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. This research shows how to combine hybrid optimization algorithms for optimum location and sizing of different sorts of Distributed Generation (DGs) with Electric Vehicle (EV) scheduling in distribution systems using static and ZIP load models to reduce the system's total real power loss. For assessment, many types of DGs and EVs are evaluated. For DGs with EVs scheduling in the distribution system, multiple static and ZIP load models are examined. Real power loss mitigation is achieved via hybrid optimization strategies that include Genetic algorithms and Monte Carlo simulation methodologies. 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 DGs with EVs couples are DG2 with Fuel cell electric vehicles.