Optimal load frequency control of multi-area multi-source deregulated power system with electric vehicles using teaching learning-based optimization: a comparative efficacy analysis

This article aims to present the efficacy analysis of electric vehicle (EV) charging/discharging process as a sink or a distributed energy source (DES) in the framework of LFC problem of multi-area multi-source deregulated power system (MAMS-DPS). The energy storage/extraction of EVs acts as a noteworthy role in mitigation of deviation frequency of DPS. A problem of AGC system for two-area DPS having two thermal power generators of non-reheat type and aggregated fleet of EVs in each area is considered to study here. The electric power contract between the distribution (DISCOs) and generation (GENCOs) companies in the restructured environment of DPS is specified through a contract participation matrix named as the DPM. In DPS area deviation frequency, generated and tie-line powers fluctuate in case of variation of load. To resolve these problems the efficacy of EV is accessed applying the optimal control scheme using TLBO algorithm and compared performances with the control schemes based on the optimization algorithms of GA as well as PSO, and PI, PID control schemes. The comparative performance of responses of system variables of the deviation frequency, generated power, and tie-line power are analysed applying the control schemes as PI control for without/with EV cases, and PID control, and the GA/PSO/TLBO-tuned PI/PID control schemes for with EV case. The TLBO-PID control scheme outperforms to mitigate the deviations observing the steady-state error, undershoot, overshoot, and settling time performance indices.