The integration of plug-in hybrid electric vehicles (PHEV), photovoltaic (PV) generators, and energy storage systems (ESS) into microgrids is highly anticipated. A coordinated control of PHEVs, PVs, and ESS will support frequency control in a microgrid. However, the size of ESS depends on the surplus power of PV. The lower the surplus power is, the smaller the size of ESS. Furthermore, the number of available PHEVs vary with the cumulative number of the participating PHEVs. This variation of the number of PHEVs may reduce the PHEVs’ control effect in the microgrid. This paper proposes a coordinated control of PHEVs, PVs, and ESS for frequency control in the microgrid using a centralized model predictive control (CMPC) considering the variation of PHEV numbers. The objectives of the coordinated control are 1) to suppress the system frequency fluctuation, and 2) to minimize the surplus power of PV and therefore reduce the size of ESS. Simulation studies indicate that the coordinated control of PHEVs, PVs, and ESS by the proposed CMPC is superior to that of the proportional integral derivative control and the distributed MPC in terms of minimizing the frequency fluctuation, the PV surplus power, and the ESS size.

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.

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.

Proton exchange membrane electrolyzers (PEMEL) installed with renewable energy resources can be used for power system ancillary services such as frequency regulation and virtual inertia emulation. However, the performance of PEMEL-based frequency ancillary services is threatened by cyberattacks that compromise the communication control. Considering denial-of-service (DoS) attacks on PEMEL communication, this paper proposes an enhanced resilient model predictive control (ER-MPC) for a PEMEL controller. The proposed ER-MPC consists of two procedures. First, the combination of autoregressive (AR) model-based prediction method and hold signal method is used to reconstruct attacked signals during severe DoS attacks. Then a model predictive control (MPC) is used to compute the control signal for PEMEL stack. The objective of ER-MPC-based controlling of PEMEL is to regulate the frequency deviation during contingency and normal operation under severe DoS attack. The effectiveness of the proposed ER-MPC was compared with that of AR-based resilient MPC and resilient MPC methods. The simulation results revealed that the proposed ER-MPC successfully improved microgrid frequency regulations under severe DoS attacks. In addition, the proposed ER-MPC-based PEMEL has a performance effect over other techniques in terms of the reduction in frequency deviation and the rate of change of frequency during severe DoS attacks, disconnection, and successful connection of wind turbine generation.

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 generators, can be connected to the MG via a power electronics inverter. However, the inverter interfaced 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 numbers of IACs.