<p>The increasing demand for electricity and the aging infrastructure of power distribution systems have raised significant concerns about future system reliability. Failures in distribution systems, closely linked to system usage and environmental factors, are the primary contributors to electricity service interruptions. The integration of distributed energy resources (DER) presents an opportunity to enhance system reliability through optimized operations. This paper proposes a novel approach that explicitly incorporates both decision- and context-dependent reliability into the optimization of control setpoints for DERs in active distribution systems. The proposed model captures how operational decisions and ambient temperature impact the likelihood of component failures, enabling a balanced approach to cost efficiency and reliability. By leveraging a logistic function model for component failure rates and employing a sequential convex programming method, the model addresses the challenges of non-convex optimization under decision-dependent uncertainty. Numerical case study on a modified IEEE 33-bus test system demonstrates the effectiveness of the model in dynamically adjusting power flows and enhancing system robustness under varying environmental conditions and operational loads. The results highlight the potential of DERs to contribute to distribution system reliability by efficiently managing power flows and responding to fluctuating energy demands, achieving an 22.6% reduction in cost from expected energy not served.</p>