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. Note to Practitioners—This paper presents an accelerated method for long-term congestion assessment in power systems with high-proportional RESs and ESSs. A robust optimization model with multi-period coupling constraints is proposed to establish operational boundaries for long-term congestion analyzes. Building on SMCS, the CE-LHS algorithm is integrated to improve convergence performance. CE produces the optimal parameter distribution of the system, while LHS effectively mitigates the truncation effect associated with CE. The application future of this method is not limited to power systems, it can also be utilized in other industrial systems characterized by significant uncertainty, such as energy systems, structural facilities, and automated control systems.