Medium- to long-term wind power output scenarios are crucial for power system planning and operational simulations. This paper proposes a two-stage hidden Markov model-based approach for modeling the time series output of multiple wind farms. First, based on the key features of the wind power output sequence, the daily typical patterns of wind power output are extracted. Then, the process of simulating the wind power output time-series is modeled as a two-layer temporal model. The upper layer uses a discrete hidden Markov model to describe the day-to-day transition process of wind power output patterns and the lower layer uses a Gaussian mixture hidden Markov model to describe the fluctuation process of wind power output values within each output pattern. Finally, the upper models corresponding to each quarter and the lower models corresponding to each pattern are trained respectively and the time-series scenarios of wind power output for multiple wind farms are generated quarter-by-quarter and day-by-day through Monte Carlo sampling. Validation using real-world wind power data demonstrates that the proposed method can effectively generate medium- to long-term output scenarios for multiple wind farms. Compared to traditional methods, the proposed method shows improvements in terms of accuracy, statistical characteristics, temporal correlation, and mutual correlation.

Rapidly expanding power demand in economically developing regions significantly amplifies the operational risks associated with the delayed commissioning of planned substations. This study proposes a data–physics fusion framework integrating analytic hierarchy process-based quantitative assessment with multi-voltage level grid evolution simulation. First, a novel set of evaluation indicators for assessing planned substation criticality, with weights determined through the analytic hierarchy process (AHP), was established, enabling rapid assessment of delay impacts on investments and identification of crucial substations. This approach addresses the fundamental limitation of traditional planning methodologies, which inadequately quantify the compound effects of substation commissioning delays on multi-voltage grid evolution and associated investment inefficiencies. Subsequently, a multi-voltage level grid evolution model was developed, which quantitatively measures the cascading effects of substation commissioning delays on both low-voltage grid development and multi-level grid construction investments. Case study validation demonstrated a strong linear correlation between the proposed substation importance scores and the incremental construction costs induced by delays. The simulation-driven impact assessment model exhibits superior accuracy in evaluating commissioning delay consequences on multi-voltage grid construction compared to conventional approaches. This research provides power grid planners with a robust decision support framework for optimizing substation construction scheduling and minimizing delay-related cost escalations in complex grid development scenarios.