Quantum mechanics

Metaheuristic

Algorithm

Control (management)

Voltage

Engineering

Pareto optimal

Distribution (mathematics)

Power (physics)

Particle swarm optimization

Computer science

Mathematical optimization

Distributed generation

Pareto principle

Physics

Mathematics

Mathematical analysis

Artificial intelligence

Geometry

Control theory (sociology)

Reduction (mathematics)

Multi-objective optimization

Electrical engineering

Alexandria Engineering Journal

In this paper, two metaheuristic techniques are used for optimal allocation of Distributed Generations (DGs) to reduce the power losses in Radial Distribution Systems (RDS). These techniques are the adaptive Particle Swarm Optimization (APSO) and the modified Gravitational Search Algorithm (MGSA). Single, as well as multiple DGs, are optimized for the optimal size and site with unity- and optimal-PFs. Besides the reduction of power losses, the voltage stability and the total voltage deviation are considered as a multi-objective optimization (MOO) problem. For MOO operation, Pareto-optimal solution, aggregated sum, and ε-constrained techniques are used for determining the DG optimal size and site. The proposed algorithms have been applied to different RDSs, including the IEEE 69-bus and the 85-bus systems. The obtained results are matched favorably with those in the literature. The operation of the DG at optimal-PF is more effective than the UPF in the reduction of power losses. Besides, installing more DGs results in better performance of the systems. The MGSA and APSO algorithms, they are compared to the AEO algorithm according to different performance metrics. The results show that the MGSA and APSO outperform the AEO algorithm. Moreover, the obtained results are significantly approved by using a t-test.

Allocation of distributed generations in radial distribution systems using adaptive PSO and modified GSA multi-objective optimizations