An Accelerated Long-Term Congestion Assessment Method for Power Systems With High-Proportional Renewable Energy and Energy Storage Systems

Congestion analysis is critical for power system analysis and operation, but the integration of renewable energy sources (RESs) and energy storage systems (ESSs) will bring great uncertainty and computational challenges. In this study, we propose an accelerated simulation method for long-term congestion assessment in power systems characterized by a high proportion of RESs and ESSs. Our approach includes an affine adjustable robust congestion assessment model capable of identifying congestion in each sampling. The model utilizes automatic generation control to address uncertainties associated with RESs and incorporates multi-period coupling constraints for components with chronological characteristics. Additionally, the Cross-Entropy-Latin Hypercube Sampling (CE-LHS) algorithm is employed to expedite convergence during sampling generation in sequential Monte Carlo Simulation (SMCS). Numerical results from several test systems demonstrate the effectiveness and computational enhancements achieved by the proposed technique.