This thesis addresses the optimization of a long-term energy planning model that integrates generator maintenance scheduling and generator failure. A two-stage stochastic model was proposed to minimize the total planning costs. Generator failure scenarios were generated and reduced using k-means clustering. A solution algorithm based on the L-shaped method was applied to decompose and solve the problem. First-stage decisions include investment decisions and maintenance schedules. The second stage handles the operational decisions. The model was tested on modified IEEE 9-bus and Garver 6-bus systems over a 10-year horizon. Results show that the two-stage stochastic program (TSSP) outperformed the simple stochastic program (SSP) by handling uncertainties more effectively, achieving a 5.14 million EUR reduction in total planning costs. Though the TSSP required more computational time, it outperformed in sensitivity analyses. While scalability remained a challenge, the model effectively identified critical investment locations in smaller systems.     «

This thesis addresses the optimization of a long-term energy planning model that integrates generator maintenance scheduling and generator failure. A two-stage stochastic model was proposed to minimize the total planning costs. Generator failure scenarios were generated and reduced using k-means clustering. A solution algorithm based on the L-shaped method was applied to decompose and solve the problem. First-stage decisions include investment decisions and maintenance schedules. The second stag...     »