A hybrid teaching–learning-based optimization technique for optimal DG sizing and placement in radial distribution systems

Distributed generation (DG) technology has proved to be an efficient and economical way of generation of power. DGs are intended to generate power near the load centers. Optimal allocation of DG resources enhances the overall performance of distribution systems. This paper presents a hybrid teaching–learning-based optimization (HTLBO) technique for the optimal allocation of DGs in distribution systems. The proposed technique is proficient in handling continuous as well as discrete variables and has the capability to escape strong local minima/maxima trappings. The validity and effectiveness of HTLBO are tested on well-defined standard mathematical benchmark functions. The proposed method is further implemented for optimal allocation of DGs in the IEEE 33-bus, 69-bus and 118-bus radial distribution test systems for minimization of power losses, voltage deviation and maximization of voltage stability index. The multi-objective function for DG allocation uses the ɛ-constraints approach. The obtained results reveal improved convergence characteristics over both teaching–learning-based optimization and quasi-oppositional teaching–learning-based optimization.