An immune-based optimization method for distributed generation placement in order to minimize power losses

The optimal sizing and placement of distributed generators has received considerable attention from researchers recently. An Immune Algorithm (IA) based optimization approach for solving the distributed generation (DG) placement problem is proposed in this paper. In the distributed generation placement problem, practical DG operating constraints including: load profiles, feeder capacities and allowable voltage limits are all considered while the investment cost, power or energy losses and voltage profile are optimized. In the proposed method, objective function (power losses) and constraints (bus voltage limits and line current limits) are represented as antigens. Through the genetic evolution, an antibody that most fits the antigen becomes the solution. In this IA computation, an affinity calculation process is also embedded to guarantee the diversity. The process stagnation can thus be prevented better.