Mixed integer Programming Approaches for Distributed Generation Planning

In this paper recently developed mixed-integer programming (MIP) tools to the problem of optimal siting and sizing of distributed generators in a distribution network. Here three methodologies for solving the DGPP via mixed-integer linear programming (MILP) and mixed-integer nonlinear programming (MINLP) approaches are compared. The single MILP approach uses the well-known DC linear approximation model. One MINLP approach uses the full nonlinear AC power flow model, while another MINLP model is similar to the DC model albeit with quadratic line loss terms. A genetic algorithm (GA) is used as a baseline of comparison for these approaches. A simple knapsack problem method involving generator selection is presented for determining lower bounds on the optimal design objective. Solution methods are outlined, and computational results show that the MIP methods, while lacking the speed of the genetic Algorithm can find improved solutions within conservative time requirements and provide useful information on optimality.