Quasi-Oppositional Whale Optimization Optimized Load Frequency Stabilization of Hybrid Power Systems Integrated with Electric Vehicle

Electric vehicle (EV) provides the most promising transportation system at present and also for the future. The uncertainties are introduced when EVs are connected to the grid, which becomes a new challenge for the load frequency control (LFC) of a power system. This paper explores the LFC of a hybrid power system (HPS) integrated with EV. The isolated HPS (IHPS) considered for this work consists of a combined cycle gas turbine (CCGT), a diesel engine generator (DEG), a wind turbine generator (WTG) and a solar photovoltaic generating unit. A superconducting magnetic energy storage is used to give uninterrupted power supply and to improve the dynamic stability to the system. The performance of charging and discharging of EV's battery is also analyzed. To enhance the performance of the EV, a proportional-integral-derivative (PID) controller is employed for controlling the pulse of the static switches. In concern with the LFC task, the PID controller is also used for WTG, CCGT, and DEG. The controller gains are tuned by the use of quasi-oppositional whale optimization algorithm (QOWOA). The performance of IHPS model is studied under different load perturbations. A comparative dynamic response of frequency deviation profile is plotted subjected to QOWOA based PID controller.

Automated summary

This paper explores the load frequency control of a hybrid power system integrated with electric vehicles (EVs) and analyzes the performance of EV's battery charging and discharging. By employing a PID controller and the quasi-oppositional whale optimization algorithm (QOWOA), the power system performance is improved and the controller gains are tuned. The study demonstrates the effectiveness of the proposed control strategy in enhancing the system's performance.