HYBRID BIOINSPIRED OPTIMIZATION ALGORITHM FOR DISTRIBUTION NETWORKS WITH PHOTOVOLTAIC GENERATION
The increase in the Distributed Deneration connection (DG), combined with the introduction of the concept of smart grids and the growing search for minimizing technical losses in electrical power systems (EPS), opens up a range of opportunities that should result in a more efficient operation and flexible power systems. Among the DG sources, the photovoltaic generation (PG) deserves mention, which, in recent years, has shown exponential growth, with prospects of maintaining this trajectory. To inject the power supplied by the PG into the network, inverters are used, which, depending on the technology used, can inject both active and reactive power, improving the power fator and controlling the voltage profile of the network. In this project a hybrid approach is developed, which associates an exact technique with a bioinspired metaheuristic technique, to optimize the distribution network with GF penetration. This optimization considers the possibility of network reconfiguration (RDS) and the adjustment of the inverters in relation to the supply of reactive power. The solution to the problem points to the grid configuration and the settings of the inverters that minimize the active power losses of the grid. The proposed model was implemented on the AMPL platform and resolved using the KNITRO software. The tests were performed using the system of 33 bus available in the literature and the results show the quality and validity of the proposed model with the assertiveness of the metaheuristic.