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Please use this identifier to cite or link to this item: http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/9297
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dc.contributor.authorMathur, Hitesh Dutt-
dc.date.accessioned2023-02-17T04:50:10Z-
dc.date.available2023-02-17T04:50:10Z-
dc.date.issued2020-
dc.identifier.urihttps://ieeexplore.ieee.org/abstract/document/9182006-
dc.identifier.urihttp://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/9297-
dc.description.abstractDistributed Energy Resources and Electric vehicles are getting more popular in the energy market. They are emerging as promising concepts that can bring a revolution in the field of energy resources and transportation systems. The integration of different renewable energy resources and electric vehicles leads to a comprehensive microgrid (MG). It is a fact that an increase in power demand leads to a drop in power frequency and vice-versa, which adversely affects the power quality of the system. Therefore, the primary idea of this research work is to regulate frequency by controlling the wind power and available EV power. In this paper, a detailed MG model is simulated in MATLAB/Simulink with wind and EV as one of the sources using fractional order PID (FOPID) controller. Further, parameters of FOPID controller is optimized by using Ant Colony Optimization(ACO) and Particle Swarm Optimization (PSO). The frequency responses, wind power, and EV power results are compared for three cases; FOPID without optimization, ACO based FOPID, and PSO based FOPID. The generalized wind model and total energy model (TEM) are used for simulating wind source and EV, respectively. Optimizer based FOPID controller provides a better response for the defined objective function.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectEEEen_US
dc.subjectMicrogriden_US
dc.subjectElectric vehiclesen_US
dc.subjectFrequency Regulationen_US
dc.subjectAnt Colony Optimization (ACO)en_US
dc.subjectParticle Swarm Optimizationen_US
dc.subjectFOPID controlleren_US
dc.titleFrequency Excursion Mitigation in a Multi-source Islanded Energy System Using Meta-Heuristic Optimization Strategiesen_US
dc.typeArticleen_US
Appears in Collections:Department of Electrical and Electronics Engineering

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