Solution methods and computational efficiency of stochastic linear programs have been extensively studied over the years. Despite this, their practical applications remain limited due to persistent computational challenges and the lack of a user-friendly interface for modeling and solving these problems. This thesis revisits those computational challenges and explores the usability of stochastic linear programs in the field of energy system modeling. This is done by implementing two solution methods for solving two-stage stochastic linear programs: the Extensive Form method and the L-Shaped method, and applying both methods on existing input problems. To additionally test the performance on an energy system model, the existing large-scale energy model Oemof-B3 is extended to a stochastic linear program, and the solution methods are applied on this problem too. Since the stochastic linear programming framework can be easily applied, and both methods successfully produce solutions for various input problems, we conclude stochastic linear programming holds significant potential as a modeling tool. However, computational challenges remain, particularly when applying the stochastic programming framework to the energy model Oemof-B3. Additionally, the lack of user-friendly tools and readily available solvers for solving stochastic linear programs limits the practical applicability of these methods.