Presenting a fuzzy multi-objective hybrid evolutionary approach for dynamic reconfiguration of distribution feeders in the presence of distributed generation units and electric vehicles

The presence of electric vehicle technology in distribution networks as controllable resources provides advantages such as voltage regulation, power peaking, and loss reduction. This technology has a positive effect on the performance of the distribution network, but the simultaneous presence of electric vehicles and distributed generation sources requires optimal planning because the lack of access to an application reduces the life of these technologies and can cause blackouts in the power network. Therefore, in this study, the dynamic reconfiguration of the distribution network in the simultaneous presence of distributed generation sources and electric vehicles is proposed. Also, the time-of-use mechanism has been proposed as one of the demand response applications to improve the power consumption of subscribers. The objective functions in this study include the reduction of energy loss, operational cost and energy not supplied as the objective function of reliability. In general, the optimization problem of distribution network reconfiguration is complex and non-convex. Also, considering the effect of distributed generation units and electric vehicles makes the problem more complicated than before. Therefore, finding a practical method to solve the optimization problem is one of the main challenges of this paper. For this purpose, a novel hybrid algorithm based on a combination of an improved particle swarm optimization-artificial bee colony is presented to overcome the complexities of the optimization problem. The proposed method has