الخلاصة:
This work is part of the study of optimal integration of Distributed Generation
(DG) based on photovoltaic and wind turbine renewable energy sources on the radial
IEEE 33-JB, IEEE 69-JB, and IEEE 118-JB distribution networks which are widely used in
literature for the planning and operation of the distribution system. In order to better
exploit both photovoltaic and wind turbine sources, recent meta-heuristic optimization
methods have been proposed to identify their optimal location and size. These
methods are the Sinusoidal Modulated Particle Swarm Optimization algorithm (SMPSO),
Moth-flame Optimization algorithm (MFO), Whale Optimization algorithm (WOA) , and
also the Salp Swarm Optimization algorithm (SSA). However, the Newton-Raphson
method has been used for the calculation of power flow. For an optimal and correct
integration, two objective functions have been proposed, the first is used to minimize
only the active power losses of the distribution networks by the integration of multi
sources of the distributed generation. While the second function is formulated as a
new multi-objective function which consists of minimizing the active power loss index
(APLI), the voltage deviation index (TVVI) and also minimizing the annual cost of active
power losses index (CLI). Another study was carried out using a hybrid algorithm called
PSO-MFO which is the result of the hybridization of the Particle Swarm Optimization
algorithm (PSO) and Moth-flame Optimization algorithm (MFO), by combining the
advantages of these two algorithms. This algorithm is used to optimize the same new
multi-objective function by integrating photovoltaic and wind sources, taking into account
the intermittent nature of these two sources as well as the variation of the load over 24
hours. The results are with a high solution quality compared to other methods reported in
the literature. The simulation results also prove that the proposed algorithms are reliable
and applicable for the optimal source integration problem of distributed generation for
various distribution networks of different sizes and complexity.
