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Author: search.filters.author.Pahasa, JonglakSubject: search.filters.subject.plug-in hybrid electric vehicle (PHEV)

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    Item type:Publication,
    Coordinated Control of Wind Turbine Blade Pitch Angle and PHEVs Using MPCs for Load Frequency Control of Microgrid
    (2016-03-01)
    Pahasa, Jonglak
    ;
    Ngamroo, Issarachai
    This paper proposes coordinated control of blade pitch angle of wind turbine generators and plug-in hybrid electric vehicles (PHEVs) for load frequency control of microgrid using model predictive controls (MPCs). The MPC is an effective model-based predictive control, which calculates future control signals by optimization method using plant model, current, and past signals of the system. The MPC-based PHEVs' power control can be used to reduce frequency fluctuation of microgrid effectively. However, for large system, large number of PHEVs is needed to produce satisfying frequency deviation. In order to reduce the number of PHEVs, the smoothing of wind power production by pitch angle control using MPC method is proposed and is coordinated with PHEVs control in this paper. The simulation results confirm that the coordinated control of pitch angle and PHEVs using MPCs is able to reduce the number of PHEVs and the frequency fluctuation can be maintained significantly. In addition, the proposed method is robust to the system parameters variation over proportional-integral derivative controllers.
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    PHEVs bidirectional charging/discharging and SoC control for microgrid frequency stabilization using multiple MPC
    (2015-03-01)
    Pahasa, Jonglak
    ;
    Ngamroo, Issarachai
    This paper proposes plug-in hybrid electric vehicles bidirectional charging/discharging and state of charge (SoC) control for a microgrid frequency stabilization using a multiple model predictive control (MMPC). The MMPC is the improved version of a model predictive control (MPC) for working with multiple operating condition of the system. The MPC is an effective model-based prediction which calculates the future control signals by optimization of a quadratic programming based on the plant model, past manipulate, and control signals of the system. By optimization of an electric vehicle power control signal at each time instant, as well as changing the MPC by electric vehicle battery SoC, the proposed MMPC is able to improve the frequency stabilization of the microgrid effectively.