IMPROVEMENT OF LOAD FREQUENCY CONTROL FOR A EVS AND RESS INTEGRATED HYBRID POWER SYSTEMS
DOI:
https://doi.org/10.55766/sujst11993Keywords:
Electric Vehicles, Fractional Order Controllers, Load Frequency Control, Load Perturbations, Mountain Gazelle Optimizer, Renewable Energy SourcesAbstract
The purpose of the study is to resolve the emergent issue by optimising the parameters of fractional order controllers (FOCs) using the Mountain Gazelle Optimiser (MGO) and validate the strategy on real-time simulator using the platform OPAL-RT. This paper implements load frequency control (LFC) in multi-area, multi-source power systems (MSPSs), encompassing thermal power plants (TPPs), hydro power plants (HPPs), and gas power plants (GPPs), as well as the integration of renewable energy sources (RESs) and electric vehicles (EVs). On the other hand, this study also takes system uncertainties such as load disturbances and nonlinearities such as governor dead band (GDB) and generation rate constraints (GRC). The superiority of MGO in optimization has been assessed in comparison to various other metaheuristic optimization technique (MOT), such as Particle Swarm Optimisation (PSO), Grey Wolf Optimisation (GWO), and Archimedes Optimisation Algorithm (AOA). The result analysis in case-1 shows that the value of objective function for MGO is 63.89%, 81.16%, 75.47%, and 95.6% less in comparison to WOA, PSO, GWO, and AOA, respectively. Similarly, in case-2 the value of the objective function for MGO-tuned fractional order proportional integral derivative (FOPID) controller is 3.8%, 11.93%, and 97.77% less in comparison to the tilt integral derivative (TID), PID, and IC, respectively. The sensitivity analysis in terms of parameter variations and stability analysis through the Bode plot also has been done for the real feasibility of the controller. System stability with respect to frequency is substantially improved by the MGO-based FOPID controller compared to other controllers.
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