ABSTRACT: This thesis proposes the application of biogeography-based optimization (BBO) to solve both economic dispatch (ED) and unit commitment (UC) problems of power systems operation. Biogeography is the study of the geographical distribution of biological organisms. Mathematical models of biogeography describe how species migrate from one habitat to another, how species arise, and how species become extinct. BBO has features in common with other biology-based optimization methods, such as genetic algorithms (GAs) and particle swarm optimization (PSO). Hence BBO is applicable to many of the same types of problems that GAs and PSO are used for. The proposed method has a simple procedure to find a near optimal solution for the non-smooth and non-convex problems mainly through two steps: migration and mutation. Economic dispatch (ED) main objective is to minimize the generation cost while satisfying the load demand and the system constraints. There are many constraints in the economic dispatch problem such as generators capacity, ramp rate limit, and prohibited operation zones. Unit commitment (UC) problem is mainly finding the minimum cost schedule to a set of generators by turning each one either on or off over a given time horizon to meet the demand load and satisfy different operational constraints. The cost function consists of the operation cost, startup, and shut down cost. There are many constraints in unit commitment problem such as spinning reserve, minimum up/down ,crew, must run and fuel constraints. Economic dispatch is used in Unit commitment to find the minimum cost of generated power from the committed units for a certain load demand at a certain period in the schedule. UC problem is a nonlinear, large scale, mixed integer combinatorial optimization method with constrains. In the study, BBO will be applied to solve the economic dispatch and unit commitment problems of non-smooth and non-convex objectives. Results will be compared with other heuristic methods presented in literature.