Cyber-attack is recognized as an emerging risk in smart grids. In this paper, we reveal a potential link between data attack and physical consequences and analyze how the attacker can launch a malicious data attack to trigger sequential outages and thus impose large damages to the grid. In this attack mechanism, the attacker constructs an optimal false data injection (FDI) attack to intentionally trigger a targeted branch outage sequence that trips multiple branches and then leads to subsequent failures. The studied attack mechanism integrates constructing an optimal data attack and identifying critical lines, and imposes a substantial security impact with a high chance of occurring. Simulations on the IEEE 118-bus system verify the introduced attack mechanism and highlight the risk of such attacks in today's smart grids.

Power systems are facing the challenges of integrating massive electric vehicles (EVs) and lacking operational flexibility. An optimal and efficient coordination strategy is needed to coordinate the operations among EV charging stations (CSs) and the distribution system operator (DSO) and to make full use of the EVs’ charging flexibility for improving the operational flexibility of power systems. To this end, this paper proposes a CSs-DSO distributed coordination strategy. The proposed strategy integrates a supply-curve-based quantification method to quantify the EVs’ flexibility contribution and a Nash bargaining game-theory-based peer-to-peer (P2P) transactions model for maximizing the profits of entities participating in the coordination. To tackle the CSs-DSO coordination problem that has a bi-level-parallel coupling structure, an analytical target cascading (ATC) and alternating direction method of multipliers (ADMM) jointly-based distributed solution approach is proposed. First, the CSs-DSO bi-level model is decoupled by ATC with tie-line power being the only exchanged information; then, the model is further decomposed for each CS by ADMM for protecting the CSs’ privacy and autonomy. The effect of coordination and the computational efficiency of the proposed strategy is verified by numerical simulations on a modified IEEE 33-bus distribution system connecting multiple CSs.

The widespread adoption of communication and control infrastructures will not only improve the microgrid system performance in normal conditions but also increase microgrid cybersecurity risks. Potential cyberattacks can deteriorate microgrid performances by corrupting and intercepting data exchanges among participating DERs, whereby microgrids deviate from desired operating conditions and stable microgrid operations are jeopardized. In this paper, a cross-layer control strategy is proposed to enhance the microgrid resilience against false data injection (FDI) and denial of service (DoS) attacks. On the one hand, the proposed control strategy will not interfere with microgrid normal operations when there are no cyberattacks. On the other hand, the proposed control strategy can effectively mitigate the impacts of FDI and DoS attacks on microgrids without relying on prompt detection and isolation of cyberattacks. The stability of the proposed control strategy is demonstrated using the Lyapunov theory under different scenarios, including without and with FDI and DoS attacks. The effectiveness of the proposed cross-layer resilient control strategy against cyberattacks is validated in a 12-bus microgrid system using time-domain PSCAD/EMTDC simulations.

Multi-agent deep reinforcement learning (MADRL) has been applied to EV charging scheduling. However, it relies on centralized training and thus is significantly challenged by privacy, communication and computing concerns. To address this issue, this letter proposes a novel distributed training and distributed execution (DTDE) framework-based fully-distributed Stackelberg multi-agent deep reinforcement learning (SMADRL) approach for EV charging scheduling. It models the agents’ asymmetry competition as a novel Stackelberg Distributed Partially-Observable Markov Decision Process (SDis-POMDP). This addresses the privacy and communication concerns as the agents do not need to share local observations, enhances the computational efficiency and scalability as all calculations are conducted at local agents, and does not rely on high-precision system modeling which is required by model-based methods. The effectiveness and efficiency of the proposed strategy are verified by comparing its performance against multiple benchmarks.

This letter describes an enhanced multi-period dispatch model for microgrids, in which frequency-aware islanding constraints are established to ensure microgrids with the capability to ride through unplanned islanding events. Rather than specifying the upward/downward primary reserve requirements as fixed amounts, the proposed dispatch scheme determines the primary reserve requirements by explicitly simulating under- or over-frequency dynamics caused by potential islanding events. To remove the nonlinearity, a two-stage solution strategy is proposed. Case studies demonstrate the effectiveness of the proposed microgrid dispatch scheme.

Efficient real-time management of electric vehicle (EV) charging in a charging station (CS) is vital to the integration of large-scale EVs in power grids. It faces critical challenges such as frequent changes in the grid’s dispatch commands, the complexity of EVs’ costs, and the uncertainties in the EVs’ charging/traveling behaviors and in the future dispatch commands. To tackle these challenges, this paper proposes a deep reinforcement learning (DRL)-based allocation approach that optimally and efficiently allocates the grid’s commands to the EVs and controls their charging in real time. It includes two stages. Stage 1 includes a data-driven EV cost quantification method, which efficiently quantifies the EVs’ flexibility contributions with long-term return consideration. Stage 2 proposes a high sample efficiency DRL-based allocation method, which optimizes the EVs’ charging and addresses the EV- and grid-related uncertainties. The proposed allocation has a fast computational speed. Finally, to address the security risk due to DRL’s stochastic exploratory actions, two safety modules are developed which ensure the EV charging security and the allocation accuracy. The effectiveness and efficiency of the proposed strategy are verified by comparing its performance against multiple benchmark approaches.