Distributed generation allocation with on-load tap changer on radial distribution networks using adaptive genetic algorithm

Abstract

This paper presents a distribution generation (DG) allocation strategy along with on-load tap changer (OLTC) for radial distribution networks using adaptive genetic algorithm (AGA). The optimal locations for DG units and OLTC, the optimal generation for the DG units, and the OLTC tap ratio are determined by minimizing a weighted objective function comprising of network power loss and maximum bus voltage deviation. A planning model incorporating DG and OLTC models is devised. Three different types of DG units, i.e., DG operating at lagging, leading, and unity power factor are used in the planning. Two new AGA variants based on adaptively varying crossover and mutation probabilities are proposed and used in this work as solution strategies and their performances are compared with some of the existing similar type of AGA variants using the results of multiple runs. The performances of the proposed AGA variants are found to be better in terms of quality of the solutions and consistency. The results also show that the combined operation of DG and OLTC reduces significant amount of power loss and bus voltage deviation. A direct performance comparison with some of the similar approaches shows that the solutions obtained with the proposed approach provide similar amount of power loss reduction and bus voltage improvement with lesser amount of DG penetration level. The approach is demonstrated on a 69-bus test distribution network and a 52-bus Indian practical distribution network.