Energy Management
S.M.H. Kamona; H.A. Abbas; A.A. Ibrahim; N.Q. Mohammed; A.A. Ali; B.A. Mohammed; M.S. Hamza
Abstract
The implementation of electric vehicles for this specific purpose could potentially cause an impact on the load on the network. From one standpoint, it is more advantageous to initiate the charging process of electric vehicle batteries as soon as they are connected to the grid, in order to guarantee ...
Read More
The implementation of electric vehicles for this specific purpose could potentially cause an impact on the load on the network. From one standpoint, it is more advantageous to initiate the charging process of electric vehicle batteries as soon as they are connected to the grid, in order to guarantee sufficient charge levels in the event of unforeseen events. The current investigation showcases an innovative algorithm specifically engineered for the smart grid, wherein the principal aim is to approximate the time needed to fully charge electric vehicles. The algorithm being evaluated prioritizes the decrease in both the unfulfilled energy demand and the daily load profile standard deviation. The algorithm has been purposefully designed to regulate and supervise the charging process in an efficient manner. The algorithm incorporates various elements pertaining to the anticipated conduct of specific electric vehicles, such as their projected arrival and departure times, as well as their initial charge status upon arrival. In situations involving a substantial quantity of automobiles, statistical techniques are applied to decrease the number of variables, thereby diminishing the algorithm's computational time. The optimization technique implemented in this research is inspired by natural phenomena and is founded upon the cuckoo orphan search pattern. The proposed algorithm and the PSO algorithm were implemented in order to simulate the 34-bus IEEE standard radio distribution network. Upon comparing the outcomes derived from the analysis, it was discovered that the implementation of the CS algorithm led to a substantial decrease in peak load by 33% in comparison to the situation in which no optimization was executed. Furthermore, the CS algorithm accomplished a 27% reduction in peak load, which was superior to the PSO algorithm.
