Coati optimization algorithm-based optimal frequency control of power systems including storage devices and electric vehicles

Abstract

Load frequency control (LFC) plays a prominent role in electrical power grids. Its importance is evident in controlling the frequency as well as the tie-line power exchange. Recently, frequency deviations and tie-line power exchange have been enhanced when implementing renewable energy sources (RESs) and energy storage (ES) equipment in modern power systems. This research proposes a novel optimization algorithm, the Coati Optimization Algorithm (COA), to fine-tune the gains of proportional-integral-derivative (PID) controllers. These gains are used in the LFC loop in the presented multi-area power system model. This power system model incorporates conventional generating units, RES, electric vehicles (EVs), and ES units, which presents a significant challenge to the PID controller in handling the effects of RES uncertainties. Two types of RESs are considered: wind and photovoltaic systems. Additionally, different elements of ES equipment have been incorporated: superconducting magnetic energy storage and capacitor energy storage and EVs integrated with the grid. The efficiency of the COA-based PID controller is compared to that tuned by alternative methods used in PID controllers based on other algorithms indicating a reduction in the transient response specifications such as maximum percentage undershoot, maximum percentage overshoot, and settling time of frequency and tie-line power deviations by more than 50 % than other several algorithms. The system's response has been assessed through three scenarios: without the effect of RESs, including RESs uncertainty, and finally, including the effect of RESs, EVs and ES units. Notably, the proposed methodology can enhance the transient response of frequency and tie-line power deviations with ES devices by more than 10 % compared with the case of without storage units.