid adoption introduces significant risks to the power supply chain, including grid overload, voltage instability, and operational resilience challenges, particularly during peak demand periods. Furthermore, the misalignment between EV charging behavior and renewable energy availability exacerbates system vulnerabilities and undermines environmental goals. Existing research lacks an integrated approach that addresses both risk mitigation and resilience enhancement under multi-period dynamic pricing, with limited empirical validation of the underlying mechanisms such as spatiotemporal charging transfer and subsidy effectiveness.

Aim:
This study aims to develop an optimal orderly charging model for EVs incorporating multi-period dynamic electricity pricing, with the dual objectives of enhancing power supply chain resilience by minimizing load risk and maximizing renewable energy absorption. It further seeks to empirically validate the role of spatiotemporal charging transfer as a core mechanism for risk mitigation and evaluate how subsidy policies can strengthen user participation and system adaptability.

Method:
We construct a mixed-integer linear programming model that integrates spatiotemporal dynamic pricing, grid safety constraints, and renewable energy matching. The model is solved using YALMIP with Gurobi. An empirical analysis is conducted using real-world data, including dynamic pricing, grid load profiles, renewable generation, and EV charging behavior. Regression models and mediation analysis are employed to examine the effects of price signals and subsidies on charging behavior and system risk resilience.

Results:
Simulation results show that the model significantly enhances grid resilience by reducing load variance and mitigating overload risks, while improving renewable energy absorption compared to unmanaged charging. Empirical evidence confirms that dynamic pricing effectively drives the spatiotemporal transfer of charging loads, which in turn mediates risk reduction and supports grid stability. Subsidies are shown to substantially increase user participation, thereby amplifying the model's risk mitigation effects and strengthening the adaptive capacity of the power supply chain.

Potential Contribution:
This study provides a risk-informed decision-making framework for governments and power companies to design dynamic pricing and targeted subsidy policies that enhance power system resilience. By empirically validating the mechanisms behind charging behavior adaptation, it offers actionable insights into how user-side management can contribute to a more robust, stable, and low-carbon power supply chain, supporting sustainable energy-transport integration in the face of growing EV penetration.