Multi-Objective Optimization of Renewable Distributed Generation Placement and Sizing for Technical and Economic Benefits Improvement in Distribution System

Abstract

In recent years, the use of renewable energy sources by many power grid companies worldwide has increased significantly. The trend towards the use of renewable energy sources is mainly due to environmental issues and rising fuel prices associated with conventional electricity generation. Distributed generation units are power generation plants that are very important for the grid architecture of today’s power system. The benefit of adding these Distributed Generation (DG) units is to increase the power supply to the grid. However, the installation of DG units can cause a negative impact if not properly allocated and/or sized. Therefore, there is a need for their optimal sizing and allocation to avoid situations such as voltage instability and high investment cost. In this paper, four heuristic based algorithms, namely Particle Swarm Optimization (PSO) algorithm, Whale Optimization Algorithm (WOA), Dolphin Echolocation Optimization (DEO), and Slime Mould Algorithm (SMA) are applied to solve the optimal placement and sizing of DG units in distribution network planning. Three cases were used to address the network problems, which are represented by adding photovoltaic cells and wind turbines individually. In the last case, both were used, and the feasibility of algorithms was confirmed for two systems, IEEE 33-bus and 69-bus test systems. The comparison results showed that the SMA algorithm produces good solutions. In general, the SMA algorithm was able to reduce the two system losses. The reduction of real power losses in SMA, taking into account the technical and economic constraints in the IEEE 33 system, is reduced to a minimum of 66.31%, 67.3%, and 81.1%, While in 69 bus, reduced to a minimum of 90.7%,91%,and 97.30% for three cases respectively, as well as improving the voltage profile, thus obtaining a more efficient system.

Keywords

• Distribution generators
• hybrid renewable energy
• optimum placement and sizing
• metaheuristic-algorithms
• radial distribution network