A novel approach for comparative analysis of distributed generations and electric vehicles in distribution systems.

Electric vehicles were introduced to the market as a way to reduce dependency on internal combustion engine-driven transportation systems. However, this method increased the burden on the current electrical grid rather than reducing it. In the power grid, distributed generation ideas are presented to reduce this burden. In order to reduce the distribution systems' overall real power loss, this research demonstrates how hybrid optimization algorithms, including genetic algorithms and Monte Carlo simulation methodologies, can be used to determine the best location and size for various types of distributed generation with electric vehicle scheduling. The suggested approach's practicality was tested using a 16-bus distribution test system. At both static and ZIP load models, the more effective kinds of distributed generation with electric vehicle couples are DG2 with fuel cell electric vehicles. [ABSTRACT FROM AUTHOR]