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has been cited by the following article:

• TITLE: Optimal Placement and Sizing of Distributed Generations for Power Losses Minimization Using PSO-Based Deep Learning Techniques

  AUTHORS: Bello-Pierre Ngoussandou, Nicodem Nisso, Dieudonné Kaoga Kidmo,   Kitmo

  KEYWORDS: Distributed Generations, Deep Learning Techniques, Improved Particle Swarm Optimization, Power Losses, Power Losses Minimization, Optimal Placement

  JOURNAL NAME: Smart Grid and Renewable Energy, Vol.14 No.9, September 28, 2023

  ABSTRACT: The integration of distributed generations (DGs) into distribution systems (DSs) is increasingly becoming a solution for compensating for isolated local energy systems (ILESs). Additionally, distributed generations are used for self-consumption with excess energy injected into centralized grids (CGs). However, the improper sizing of renewable energy systems (RESs) exposes the entire system to power losses. This work presents an optimization of a system consisting of distributed generations. Firstly, PSO algorithms evaluate the size of the entire system on the IEEE bus 14 test standard. Secondly, the size of the system is allocated using improved Particles Swarm Optimization (IPSO). The convergence speed of the objective function enables a conjecture to be made about the robustness of the proposed system. The power and voltage profile on the IEEE 14-bus standard displays a decrease in power losses and an appropriate response to energy demands (EDs), validating the proposed method.