Scenario Forecasting of Wind-Solar-Load and Ramping Demand Assessment Based on Key Meteorological-State Labels Under Limited Explicit Weather Data

In the absence of explicit meteorological observations, the correlation among wind power, photovoltaic output, and load is difficult to characterize directly using conventional weather-driven models, which further increases the difficulty of net-load ramping assessment. To address this issue, this paper proposes a weak meteorological coupling method for wind-solar-load scenario generation and ramping demand assessment based on key meteorological-state labels. First, the load series is decomposed on multiple time scales to extract its trend, periodic components, and random residuals. Then, high-wind and low-wind states are identified on the wind side through continuous weather-process blocks, while PV scenarios are generated based on low-solar states and their overlap probabilities with wind states. Meanwhile, load scenarios are generated through seasonally conditioned residual sampling. In this way, wind-solar-load joint modeling is achieved without relying on explicit meteorological variables. Finally, net load is constructed from the generated joint scenarios to evaluate upward and downward ramping demand as well as extreme ramping risk. Case study results show that the proposed method can preserve the deterministic structure of load while reasonably characterizing the uncertainty of wind and PV output. At the system level, the prediction intervals of both upward and downward ramping demands achieve high coverage rates, indicating that the proposed method can stably reflect the range of bidirectional regulation requirements. Further analysis shows that extreme ramping events are mainly caused by the synchronous rapid drop of wind and PV output during critical periods rather than by abrupt load growth alone. The proposed method can provide useful support for ramping demand assessment of renewable power systems under limited meteorological information.