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In the realm of power systems, load redistribution attacks (LRAs) involve the manipulation of measurements by attackers, leading operators to make decisions based on falsified estimated loads. Detecting LRAs is a well-explored area, but the critical post-detection phase, which requires operators to understand the attack's nature, remains under-addressed. Our paper introduces a novel probabilistic mechanism based on the Bayesian network model. It excels at not only detecting affected areas but also pinpointing the buses with manipulated loads under LRAs. Our work prioritizes simplicity by offering a straightforward calculation method that doesn't rely on complex tools or extensive system parameters. It also exhibits robustness against measurement noise. Acknowledging the paramount importance of data recovery, we highlight that post-detection measures are essential for minimizing errors in optimal power flow calculations. However, this data recovery is challenging due to the complexity of understanding the attacker's behavior. Our methodology employs kernel density estimation for precise probability density calculations and integrates the Bayesian Markov model to reduce computational complexity. Rigorous testing on the IEEE 118 bus system demonstrates its strong performance in producing consistently satisfactory results.

The fluctuated power output of renewable energy sources brings new challenges to frequency control, especially for islanded microgrids with small spinning reserves. However, energy storage systems and widespread flexible loads can be employed to the frequency regulation thanks to their flexibility of power outputs. This paper investigates the frequency regulation problem for islanded microgrids with distributed heterogeneous energy storage systems (HESS) composed of battery energy storage systems (BESS) and building thermal energy storage systems (BTESS). A distributed event-triggered balanced power sharing strategy considering denial of service (DoS) attacks is designed for frequency regulation by allocating HESS power according to BESS state of charge (SoC), BTESS state of temperature (SoT) and their capacities. The range of control parameters for a stable controller are all provided by Lyapunov analysis. Moreover, the frequency feedback control gain for HESS is derived by using linear quadratic regulator. Simulation results show that the proposed frequency regulation strategy can guarantee the recovery of microgrids frequency and the proportional sharing of HESS power. Besides, SoC and SoT balancing with fewer communications are achieved, even with considering various parameters of HESS, such as capacity, efficiency and with communication link failures as well as DoS attacks.