TY  - EJOU
AU  - Song, Runkai 
AU  - Zhong, Siyu 
AU  - Sun, Guangzeng 
AU  - Li, Zesen 
AU  - Zhou, Ming 
AU  - Wu, Zhaoyuan 

TI  - Risk-Consistent Assessment of Power-System Flexibility Demand under Stochastic Source–Load Trajectories
T2  - Energy Engineering

PY  - 
VL  - 
IS  - 
SN  - 1546-0118

AB  - High penetration of wind and photovoltaic (PV) generation amplifies net-load volatility, making capacity-margin-based reliability indices insufficient to characterize the trajectory-driven flexibility stress caused by fast net-load variations. This paper proposes a stochastic source–load trajectory generation method and a risk-consistent flexibility adequacy assessment framework for multi-scenario operating conditions. For renewables, historical wind and PV capacity factors are mapped into a Gaussian domain via a probit transform and modeled using a jointly fitted seasonal Fourier–AR(1) formulation, from which Monte Carlo sampling generates trajectories that preserve intra-day/seasonal patterns and short-term temporal dependence. For load, stochastic scenarios are constructed by superimposing Gaussian perturbations on the historical profile. Based on the resulting net-load trajectories, flexibility demand is quantified using ramping-rate and mileage-based indicators with explicit upward/downward directions, and adequacy is evaluated under multiple confidence levels by comparing demand quantiles with conservative capability estimates derived from scenario-based economic dispatch. A case study based on real operational datasets shows that flexibility inadequacy escalates markedly at higher confidence levels, and reveals persistent cross-season shortages in downward flexibility driven by minimum-output constraints. In addition, comparison with a pure AR(1) benchmark confirms the necessity of explicitly modeling seasonal structures in renewable scenario generation, and a load-composition-based sensitivity analysis further examines the robustness of the adequacy results under heterogeneous demand structures. These results highlight the necessity of strengthening flexibility resources in future planning and operational preparedness under high renewable penetration.
KW  - Flexibility adequacy; risk-consistent assessment; stochastic source–load trajectories; capacity-factor Gaussianization; seasonal autoregressive modeling

DO  - 10.32604/ee.2026.078600