In recent years, distributed photovoltaic (PV) systems have experienced accelerated deployment in distribution networks, driven by inherent advantages. However, high-penetration PV integration introduces critical operational challenges, including voltage violations and reverse power flows, which become progressively more pronounced as penetration levels increase and may ultimately harm user interests. To comprehensively assess the network's PV hosting capacity (PVHC) while safeguarding the interests of both PV owners and load consumers, this article proposes a novel stochastic biscenario (prefault and postfault) assessment framework, which explicitly quantifies and constrains postfault PV curtailment and load shedding, mediated by battery energy storage systems. We derive an analytical linearized expression for the nonlinear PV curtailment under all N-1 contingencies, transforming the original PVHC assessment model into a tractable mixed-integer linear programming model. Furthermore, our analysis reveals that, an inherent tradeoff exists between reducing fault-induced PV curtailment and enhancing load reliability in storage-integrated grids due to state of charge constraints, which holds significant guiding importance for the practical industrial application of PV integration. Finally, the effectiveness, and scalability of the proposed method are verified through different test systems.

With the continuous integration of distributed generations and flexible loads, accurate real-time forecasting-aided state estimation (FASE) is increasingly crucial for the safe operation of distribution systems. However, compared to the static state estimator that utilizes information from only a single time instance, the Kalman filter (KF)-based FASE, while capable of leveraging predicted information to enhance state tracking performance, faces challenges in practical application due to heavy computational cost. This paper proposes a complex domain constant Jacobian matrix FASE method, which enables fast and robust state estimation for distribution systems. Moreover, a corresponding massively parallel processing scheme is constructed utilizing low-power field-programmable gate arrays (FPGAs) to further enhance computational efficiency, ensuring the reliability of faster-than-real-time (FTRT) hardware-in-the-loop (HIL) applications. Case studies conducted on single-phase IEEE 33-bus as well as three-phase unbalanced IEEE 13-and 123-bus distribution systems with real-world load and DG profiles validate the advantages of the proposed method.

This paper develops a robust variational Bayesian unscented Kalman filter (RVBUKF) to track the state of active distribution networks with distributed energy resources. First, a spherical simplex-based unscented transform (UT) strategy is used to select Sigma points to reduce the computational burden. Subsequently, based on unscented Kalman filter and variational Bayesian theory, a variational update form of the covariance matrix is derived by using the inverse Wishart probability density function. To mitigate the impact of outliers, the Gaussian process regression model trained by historical data is used to synchronously predict observation values to replace abnormal measurements. Finally, simulation experiments are conducted on the distribution system containing distributed generations (DGs). The numerical results demonstrate that the developed method can accurately track state changes in uncertain scenarios while reducing the computational burden of UT by almost half.

ECS-Grid is the first data-oriented real-time electromagnetic transient simulation platform for cyber-physical power systems (CPPS). Traditional simulation tools are constrained by object-oriented programming (OOP) architecture, which is now a significant obstruction to creating a comprehensive cyber-physical simulation. Therefore, the proposed ECS-Grid platform follows a new data-oriented paradigm based on an entity-component-system (ECS) framework, which delivers higher flexibility, extensibility, scalability, and performance to support cyber-physical system research. ECS-Grid proposes a layer of virtual intelligent electronic devices (vIEDs) to model IEDs in CPPSs. The vIEDs directly talk to physical components and communicate asynchronously with cyber services via the proposed high-performance JSON-like binary protocol. Tests with the islanding and the man-in-the-middle cyberattack scenarios on a 711-node ac–dc microgrid cluster based on a modified CIGRE 15-Bus system are performed and give accurate results. A faster-than-real-time performance is achieved on the 10th Gen Intel Core TM i7 computer, and real-time performance is achieved on distributed embedded NVIDIA Jetson platform. The ECS-Grid design and test results demonstrate the potential of the ECS data-oriented paradigm and may inspire the renovation of industrial simulation software.

In the wide area measurement system (WAMS) of smart grid, the real-time monitoring and protection applications have stringent requirements for the end-to-end transmission delays between phasor measurement units and phasor data concentrator, and fast failover (FF) is required to ensure the communication performance after link failures. In this work, the software-defined network (SDN) technology is utilized to enable datapath failover upon a link failure with a global view of the communication network. Then, a novel dataplane-based fast failover (DFF) mechanism is proposed todirectlyreroute the data packet in dataplane without interacting with the SDN controller. Based on the mathematical analysis over the WAMS topology features, the proposed DFF optimizes two procedures of failover: backup path construction and backup path installation. Using the proposed backup path construction algorithms, the 3-approximate and (1+2$\varepsilon$)-approximate ($0< \varepsilon < 1$) backup paths can be constructed, theoretically guaranteeing the data transmission delays bothduringandafterfailover. Using the proposed LinkID-based FF group table installation method, the conflict of forwarding rules between original and backup paths can be eliminated, while the storage cost is also optimized. The simulation results on six IEEE benchmark test power systems show that the proposed DFF mechanism could achieve lower data transmission delays during and after failover compared with the existing control plane based and dataplane-based failover mechanisms.

This paper proposes digital hardware building block concept for emulating power system networks on field-programmable gate arrays (FPGAs) in real time. Basic hardware emulation building blocks (HEBBs) for machines, transmission lines, nonlinear elements, and loads are presented to demonstrate how real-time simulation can be achieved for realistic power systems. All of the hardware modules were developed using the VHDL language for portability and extensibility. Employing multiple FPGAs, a large-scale power system consisting of 1260 three-phase buses is modeled in detail in real time to show both electromagnetic transients and electromechanical dynamics in the system. The advantage of HEBB-based modeling is that the design and development of new technologies can be accelerated by utilizing massive real-time digital simulators capable of modeling multiscale and multidomain dynamics.

Accurate models of power electronic devices are necessary for hardware-in-the-loop (HIL) simulators. This paper proposes a digital hardware emulation of device-level models for the insulated gate bipolar transistor (IGBT) and the power diode on the field programmable gate array (FPGA). The hardware emulation utilizes detailed physics-based nonlinear models for these devices, and features a fully paralleled implementation using an accurate floating-point data representation in VHSIC hardware description language (VHDL) language. A dc-dc buck converter circuit is emulated to validate the hardware IGBT and diode models, and the nonlinear circuit simulation process. The captured oscilloscope results demonstrate high accuracy of the emulator in comparison to the offline simulation of the original system using Saber software.

This paper proposes large-scale transient stability simulation based on the massively parallel architecture of multiple graphics processing units (GPUs). A robust and efficient instantaneous relaxation (IR)-based parallel processing technique which features implicit integration, full Newton iteration, and sparse LU-based linear solver is used to run the multiple GPUs simultaneously. This implementation highlights the combination of coarse-grained algorithm-level parallelism with fine-grained data-parallelism of the GPUs to accelerate large-scale transient stability simulation. Multithreaded parallel programming makes the entire implementation highly transparent, scalable, and efficient. Several large test systems are used for the simulation with a maximum size of 9,984 buses and 2,560 synchronous generators all modeled in detail resulting in matrices that are larger than 20, 000 × 20, 000.