ABSTRACT
Distribued Generations (DG) have economic, financial, and environmental benefits. DG reduces power losses in the distribution system but has a negative impact on the protection devices. In this article, the IEEE 33 bus system will be used and tested by adding up to three DG units using MATLAB/SIMULINK software. the optimization techniques that will be used are Grey Wolf Optimizer, Whale Optimization Algorithm, Genetic Algorithm, and Coronavirus Herd Immunity or COVID-19 optimization techniques to select the optimal site and size of the DG units based on the lowest pay-back period considering the voltage limits and power losses. The paper proposes a modified mutation operator for COVID-19 based on Gaussian and Cauchy mutations to have better performance and lower variance. The proposed algorithm is compared with the other optimization techniques. The proposed algorithm achieved better results, which proved to have competitive performance with state-of-the-art evolutionary algorithms. © 2022 IEEE.
ABSTRACT
Recently, no one can deny the importance of distributed generation (DG). The tendency toward using DG in electrical generation networks is increasing rapidly. In addition to the positive aspects, the ecological, economic, and technical advantages of connecting DG units to distribution networks, the location and the size of the DG unit are important factors for these advantages. For this purpose, a novel method of optimal apportionment and sizing when introducing DG is presented, based on the curtailment of the SPP (simple payback period). This can be achieved through the use of the IEEE 33 bus system and optimization techniques such as Grey Wolves, Grey Wolves with Cauchy equation, and Coronavirus techniques that help determine the location and size of generators in the distribution network. In this paper, we aim to present a thorough study on the impact of the DG units, when connected to distribution networks, based on the economical viewpoint using MATLAB software. © 2022 IEEE.