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Growing global concerns over reducing carbon emissions in the electricity market have accelerated the integration of renewable energy sources and electric vehicles, increasing variabilities and uncertainties across various nodes of power networks. System planners and operators recognize the importance of probabilistic bulk system reliability assessment methods capable of capturing the real-time behavior of components in the emerging systems. In this regard, the paper proposes a methodology for conducting bulk system reliability assessments of power system networks characterized by variable supply and demand profiles at different bulk power points. This paper implements a nodal negative load modeling method to integrate wind power generation in reliability assessment, capturing the cross-correlation between demand–supply variabilities at any node of the network. The multi-state load model employs the load cut-off strategy to reduce the number of demand scenarios, enhancing the computational efficiency. Moreover, the multi-state wind modeling approach considers the penetration levels, ensuring the impact of increasing penetration is appropriately captured. The methodology determines a list of a reduced set of scenarios for which consequence assessment needs to be conducted. The proposed framework and methods can readily be applied by power utilities, as these methods can be incorporated into most commercial software that uses an analytical approach for CSR assessment. The methodology is illustrated using the Roy Billinton Test System (RBTS) and can be effectively applied to other networks. Full article