Multiverse optimized ANFIS scheduled fractional ordered proportional-integral-derivative controller for mitigation of frequency excursions in AC microgrid coupled with electric vehicles

ABSTRACTOwing to the current environmental concerns, the RESs have become popular as the microgrid structures for power generation. However, due to capricious weather and loading conditions, the generated power and thereby the microgrid frequency get adversely affected. This instant study puts forth the control strategy for the mitigation of frequency excursions, arising out of step load disturbance, in AC microgrid through adaptive network fuzzy inference system (ANFIS) scheduled fractional ordered proportional-integral-derivative (PID) control optimally tuned with a multiverse optimizer. The control strategy proposition is compared against multi-verse optimized PID and fractional order PI controls. Furthermore, the study investigates as to how EV affects in stabilizing the system frequency in the backdrop of a load disturbance. For a more realistic assessment, the proposition is assessed in the face of system nonlinearities and random load perturbations also to establish its robust and stable behavior. The results prove the efficacy of the multi-verse optimized ANFIS scheduled fractional ordered PID controller. Simulations are executed using MATLAB® software. The results are also validated by experimental studies employing a hardware-in-loop configuration on the OPAL-RT real-time simulator.KEYWORDS: Microgridrenewable energy systemenergy storage systemfrequency excursionsfractional-ordered controller Disclosure statementNo potential conflict of interest was reported by the author(s).Nomenclature Abbreviation=ANFIS=Adaptive Network Fuzzy Inference SystemFOPID=Fractional-Ordered Proportional-Integral-DerivativeEV=Electric VehicleICA=Imperialist Competition AlgorithmBESS=Battery Energy Storage SystemMC=Microsource ControllerMVO=Multiverse OptimizerRES=Renewable Energy SourcesV2G=Vehicle to GridDER=Distributed Energy ResourcesAGC=Automatic Generation ControlFESS=Flywheel Energy Storage SystemCES=Capacitive Energy StoragePSO=Particle Swarm OptimizationLFC=Load Frequency ControlCOA=Coyote Optimization AlgorithmESS=Energy Storage SystemsGOA=Grasshopper Optimization AlgorithmDG=Distributed GenerationMPC=Model Predictive ControlPV=PhotovoltaicWTG=Wind Turbine GeneratorMGCC=Microgrid Central ControllerLC=Load ControllerITAE=Integral of Time multiplied Absolute ErrorFC=Fuel CellPEV=Plug-in EVLCC=Local Control CentreISE=Integral of Squared ErrorMF=Membership FunctionsLSE=Least Squares ErrorDEG=Diesel Engine GeneratorBP=BackPropagationALO=Ant Lion OptimizationFIS=Fuzzy Inference SystemSMES=Superconducting Magnetic Energy StoragePCC=Point of Common CouplingDE=Differential EvolutionIAE=Integral of Absolute ErrorTLBO=Teaching–Learning-Based OptimizationSSA=Salp Swarm AlgorithmSubscripts=Tg=Generator Time ConstantTI/c=Interconnection Device Time ConstantTIN=Inverter Time ConstantTt=Turbine ConstantKP=Proportional GainD=Damping CoefficientKI=Integral GainR=Droop ConstantKD=Derivative GainH=Inertia ConstantΛ=Order of IntegratorTBESS=BESS Time Constantµ=Order of DifferentiatorTFESS=FESS Time Constant