Stratégies d’Intégration optimale de sources des énergies renouvelables dans le réseau de distribution électrique en considérant des indices techniques et économiques.

Abstract

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.