This thesis creates open-source Python scripts for conducting generation adequacy analysis studies. First, scripts without wind considerations are constructed, and then the scripts have been modified to include wind considerations. Generation and load models have been implemented to obtain the reliability (adequacy) indices Loss of Load Expectation (LOLE) and Expected Energy Not Served (EENS). Two probabilistic methods have been implemented: an analytical method and a non-sequential Monte Carlo Simulation (MCS) method. Both methods use the IEEE load curve for their load model, but different generation models are used. The analytical model uses a recursive algorithm, where generation units are added one by one to obtain a Capacity Outage Probability Table (COPT). The non-sequential MCS method uses state sampling, where a uniform random number is generated for each hourly increment and a state is selected based on the state probabilities of a generator. Wind considerations have been added to the scripts by modelling the power output curve of a wind turbine. The proposed scripts have been tested on two test systems - the Roy Billinton Test System and the IEEE Reliability Test System. The resulting reliability metrics have been compared with benchmark values in the literature and found to be closely matching. Furthermore, methodological clarity on how to obtain the presented scripts is given. Even though all elements of the implemented methods are present in the literature, not all are clearly explained or combined in one place. This work aims to overcome this limitation.