## 1 Introduction  
A net-zero emission target in power systems will usher in increased uncertainties and variabilities in the supply and demand of power at the various nodes of the power network, which will adversely impact supply reliability. Uncertainties in power generation stem primarily from increasing penetrations of renewable energy sources, such as wind power technologies. The integration of multiple wind farms in different geographic areas, characterized by distinct wind patterns, results in variable power generation at the different buses within the network. While the effect of uncertainty and variability in wind power generation can be minimal at lower levels of penetration, increasing penetration will have a remarkable impact; necessitating detailed study and analysis for evaluating the reliability of wind-integrated power systems (WIPS) [1]. The cross-correlation in power variations among different wind farms at the different geographical locations can significantly impact overall generation fluctuation and should, therefore, be an important consideration in reliability evaluations [2]. Furthermore, the expected integration of electric vehicles and the introduction of various demand response programs by electric power utilities alter the load variation profiles at the different bulk power points, adding demand uncertainties. Existing bulk system reliability evaluation methods and available software tools consider conventional load models obtained from bimodal diurnal load curves and assume the variations at the different buses to be fully correlated. This assumption will no longer hold true as power systems transform towards a net-zero emission scenario. With the diverse nodal load profiles, it becomes necessary to develop an appropriate load model capable of capturing the uncertainties in the demand variations within a node and across multiple nodes. The bulk system consists of the generation and the transmission facilities and falls within the control jurisdiction of the independent system operator responsible for grid reliability. The ability of the bulk system to provide supply continuity at the bulk load points is often termed composite system reliability (CSR). The rising variabilities and uncertainties in supply and demand dictates a probabilistic assessment of CSR which requires the portrayal of the stochastic behavior of the system through proper mathematical modeling of the system components and their characteristics. Although reliability assessments are routinely done in a wide range of engineering applications, such as the railway system [3], this paper is focused on the reliability assessment of emerging power systems. Most CSR tools incorporate random failures of generating units using a two-state Markov model. Such models need further advancement to represent intermittent and variable power generation from renewable energy sources. Multi-state generation and the negative load modeling approach are two well-known modeling techniques to integrate wind power generation in reliability assessments [4]. The multi-state generation approach extracts a suitable discrete probability distribution of capacity states from historical time-series data. A considerable amount of work has been done in implementing the multi-state approach [5; 6; 7]. Although the accuracy of assessment increases with the number of capacity states in the multi-state model, the computation time increases substantially and becomes a major concern. It is stated in [8] that a five-state wind power model can provide a reasonable trade-off between the computation time and accuracy. This finding, however, should be critically analyzed in a low-carbon or a net-zero emission scenario where renewable resources dominate the energy supply. The variable nature of electricity demand in the evolving power system is another important aspect that needs to be incorporated in the reliability study. Most CSR studies use a fixed load model represented by the system peak load due to its computational simplicity.