A Comprehensive Review on Uncertainty and Risk Modeling Techniques and Their Applications in Power Systems

Abstract

The increasing integration of renewable energy sources (RESs) into power systems has introduced new complexities due to the inherent variability and uncertainty of these energy sources. In addition to the uncertainty in RES generation, the demand-side load of power systems is also subject to fluctuations, further complicating system operations. Addressing these challenges requires effective modeling and assessment techniques to quantify and mitigate the risks associated with system uncertainties. This paper evaluates the impact of various uncertainty modeling techniques on power system reliability with wind farm integration. Furthermore, this paper reviews the state of the art of the various uncertainty and risk modeling techniques in power systems. Through a detailed case study, the performance of these techniques in modeling uncertainties of wind speeds is analyzed. Based on the results, the integration of wind turbines improves the system’s overall reliability when there is a reduction in conventional power plants (CPPs)’ generation, which are dispatchable energy sources providing a stable and flexible supply. However, the generation of wind farms is associated with uncertainty. The results show Monte Carlo simulation combined with the K-Means method is consistently a more accurate uncertainty model for wind speeds, closely aligning with real-case scenarios, compared to other methods such as Markov Chain Monte Carlo (MCMC), robust optimization (RO), and information-gap decision theory (IGDT).