Electric buses (EBs) possess large-capacity batteries and are dispatched by a central operator, exhibiting great potential to enhance the resilience of distribution systems (DSs) against meteorological disasters. This paper proposes an optimization model for joint post-disaster DS restoration, considering coordinated dispatching with EBs. By assuming that the DS can rent some EBs from the bus company, an EB scheduling problem with adjustable timetables is established. Idle buses are placed at designated areas and feed power back to the grid via charging piles or charging stations in case of need. The schedule of the remaining buses should meet the passenger transport demand, which is smaller than usual because of bad weather. The objective is to maximize the total benefits and minimize the EB rental cost of the grid company. Techniques in integer algebra are used to reformulate the proposed restoration problem with bus scheduling constraints as a mixed-integer linear program, which can be processed by off-the-shelf solvers. The proposed method is tested on a modified 15-bus system and IEEE 123-bus system. The results demonstrate that the resilience of the system is enhanced, and the benefits of the grid company increase significantly, because of the flexibility brought by EBs.

State estimation is a fundamental function in modern energy management system (EMS), but its results may be vulnerable to false data injection attacks (FDIA). FDIA is able to change the estimation results without being detected by the traditional bad data detection algorithms. In this paper, we propose an accurate and computational attrac-tive approach for FDIA detection. We first rely on the low rank characteristic of the measurement matrix and the sparsity of the attack matrix to reformulate the FDIA detection as a matrix separation problem. Then, three algo-rithms that solve for this problem are presented and com-pared, including the traditional Augmented Lagrange Mul-tipliers (ALM), the Low Rank Matrix Factorization (LMaFit) and the proposed new Go Decomposition (GoDec). Numerical simulation results show that our GoDec outperforms the other two alternatives and demon-strates a much higher computational efficiency. Furthermore, GoDec is shown to be able to handle measurement noise and applicable for large-scale attacks.

Electric buses (EBs) have been widely adopted in public transit. EBs can feed power back to the distribution system (DS) at a charging station and serve as temporary mobile power sources (MPSs), such as hurricanes. Thus, they have great potential to enhance grid resilience against meteorological disasters. This article proposes a DS restoration method for enhancing resilience considering the routing and scheduling of a group of EBs. To maintain the function of EBs in public transit, we establish an EB scheduling problem (EBSP) with predetermined optional service trips. The schedule must meet a certain public transit demand, and the not-in-service EBs can transfer power among charging stations, unleashing their abilities as MPSs. The proposed model considers the en-route energy consumption of EBs, and the restoration process aims to minimize the losses of loads over time and the energy usage of EBs for supporting the grid. The problem is finally cast as a mixed-integer linear program (MILP), which can be processed by off-the-shelf solvers. The proposed model is tested on two systems with dozens of EBs. The results validate our work’s effectiveness and demonstrate the potential of EBs in enhancing the resilience of DSs.

Since modern smart grids have various and deeply coupled cyber-physical components, they are vulnerable to malicious cyber attacks. Although regular defenses including firewall and IDS are deployed, they may be weakened by zero-day vulnerabilities and sophisticated attack schemes. Therefore, defense strategies to mitigate the risk of blackouts during cyber attacks are necessary. This paper proposes a cyber-physical coordinated defense strategy to overcome the disruption and minimize the risk as much as possible. At the cyber layer, a zero-sum multilevel Markovian Stackelberg game is proposed to model sequential actions of the attacker and the defender. The defender distributes defensive resources to protect lines in a real-time manner, according to the attacker's action. If cyber attacks should result in physical outages, defense at the physical layer is then employed. A security-constrained optimal power flow reserving security margin of critical components will be performed to minimize the blackout scale and potential future risk. To solve the corresponding optimization problem and further get the optimal defense strategy, this paper devises a novel “water-pouring” algorithm. Lastly, test results show that the proposed dynamic defense strategy mitigates risk significantly and outperforms existing methods.

Electromagnetic transient (EMT) simulation is critical and fundamental in the design and operation of the modular multilevel converter (MMC). This article proposes a highly efficient EMT simulation model for MMC based on hybrid numerical integration. The topology and operational principle of MMC are elaborated first. Based on this, an efficient simulation model for MMC is proposed. Each arm of the MMC is reduced to a two-node Norton equivalent circuit in the main network simulation. The computation of the capacitor dynamic equations is decoupled from that of the arm inductor dynamic equation. They are computed in a leapfrog manner. This makes the equivalent conductance of the MMC arm constant and greatly improves the simulation efficiency. Moreover, the dynamic equations of the capacitors are also decoupled from each other. Various operational conditions of the MMC are also considered, and accuracy and adaptability are thus ensured. Extensive studies on power systems with different scales verify the accuracy and improved efficiency of the proposed EMT simulation model for MMC.

In the context of high penetration of renewables, the need to build dynamic models of power system components based on accessible measurement data has become urgent. To address this challenge, firstly, a neural ordinary differential equations (ODE) module and a neural differential-algebraic equations (DAE) module are proposed to form a data-driven modeling framework that accurately captures components' dynamic characteristics and flexibly adapts to various interface settings. Secondly, analytical models and data-driven models learned by the neural ODE and DAE modules are integrated together and simulated simultaneously using unified transient stability simulation methods. Finally, the neural ODE and DAE modules are implemented with Python and made public on GitHub. Using the portal measurements, three simple but representative cases of excitation controller modeling, photovoltaic power plant modeling, and equivalent load modeling of a regional power network are carried out in the IEEE-39 system and 2383wp system. Neural dynamic model-integrated simulations are compared with the original model-based ones to verify the feasibility and potentiality of the proposed neural ODE and DAE modules.