Power and Energy Management Strategies for a Microgrid with the Presence of Electric Vehicles and CAES Considering the Uncertainty of Resources

Abstract

We are witnessing the growth of microgrid technology and the development of electric vehicles (EVs) in the world. These microgrids seek demand response (DR) and energy storage for better management of their resources. In this research, microgrids, including wind turbines, photovoltaics, battery charging/discharging, and compressed air energy storage (CAES), are considered. We will consider two scenarios under uncertainty: (a) planning a microgrid and DR without considering CAES, and (b) planning a microgrid and DR considering CAES. The cost of charging the battery in the second study decreased by $0.66 compared to the first study. The battery is charged with a difference of $0.7 compared to the case of the first study. We will also pay for unsupplied energy and excess energy in this microgrid. Then, we test the scheduling of vehicles to the grid (V2G) in the IEEE 33-bus network. The first framework for increasing network flexibility is the use of EVs as active loads. The scheduling of vehicles in the IEEE 33-bus network is simulated. Every hour, plug-in hybrid electric vehicle (PHEV) charging and discharging, active power loss, and cost will be compared with IHS and PSO algorithms. The difference obtained using the IHS algorithm compared to the PSO algorithm is 1.002 MW and the voltage difference is 9.14 (Formula presented.).