Optimal Sizing and Placement of Renewable Energy Sources Based Distributed Generations With Smart Scheduling of Electric Vehicles Charging Stations

One of the most beneficial and effective methods for reducing the power losses of the distribution networks (DNs) is using distributed generations (DGs). The issue of optimal placement and sizing of DGs is a challenge that needs to be investigated carefully, as an improper location and sizing lead to a negative effect on the DN. In this work, an IEEE 33-bus is used as a test system for optimal placement and sizing of four DGs, three of them being photovoltaic (PV) sources and the fourth is a wind turbine (WT). The environmental data (irradiance, temperature, and wind speed) of Baghdad city (latitude: 33.29°, longitude: 44.38°) are used for training the artificial neural networks (ANNs) to forecast the day ahead values of the environmental variables for calculating the power production of PVs and WT. Particle swarm optimization (PSO) technique is used to optimize the location and sizing of the DGs. The operation cost of the system is optimized using genetic algorithm (GA) depending on the optimized sizing and placement of the DGs. Four electrical vehicles charging stations (EVCSs) are interconnected to the implemented DN with considering the uncertainty of hourly charging power demand using the queuing model. The optimal cost of the EVCSs is determined by using fuzzy logic system (FLS) to optimize the energy management of the daily power dispatch and peak power shifting to meet the peak power production of the DGs. The power losses are minimized by 50%, enhancing the voltage profile of the distribution system, and the operation cost is minimized by 19%. The annual operation cost saving of EVCSs is found to be 44.3%.