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Improved Resilient Model Predictive Control for Enhanced Microgrid Virtual Inertia Emulation by Virtual Energy Storage System Under DoS Attacks
(2023-01-01)
Muangchuen, Satawat
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Pahasa, Jonglak
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Ngamroo, Issarachai
The distributed control of a microgrid is fully dependent on advanced information and communication technologies that are sensitive to cyber-physical systems. Cyberattacks, such as denial-of-service (DoS) attacks, can cause unstable operation of low-inertia microgrids. This paper proposes enhanced microgrid virtual inertia control under DoS attacks using an improved resilient model predictive control (IRMPC)-based virtual energy storage system (VESS). IRMPC comprises an attack detector, an autoregressive (AR)-based signal estimator, and an MPC-based VESS controller. An attack detector was used to detect the DoS attacks. An AR-based signal estimator is then used to estimate the feedback data that are subjected to DoS attacks. The firefly algorithm was used to optimize the AR parameters. The effectiveness of the proposed IRMPC was compared with that of conventional model predictive control, conventional model predictive control-based VESS, and resilient model predictive control-based VESS. The simulation results revealed that under a DoS attack, the proposed IRMPC can successfully improve the microgrid virtual inertia emulation. Additionally, the proposed IRMPC has a performance effect over the compared techniques in terms of the reduction in RoCoF deviation and frequency deviation during normal situations, DoS attacks, and disconnection of wind turbine generation. The simulation results also confirmed that IRMPC is robust to microgrid parameter variations when compared to the other methods.
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Fabrication of Metallic Nano-Ring Structures by Soft Stamping with the Thermal Uplifting Method
(2022-05-01)
Potejanasak, Potejana
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Pahasa, Jonglak
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Ngamroo, Issarachai
In this study, the unconventional microfabrication method by the combined processes of the chemical soft stamping technique with the thermal uplifting technique to fabricate metal nanoarrays on a glass plate is proposed and their feasibility verified. The gold micro-ring arrays on a quartz glass plate are realized by utilizing a chemical template with the thermal uplifting method. Their optical properties are studied experimentally. First, a plastic mold is made of a Biaxially Oriented Polyethylene Terephthalate (BOPET) via the hot embossing method. Then, the Methanal micropatterns are transferred onto an etched surface of a substrate via a soft stamping process with a BOPET mold. The gold thin film is coated onto the methanol patterned glass plate via the Ar+ sputter coating process. Finally, the metallic micro-ring structures are aggregated on a glass plate via the thermal uplifting technique. The LSPR optical properties as the extinction spectrums of the gold micro-ring structure arrays are investigated experimentally. It is confirmed that this method was able to fabricate plasmonic micro-ring arrays with low cost and high throughput.
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MPC-Based Virtual Energy Storage System Using PV and Air Conditioner to Emulate Virtual Inertia and Frequency Regulation of the Low-Inertia Microgrid
(2022-01-01)
Pahasa, Jonglak
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Potejana, Potejanasak
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Ngamroo, Issarachai
Grid-connected large-scale power converter-based intermittent renewable energy sources (RES) reduce system inertia, increase frequency fluctuation, and increase the rate of change of frequency (RoCoF). An energy storage system (ESS) is an indispensable component of a smart grid, and is used to overcome low-inertia problems. However, the capital and maintenance costs of ESS are high and high RoCoF events are less frequent in power systems. Therefore, the introduction of a virtual energy storage system (VESS) to provide the function of a conventional ESS for power system ancillary services is an innovative and cost-effective method. This study investigated a VESS using photovoltaic (PV) generators and inverter air conditioners (IACs) to provide virtual inertia and frequency regulation for a low-inertia microgrid. A model predictive control (MPC)-based VESS regulates indoor temperature, microgrid frequency, and RoCoF. The impact of parameter variation, that is, the microgrid frequency weight, indoor temperature weight, virtual inertia gain, and number of IACs, was studied and selected by considering the ability of the parameters to provide virtual inertia and frequency regulation. Finally, the efficiency and robustness of the proposed MPC-based VESS technique are compared with those of a conventional VESS. Simulation results revealed that the proposed MPC-based VESS can improve the virtual inertia, reduce the frequency deviation, and reduce the RoCoF of the studied microgrid. In addition, the proposed method is robust to variations in the system parameters.
