This dissertation presents a novel model predictive control (MPC) for fast frequency control (FFC) in a power system aiming to optimally allocate the power of an energy storage system (ESS) and to effectively mitigate the unknown time-delay switch attack (TDSA) imbedded by adversaries via communication networks. As the penetration of renewable energy sources (RESs) to the power system is extensively deployed, the system inertia can be decreasing. These circumstances will cause the system frequency to be more susceptible to any slight contingencies. To improve the resilience of the power system interfaced by the power converters, the ESSs are utilized and being controlled in such a way that they have the capabilities of maintaining the frequency from any disruptions. FFC is a solution, which can address this problem. Nevertheless, since the ESSs are limited power resources, their specifications are included in the control design to obtain the feasible solutions so as to improve control performance. MPC has received great attention from scholars to be applied as FFC in power system since it is more beneficial than other control strategies in taking into account the input and output constraints. Furthermore, the modern power system enables an advanced communication infrastructure, letting the measured data from phasor measurement unit (PMU) and control commands from control center communicate over the networks. This possesses challenging problems, since the networks are more prone to generate the communication delay or other unknown source of time-delay, which is deliberately injected by adversaries to destabilize the power system. The situation becomes even worse when the unknown TDSA is excessively long and exceeds the critical time-delay, leading to power system instability. To overcome these problems, an improved MPC, synthesizing the super-twisting algorithm time-delay estimator (STA TDE) and sequential state predictor (SSP), is designed to accurately estimate and effectively counteract the unknown and random TDSA. Also, the proposed method is able to optimally allocate the ESS power within its specified limits. All presented case studies justify the effectiveness of the proposed method.

This dissertation presents a novel model predictive control (MPC) for fast frequency control (FFC) in a power system aiming to optimally allocate the power of an energy storage system (ESS) and to effectively mitigate the unknown time-delay switch attack (TDSA) imbedded by adversaries via communication networks. As the penetration of renewable energy sources (RESs) to the power system is extensively deployed, the system inertia can be decreasing. These circumstances will cause the system frequency to be more susceptible to any slight contingencies. To improve the resilience of the power system interfaced by the power converters, the ESSs are utilized and being controlled in such a way that they have the capabilities of maintaining the frequency from any disruptions. FFC is a solution, which can address this problem. Nevertheless, since the ESSs are limited power resources, their specifications are included in the control design to obtain the feasible solutions so as to improve control performance. MPC has received great attention from scholars to be applied as FFC in power system since it is more beneficial than other control strategies in taking into account the input and output constraints. Furthermore, the modern power system enables an advanced communication infrastructure, letting the measured data from phasor measurement unit (PMU) and control commands from control center communicate over the networks. This possesses challenging problems, since the networks are more prone to generate the communication delay or other unknown source of time-delay, which is deliberately injected by adversaries to destabilize the power system. The situation becomes even worse when the unknown TDSA is excessively long and exceeds the critical time-delay, leading to power system instability. To overcome these problems, an improved MPC, synthesizing the super-twisting algorithm time-delay estimator (STA TDE) and sequential state predictor (SSP), is designed to accurately estimate and effectively counteract the unknown and random TDSA. Also, the proposed method is able to optimally allocate the ESS power within its specified limits. All presented case studies justify the effectiveness of the proposed method.

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