The work introduces a new methodology to solve multiobjective DER accommodation problem of distribution systems by combining a technique for order of preference by similarity to ideal solution (TOPSIS) and an improved elephant herding optimization (EHO) technique. A complex real-life multiobjective distributed energy resources (DERs) planning problem is formulated and solved using the proposed method. The aim is to determine the optimal sites and sizes of DERs to maximize the overall benefits of utility and consumers. The proposed technique is productively implemented on three small to large-scale benchmark test distribution systems of 33-bus, 118-bus and 880-bus. The optimal solutions obtained are compared with the methods available in the literature. The comparison shows that the proposed optimization method is promising.

This study presents a new multi‐objective Taguchi approach for optimal integration of distributed generations (DGs) in small and large scale distribution networks. The Taguchi method (TM) is a statistical method and employs orthogonal arrays (OAs) to estimate the output response in less number of computations. In every cycle, OA is updated according to mean response of each parameter at its respective levels in the previous cycle. A new node priority list is proposed to guide TM to select promising nodes. For multi‐objective problems, a trade‐off is developed between various objectives using the technique for order of preference by similarity to ideal solution that reduces Euclidean distances of various objectives from their best solutions and increases Euclidean distances from their worst solutions. A multi‐objective DG integration problem is formulated to demonstrate the applicability of the proposed approach and tested on IEEE 33‐bus, 118‐bus and a practical 201‐bus radial distribution systems. The simulation results are compared with existing multi‐objective optimisation techniques used for optimal DG integration problems in the literature and found to be promising.