Abstract

Electric vehicles (EVs) are becoming increasingly prevalent worldwide due to their potential to reduce carbon emissions and improve air quality. However, the widespread adoption of EVs presents significant challenges for the power grid, particularly in managing the increased demand for electricity. Under this need, this chapter proposes a Demand-Side Management (DMS) for EVs in a Santa Elena distribution network using artificial intelligence. The proposed approach incorporates an algorithm that uses K-means for pattern recognition and selects a feeder with a representative demand of the system, which reduces the computational burden. To reduce the peak of the demand, a power flow executed in CYME® and Particle Swarm Optimization (PSO) programmed in Python is used to implement the DMS. The results reveal that the proposed algorithm contributes to managing the feeder demand, improving the voltage profile and power factor in the charging station node