## 1 Introduction
Many reliability software tools, therefore, implement analytical methods. Several studies have explored analytical methods in generation adequacy studies [12; 13; 14] to incorporate the cross-correlation between wind power variations at the different geographic locations. Very little work can be found to address this issue  
cross-correlations, analyzing how these parameters would impact bulk-system reliability, developing a computational framework that uses new supply/demand models, and using the developed computational methods and tools to conduct CSR assessments. The likelihood of load curtailments and voltage violations are determined through power flow studies of all possible contingencies of each operating condition of the power system in a CSR assessment. The computation time can be exhaustive if all the \(2^{n}\) contingencies are to be considered in a typical-sized power system with \(n\) components. Considerable efforts have been made in the past [16; 17; 18] to reduce the number of contingencies by selecting those that have a significant impact on reliability indices, while eliminating others with comparatively less impact. Available software tools often implement contingency selection and reduction methods, such as the wind chime method [18], to reduce computational time. Moreover, most studies consider only one operating condition with the annual peak load scenario to limit the computation time. An accurate CSR assessment should, however, consider multiple load scenarios and various wind-generation scenarios. As system variability and uncertainties increase in modern power systems, the number of operating scenarios expands, leading to an exponential rise in the number contingencies. This escalation in the number of scenarios makes the analysis computationally exhaustive and unmanageable, which points to a major research gap in identifying the optimum set of evaluation scenarios without ignoring the scenarios that potentially influence the evaluated reliability indices. This paper attempts to address this issue in two ways; first, it converts a significant portion of wind generation into negative load, thereby reducing the multi-wind-generation scenarios, and second, it determines the cut-off load point and eliminates the net load scenarios beyond the cut-off value which do not contribute to reliability events. This paper addresses the challenges of incorporating the increasing variabilities in the generation and demand in the CSR assessment of power systems that are rapidly transiting towards a net-zero environment. The paper proposes an analytical multi-state renewable generation and load variation models and develop a novel CSR framework that can easily be embedded in existing CSR software tools. The paper proposes a nodal negative load model to capture the cross-correlation between the load and wind generation at each node. The residual wind generation model captures line congestion contingencies arising from wind power fluctuations in the CSR assessment. The reduced penetration of the residual wind generation also reduces the wind-generation scenarios to lower the computational burden. The novel load modeling method presented in this paper determines a cut-off load value to filter out the demand scenarios which do not contribute to the CSR indices, thereby further improving computational efficiency.