Long-term planning in electric power systems requires simulations of unit commitment (UC) and economic dispatch (ED) for long time periods up to 20 years. Such simulations are conducted with production cost models (PCMs), which involves solving large-scale mixed-integer programming (MIP) problems with a high number of variables and constraints, because of the long planning horizon. We develop new optimization modeling and solution techniques based on a decomposition scheme to reduce solution time and improve accuracy in PCM. We propose a temporal decomposition method that solves the UC problem by systematically decoupling the long-horizon MIP problem into several sub-horizon models. The decomposition is obtained by the Lagrangian relaxation of the time-coupling UC constraints such as ramping constraints and minimum uptime/downtime constraints. The key challenge to this decomposition approach is to solve several sub-MIP problems while effectively searching for dual variables i n order to accelerate the convergence of the algorithm. We implement the temporal decomposition in the parallel decomposition framework DSP, which can solve the multiple subproblems in parallel on high-performance computing clusters. We also implement the branch-and-bound method on top of the decomposition in order to recover primal feasible solutions and find a primal optimal solution. Numerical results of the decomposition method are reported for IEEE 118-bus system with up to 168-hour time horizon.