A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering

Thesis (M.Eng.) - Asian Institute of Technology

Integrating Genetic Algorithms, Tahu Search and Simulated Annealing for Multi-stage Transmission Expansion Planning. The penetration of "least cost planning " concepts into electric utilities has given rise the necessity of developing comprehensive optimization models for utility planning. More widely, it focuses individually on Generation, Transmission and Distribution planning. Practically, transmission expansion planning problem is a hard, large-scale combinatorial problem with dynamic nature. In recent past several research work has been done using various techniques for both static and dynamic network planning. However, the emerging need for a robust software model is still contemporary. This thesis proposes an integrated approach of Genetic Algorithms, Tahu Search and Simulated Annealing for multi-stage (dynamic) transmission network expansion planning. First, dynamic network expansion planning problem is modeled with the above three nonconvex optimization approaches individually. Second, the efficiency and robustness of each approach is tested with the modified Garver 's 6-bus network. Third, the most interesting f eatures of each of the three approaches is combined to develop the integrated algorithm and its validity is tested with the modified IEEE-14 network. Finally, a real-world application (Sri Lanka transmission network) of the integrated algorithm is presented for multi-stage transmission expansion planning along with optimal capacitor placement study.