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Author: search.filters.author.Ngamroo, IssarachaiSubject: search.filters.subject.Electric vehicle

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    Synthetic Inertia-Power Sharing in High Renewable Power Grids Through Vehicle-to-Grid Topology
    (2024-01-01)
    Kerdphol, Thongchart
    ;
    Surinkaew, Tossaporn
    ;
    Ngamroo, Issarachai
    With the increasing integration of renewable energy sources (RESs), the overall inertia of the power system is expected to decline. The remaining inertia is crucial for regulating system frequency and mitigating excessive rates of change. The deployment of dispatchable loads, such as electric vehicles (EVs), offers a promising solution. This paper presents a synchronized inertia support framework utilizing a vehicle-to-grid (V2G) system through its bidirectional chargers. This concept is realized by integrating a large-scale energy storage system (ESS) composed of controllable EVs into an enhanced inertia emulation structure. The synthetic inertia control strategy has been refined to account for EV user convenience and synchronized state of charge (SOC) management, facilitating synchronized inertia power sharing. This approach enhances the grid's dynamic performance and resilience. Simulation results demonstrate that the proposed method effectively delivers rapid inertia support from the onboard ESS of EVs, improving frequency stability.
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    Simultaneous control of frequency fluctuation and battery SOC in a smart grid using LFC and EV controllers based on optimal MIMO-MPC
    (2017-03-01)
    Pahasa, Jonglak
    ;
    Ngamroo, Issarachai
    This paper proposes a simultaneous control of frequency deviation and electric vehicles (EVs) battery state of charge (SOC) using load frequency control (LFC) and EV controllers. In order to provide both frequency stabilization and SOC schedule near optimal performance within the whole operating regions, a multiple-input multiple-output model predictive control (MIMO-MPC) is employed for the coordination of LFC and EV controllers. The MIMO-MPC is an effective model- based prediction which calculates future control signals by an optimization of quadratic programming based on the plant model, past manipulate, measured disturbance, and control signals. By optimizing the input and output weights of the MIMO-MPC using particle swarm optimization (PSO), the optimal MIMO-MPC for simultaneous control of the LFC and EVs, is able to stabilize the frequency fluctuation and maintain the desired battery SOC at the certain time, effectively. Simulation study in a two-area interconnected power system with wind farms shows the effectiveness of the proposed MIMO-MPC over the proportional integral (PI) controller and the decentralized vehicle to grid control (DVC) controller.