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.