The growing adoption of behind-the-meter distributed energy resources (DERs) such as rooftop photovoltaic (PV) systems and energy storage (ES) has transformed end users from passive consumers into active participants within modern distribution networks

thereby introducing new complexities in reliability assessment. This paper proposes a bottom-up

probabilistic framework that systematically integrates residential DER adoption into predictive reliability analyses. The framework models PV and ES adoption using joint probability distributions at the customer level

incorporates component reliability

and employs an adaptive Monte Carlo simulation to estimate not only mean values of reliability indices but also their distributional variability across customers. The approach is demonstrated on the RBTS Bus 4 system under sixteen joint adoption scenarios. Results show that high PV-storage adoption can reduce system-average SAIFI and SAIDI by more than 90% compared to the baseline

while shallow adoption may worsen interruption frequency for some users. These findings highlight the importance of capturing adoption heterogeneity and distributional outcomes in reliability assessment

providing utilities and regulators with a robust tool for planning in DER-rich distribution grids.