In the power demand side, responsive loads can provide fast regulation and ancillary services as reserve capacities in interconnected power systems. This paper presents a distributed pinning demand side control (DSC) strategy for coordinating multiple load aggregators, i.e., aggregated responsive loads, to provide frequency regulation services. Specifically, a leader-following communication protocol is considered for the load aggregators in which there is a centralized pinner (leader) and multiple load aggregators (followers). The regulation objective is generated from the pinner and only shared with a small fraction of load aggregators. Moreover, a multi-step algorithm is proposed to determine the control gains in the DSC, which not only guarantees the stability of the close-loop system, but also restrains the plant disturbance. Furthermore, the distributed pinning DSC algorithm is integrated into the traditional centralized proportional-integral-based automatic generation control (AGC) framework, which has formed the coupled secondary frequency control structure. It has been shown that the total power mismatch in each control area is shared with both AGC units and load aggregators, and the system frequency can be improved by considering the distributed pinning DSC for load aggregators. Finally, simulation results are provided to demonstrate the effectiveness of the proposed coupled frequency control strategy.

The load frequency control (LFC) system, a critical component maintaining frequency stability in the smart grid, is vulnerable to invisible false data injection attacks (FDIAs). These FDIAs can get behind the bad data detection (BDD) system and cause significant damage to the smart grid. Firstly, This paper proposes a detection and defense model against unobservable FDIAs in the LFC system based on the combination of the attack-detection evolutionary game (AEG) model and Kalman filtering (KF) algorithm. Then, Support vector machines (SVM) and K-Nearest neighbor (KNN) are two detection algorithms of the AEG model that are trained by gathering historical data of frequency deviation, tie-line power deviation, and active power load deviation in the smart grid responding to two different forms of FDIAs. The optimal detection algorithm is provided by analyzing the evolution of the equilibrium point of the game. Finally, a mitigation method based on the KF algorithm is proposed, in which the optimal control signal is estimated and issued to the LFC system in order to restore the system frequency stability. Simulation results on a two-area interconnected power system demonstrate the effectiveness of the proposed detection and defense strategies.