Two-stage distributed generation optimal sizing with clustering-based node selection

Nowadays, distributed generation (DG) is playing a significant role in the electrical energy systems. The diffusion of DG in the electrical networks could be beneficial to improve network operation, but excessive amounts of DG in operation could cause violations of the system constraints. This paper presents a new method to obtain the optimal size of DG sources in electrical distribution systems, taking into account the time-dependent evolution of generation and load. This method adopts a procedure composed of two nested calculation stages. The external stage is carried out by selecting a set of candidate nodes through a clustering-based approach based on normalized loss sensitivity factors and normalized node voltages. The internal stage is an exhaustive search driven by the calculation of an objective function with energy losses and voltage profile components, aimed at finding upgraded DG sizes using exhaustive search on a set of available sizes at the candidate nodes. The resulting method avoids the combinatorial explosion of the solutions to be analysed and determines pseudo-optimal DG sizing without violation of any of the system constraints under any operating condition. The proposed method is tested on a 20 kV rural distribution network, showing its effectiveness in obtaining the pseudo-optimal solution with a relatively low computational burden.