Reinforcement of power system performance through optimal allotment of distributed generators using metaheuristic optimization algorithms

Abstract

Owing to the acute shortage of electric power in the majority of countries, short-term measures such as installation of Distributed Generators (DGs) have attracted much attention in recent decades. Employment of DGs can provide numerous advantages for the power systems through reduction of losses, escalation of the voltage profile, as well as mitigation of pollutant emissions. However, in case they are not optimally allotted, they may even lead to aggravation of the network operation from different aspects. The aim of this paper is to explore the optimal size and location of DGs using metaheuristic optimization algorithms so that the network performance is enhanced. The salient feature of the proposed strategy compared to the previous works is that it contemplates optimal allotment of DGs under various objectives, i.e. minimization of total network active and reactive power losses, and Cumulative Voltage Deviation (CVD), with different weight values. Furthermore, the impact of enhancement in the number of DGs on different aspects of power system performance is investigated. Finally, to increase the accuracy of the results, three different nature-inspired optimization algorithms, i.e. Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), and Particle Swarm Optimization (PSO) are deployed, and their speed in approaching the global optimum is compared with each other. The simulation results on IEEE 14-bus system indicate that the proposed strategy not only can reinforce the overall network performance through reduction of active and reactive power losses, and voltage deviation but also lead to the improvement of network voltage profile.