Title: Impact of optimal location and sizing of distributed generation and automatic reclosers in distribution systems

Authors: Luis F. Grisales; Alejandro Grajales; Oscar D. Montoya; Ricardo A. Hincapie; Mauricio Granada

Addresses: Electromechanical and Mechatronics Department, Instituto Tecnológico Metropolitano, Calle 73 No.76A-354, Medellín, Colombia ' Program of Electrical Engineering, Universidad Tecnológica de Pereira, Carrera 27 No.10-02 Barrio Los Alamos, Pereira, Colombia ' Program of Electrical Engineering, Universidad Tecnológica de Pereira, Carrera 27 No.10-02 Barrio Los Alamos, Pereira, Colombia ' Program of Electrical Engineering, Universidad Tecnológica de Pereira, Carrera 27 No.10-02 Barrio Los Alamos, Pereira, Colombia ' Program of Electrical Engineering, Universidad Tecnológica de Pereira, Carrera 27 No.10-02 Barrio Los Alamos, Pereira, Colombia

Abstract: In this paper, a cascade methodology of two stages to solve the problem of the optimal location and sizing of distributed generators (DG), and determining an adequate protection scheme to improve reliability indices and reduce the non-supplied energy level (NSEL) is proposed. In stage 1, the optimal location of DG is determined by using a mathematical model, in which the objective function is composed by a linear combination between reduction of power losses and investment cost. To solve this problem, three sensitivity indices to determine a candidate set of nodes to install DG and three types of technologies are considered. A hybrid methodology based on Chu-Beasley genetic algorithm and particle swarm optimisation is used as a solution technique. In stage 2, normally closed and normally open reclosers are located; the concept of operational areas is analysed and a multi-objective problem is formulated, where the investment costs minimisation is the first objective function and NSEL minimisation is the second objective function. To solve this stage, the NSGA II algorithm is employed. A 102-node test feeder was used in order to prove the efficiency of the methodology proposed.

Keywords: automatic reclosers; Chu and Beasley genetic algorithm; CBGA; distributed generation; multi-objective optimisation; non-supplied energy level; NSGA II; operational areas; particle swarm optimisation; PSO.

DOI: 10.1504/IJPEC.2019.096724

International Journal of Power and Energy Conversion, 2019 Vol.10 No.1, pp.76 - 88

Received: 18 Nov 2015
Accepted: 14 Dec 2016
Published online: 10 Dec 2018 *

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