Meta-heuristics Applied to Multiple DG Allocation in Radial Distribution Network: A comparative study

• Published in: Intelligent Systems and Computer Vision (Online) pp. 1 - 8
• Main Authors: Tarraq, Ali, Elmariami, Faissal, Belfqih, Abdelaziz, Haidi, Touria, Agouzoul, Naima, Gadal, Rabiaa
• Format: Conference Proceeding
• Language: English
• Published: IEEE 18.05.2022
• Subjects: Chaotic-based Neural Network Algorithm; Distribution networks; Improved Cuckoo Search Algorithm; Improved Grey Wolf optimizer; Linear programming; Meta-heuristics; Minimization; Multiple DG allocation; Optimization methods; Power loss reduction; Reactive power; Uncertainty; Voltage
• ISSN: 2768-0754
• DOI: 10.1109/ISCV54655.2022.9806131

Due to the increasing electricity demand, the distribution network is becoming more and more uncontrollable and subject to higher power losses. To cope with this problem, the optimal integration of distributed generators (DGs) is proving to be efficient and sustainable. In this context, this paper investigates the minimization of active losses and the improvement of voltage profile through the integration of Multiple DGs in the IEEE 33-bus radial distribution system (RDS). The study aims to determine the optimal locations and sizes of l to 7 DGs to be integrated, in the case of unity power factor (UPF-DG) and optimal power factor (OPF-DG). The results are evaluated in a comparative study between three meta-heuristic optimization methods, namely Improved Cuckoo Search Algorithm (ICCSA), Improved Grey Wolf optimizer (IGWO), and a Chaotic-based Neural Network Algorithm (CNNA). In summary, CNNA outperforms the other algorithms mentioned above by increasing the problem dimension. Indeed, the total active loss reduction can reach 98.27% by integrating seven OPF-DGs. On the opposite, poor results are generated by ICCSA.