###### 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). uncertainty modeling techniques; risk assessment; renewable energy source (RES); power system reliability; wind farms [1][1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36]  
to predicting outcomes and assessing risks in complex systems. Additionally, possibilistic frameworks, such as fuzzy logic and Dempster-Shafer theory, are explored for their ability to handle imprecise data, offering flexibility in systems with significant uncertainty or data scarcity. Further, the paper evaluates key analytical techniques such as Monte Carlo simulation, Latin Hypercube Sampling, and point estimation methods (PEMs). These are assessed for their effectiveness in different contexts, including environmental risk management and power system optimization. Case studies demonstrate their practical application and highlight where each method offers the most value. Advanced methodologies, such as robust optimization (RO) and information-gap decision theory (IGDT), are also examined for their growing importance in managing uncertainty in the energy and finance sectors. By integrating a range of methodologies, probabilistic, possibilistic, and decision theory-based, the paper advocates for a holistic, cross-disciplinary approach to risk management. This integrated perspective not only strengthens theoretical models but also ensures their applicability in real-world power system operations. The paper concludes by discussing the policy and regulatory implications of these models, emphasizing the need for ongoing research and collaboration to refine risk management strategies. Ultimately, this review aims to equip practitioners and policymakers with the tools and knowledge to develop power systems that are not only resilient and reliable but also adaptable to the uncertainties inherent in modern energy landscapes. The early interest in applying probability methods to evaluate generating capacity requirements emerged in 1933 [1]. A pivotal moment occurred at the American Institute of Electrical Engineers conference in 1947, where the application of probability theory to account for generation unit reliability was introduced. Throughout the 1950s and into the 1960s, the concept of the loss of load probability (LOLP) was further expanded. Notable contributions from engineers like [PERSON] and [PERSON] helped establish a consensus on acceptable reliability criteria.