Optimal Allocation of Distributed Energy Resources Using Improved Meta-heuristic Techniques

Abstract

The optimal allocation of distributed energy resources is one of the most important and challenging task toward realizing smart grid objectives. Smart grid initiatives may be realized after obtaining integrated solutions of distributed energy resources while taking into account the realistic operational strategy of distribution network reconfiguration. This article addresses improved variants of three meta-heuristic techniques—the improved genetic algorithm, improved particle swarm optimization, and improved teaching–learning-based optimization—to efficiently handle the problem of simultaneous allocation of distributed energy resources, such as shunt capacitors and distributed generators in radial distribution networks. The problem is formulated to maximize annual energy loss reduction and to maintain a better node voltage profile while considering network reconfiguration under a variable load scenario. Several algorithm specific modifications are suggested in the standard forms of genetic algorithm, particle swarm optimization, and teaching–learning-based optimization to overcome their intrinsic flaws. In addition, an intelligent search algorithm is proposed to further enhance the performance of optimizing techniques. The proposed methods are investigated on the benchmark IEEE 33-bus test distribution system, and a comparative analysis is carried out to judge the suitability of the proposed techniques. The application results obtained are promising when compared with other established methods.