Robust planning with multiple timescale dynamic adaptability for the green hydrogen ammonia synthesis system

System planning that reconciles intermittent renewable supply with continuous process demand is critical for ensuring the economic viability and operational reliability of green hydrogen ammonia synthesis (GHAS) systems. However, conventional planning methodologies exhibit significant limitations, including temporal discontinuity in scenario generation and inadequate industrial applicability for GHAS deployment. To this end, this paper introduces a progressive growing generative adversarial network (PGGAN)-based scenario generation framework operating at monthly resolution, which preserves meteorological event continuity while enhancing spatiotemporal detail for ammonia synthesis process simulation. Concurrently, an adaptive time-granularity robust planning model grounded in Information-Gap Decision Theory (IGDT) is developed, dynamically adjusting temporal resolution with coarse granularity during stable renewable power output phases and fine granularity during high-variability intervals. This enables effective accommodation of renewable intermittency while mitigating power oscillations in synthesis loops and catalytic degradation phenomena. Extensive simulations demonstrate that PGGAN-generated scenarios achieve a Kolmogorov-Smirnov statistic of 0.023, representing a 56.4% improvement over Wasserstein GAN-Gradient Penalty method. The planning model maintains only a 2.81% error with benchmarks model, but the number of variables is reduced by 68%.