Contribution à l’optimisation de l’intégration des énergies renouvelables au réseau électrique en présence des dispositifs FACTS-HVDC

Abstract

With the increase in energy consumption and fuel prices in recent years, many governments have supported renewable energy application projects in their countries. The stochastic nature of these resources creates new challenges for grid operators. Hence, it is necessary to meet these challenges through optimal analysis of the planning and operation of modern power systems for the economic integration of this type of energy. In this thesis, the work done focuses mainly on solving the optimal power flow (OPF) problem considering stochastic wind power, flexible AC transmission system (FACTS) and high voltage direct current (HVDC) technology. Among the important points addressed by this thesis is the incorporation of remotely located large wind farms by a multi-terminal DC (VSC MTDC) system. In this work, two advanced artificial intelligence (AI) techniques i.e., slime mould algorithm (SMA) and ant lion optimization (ALO) have been adopted to solve the OPF problem. In the case without wind power, numerical cases have been studied to illustrate the effectiveness of the proposed algorithms. The obtained results proved that the proposed algorithms significantly outperformed other existing techniques in the literature. In the problem of OPF considering stochastic wind power with and without incorporating the VSC-FACTS-HVDC system, the objective function is to minimize the total generation cost according to the optimal scheduling of thermal and wind units. The proposed algorithms were examined on the IEEE-30 bus and Algerian with 114-bus systems. The results obtained showed that the integration of wind farms has significantly improved the power system compared to the results obtained without wind power.