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Two-Stage Optimization Based on SOC Control of SMES Installed in Hybrid Wind/PV System for Stabilizing Voltage and Power Fluctuations
(2021-11-01)
Pahasa, Jonglak
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Ngamroo, Issarachai
This paper proposes the two-stage optimization of superconducting magnetic energy storage (SMES) integrated into hybrid wind/photovoltaic (PV) generators considering the state of charge (SOC) control for stabilizing voltage and power fluctuations. The first stage aims to achieve the minimal coil inductance of SMES that guarantees the stored energy for system stabilization. In the second stage, the control parameters of SMES are optimized to keep the SOC at the desired level. As a result, the minimum coil inductance with sufficient stored energy of SMES for stabilizing system and regulating SOC at the target value can be obtained for entire period of operation. Study results in the distribution system with various loads ensure that the hybrid wind/PV with optimized internal SMES yields superior stabilizing performance in comparison with the SMES externally installed at the wind/PV terminal.
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Multi-objective decentralized model predictive control for inverter air conditioner control of indoor temperature and frequency stabilization in microgrid
(2021-11-01)
Pahasa, Jonglak
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Potejana, Potejanasak
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Ngamroo, Issarachai
Microgrid (MG) is a novel concept for a future distribution power system that enables renewable energy sources (RES). The intermittent RES, such as wind turbines and photovoltaic gen-erators, can be connected to the MG via a power electronics inverter. However, the inverter inter-faced RESs reduce the total inertia and damping properties of the traditional MG. Consequently, the system exhibits steeper frequency nadir and the rate of change of frequency (RoCoF), which may degrade the dynamic performance and cause the severe frequency fluctuation of the system. Smart loads such as inverter air conditioners (IACs) tend to be used for ancillary services in power systems. The power consumption of IACs can be regulated to suppress frequency fluctuation. Nevertheless, these IACs, regulating power, can cause the deviation of indoor temperature from the temperature setting. The variation in indoor temperature should be controlled to fulfill residential comfort. This paper proposes a multi-objective decentralized model predictive control (DMPC) for controlling the power consumption of IACs to reduce MG frequency fluctuation and control the variation in indoor temperature. Simulation results on the studied microgrid with the high penetration of wind and photovoltaic generator demonstrate that the proposed DMPC is able to regulate frequency deviation and control indoor temperature deviation as a user preference. In addition, the DMPC has a superior performance effect to the proportional-integral (PI) controller in terms of reducing frequency deviation, satisfying indoor temperature preferences, and being robust to the varying num-bers of IACs.
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Adaptive Output Power Smoothing of Grid-Connected Hybrid Wind-Photovoltaic by SMES
(2020-10-16)
Pahasa, Jonglak
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Ngamroo, Issarachai
This paper proposes an adaptive output power control of superconducting magnetic energy storage (SMES) to solve the output power fluctuation problem of the grid connected hybrid permanent magnet synchronous generators (PMSG) wind and photovoltaic generations (HWPV). By the power control of SMES, the adaptive output power reference is employed to achieve the desired output power of the HWPV while the variation of SMES coil current is regulated between the minimum and maximum limits. Study result shows that the proposed adaptive power control of SMES is able to alleviate the HWPV output power fluctuation, and maintain the SMES coil current in the acceptable range effectively.
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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
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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.
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Coordinated Control of Wind Turbine Blade Pitch Angle and PHEVs Using MPCs for Load Frequency Control of Microgrid
(2016-03-01)
Pahasa, Jonglak
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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
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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.
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Model predictive control-based wind turbine blade pitch angle control for alleviation of frequency fluctuation in a smart grid
(2014-10-15)
Pahasa, Jonglak
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Ngamroo, Issarachai
This paper proposes model predictive control (MPC) for wind turbine blade pitch angle control of a smart grid. 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 blade pitch angle control is used for smoothing of wind power production of wind turbine generator. As a result, the frequency fluctuation in the smart grid system resulted from wind power deviation can be maintained. The simulation results performed in a two-area interconnected power system show that the MPCs-based blade pitch angle of wind turbine generator is capable to reduce frequency fluctuation on the study system.

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