Distributed energy resource placement considering hosting capacity by combining teaching–learning-based and honey-bee-mating optimisation algorithms

Abstract

An emerging challenge of most electric utilities is to expand hosting capacity constraints and maximise the interconnection of distributed energy resources (DER). DERs have proliferated in distribution systems in most jurisdictions, and this proliferation has not occurred in an orderly fashion. Reports of degradation in power quality and operations are common. With the continuing adoption of DERs, it is imperative to develop methods to unlock the potential of DERs in a way that they are used to support system needs, rather than degrading them. This concept’s advantages are varied in different power system scenarios based on the considered models for owning and operating DER. With this objective in mind, this paper proposes a new algorithm to identify the optimal locations for new DERs. The proposed method is a novel combination of teaching–learning-based optimisation (TLBO) and honey-bee-mating optimisation (HBMO) algorithms, and it capitalises on both techniques’ benefits. A major novelty of this method is that it fully accounts for current hosting capacity and all existing DERs and incorporates these parameters in the optimisation algorithm. The results reveal that adding fuzzy clustering to the multiobjective process improves the DER placement problem. The DERs considered in this optimisation problem are fuel cell units, and the objective function includes cost, losses, and voltage deviation. The proposed technique is adopted in the IEEE 70-bus-radial test system, and its performance is compared with those of other optimisation algorithms. The results reveal the proposed algorithm’s superiority in accuracy and computational speed when reaching the optimal solution. As a case in point, the calculation time of the proposed algorithm is about %9 and %22 faster than TLBO and HBMO, respectively.