Trip Pattern Impact of Electric Vehicles in Optimized Power Production using Orca Algorithm

Abstract

Power systems are run by combining different energy producers while the demand serves as the system’s energy user and covers all of the non-flexible and flexible loads, including electric vehicles (EVs). This study investigated the trip pattern impact of EVs, utilizing the Orca Algorithm (OA), in optimizing power production, applied to the IEEE-62 bus system as a model. Considering one-way and two-way trips over several categories of typical roads, the mobility of 14,504 EVs, divided into four driving patterns (Mobility 1-4), was estimated. Approximately 2,933 EVs traveled for working/business/study purposes, 3,862 EVs traveled for service/shopping purposes, approximately 5,376 EVs traveled for leisure purposes, while 2,334 EVs traveled for other reasons. The system had a total demand of 18,234.9 MVA, including 3,352.8 MW for electric vehicles and 14,151.5 MW for non-flexible loads. The EVs traveled a total of 119,018 km in Mobility 1, 141,799 km in Mobility 2, 184,614 km in Mobility 3, and 82,637 km in Mobility 4. The power produced was also used to charge the EVs during trips.