Interaction strategy for grid-hydrogen-vehicle system in extreme heat scenarios: Hybrid system dynamics and Bilevel optimization approach

Amidst the accelerated global energy transition, the hydrogen energy system (HES) is booming, with hydrogen fuel cell vehicles (HFCVs) increasingly influencing the transportation sector. The grid‑hydrogen-vehicle (GHV) system, as a multi-energy supply framework, holds great significance for optimizing urban energy structures. However, frequent extreme climate events severely threaten the stability of distribution networks, making energy security a pressing issue. This study focuses on the power supply preservation challenges of distribution networks under extreme heat scenarios and proposes an interaction strategy for the GHV system combining system dynamics and spatiotemporal bilevel optimization. System dynamics is employed to construct causal loop and stock-flow diagrams, elucidating the dynamic relationships and interaction mechanisms among power flow, hydrogen flow, and benefit flow within the grid operator, HES operator, and HFCV users. The spatiotemporal bilevel optimization model optimizes the interaction of the grid, HES, and HFCV over time to balance power supply and demand, while spatially optimizing hydrogen production, transportation, storage, and utilization in conjunction with vehicle travel behavior for efficient energy allocation. Case studies using IEEE 33-node distribution networks with a 30-node transportation network and IEEE 123-node networks with the actual transportation network of Xi'an verify the effectiveness and applicability of the proposed method across different scales. This approach effectively ensures power supply, reduces load shedding, and enhances benefits for all entities under extreme heat conditions, offering new insights and methods for addressing energy supply issues in extreme heat scenarios. ¹ Keywords. Grid‑hydrogen-vehicle system, System dynamics, Spatiotemporal bilevel optimization, Extreme heat scenarios, Power supply preservation, Vehicle-to-grid.