Abstracts:This letter uses sparse matrix statistics to highlight a structural gap between North American grid models and the synthetic cases commonly used in research. North American grids exhibit area-based modularity, characterized by dense intra-area links and limited inter-area connectivity. By contrast, synthetic grids often over-mesh across areas. Analysis shows that these surplus tie lines increase fills in the Jacobian factorization, which reduces realism and computational efficiency. The observations confirm that inter-area connectivity influences sparse matrix behavior. This work emphasizes the importance of incorporating modular structure into synthetic grid design.

Abstracts:The increasing penetration of renewable energy sources (RESs) reduces system inertia and the availability of primary frequency response (PFR), leading to deeper frequency nadir (FN). Therefore, accurate estimation of the FN has become a key challenge in power system analysis. While traditional approaches have analyzed FN reduction with increasing RES penetration, they often rely on simplified or linearized models, which limit their accuracy under transient conditions. This study introduces an energy-based formulation in which the power imbalance between a disturbance and the frequency response is systematically analyzed in terms of energy. This formulation directly relates FN formation to the energy mismatch between the load demand and the energy supplied by generating units. Accordingly, the FN is estimated under varying RES penetration by incorporating inertia, PFR, and the energy mix. In addition, the formulation is applied to determine the extra frequency support required to maintain the FN above a secure level.

Abstracts:The traditional prediction methods primarily emphasize accuracy. This letter presents an end-to-end training framework that integrates a convex hull support function with the Mean Squared Error (MSE) for cost-focused learning. Specifically, by introducing a convex hull loss, decision cost information is incorporated into the model's learning process, while the MSE term ensures that the predicted results align with the actual distribution. Experimental results on the IEEE-33 bus system demonstrate that the proposed method achieves a 12.4% improvement in cost reduction compared with traditional forecasting methods. Furthermore, the proposed approach significantly improves training efficiency compared with other decision-focused baselines. The proposed framework provides a generalizable paradigm for cost-aware forecasting under renewable uncertainty.

Abstracts:Efficient monitoring of center of inertia (COI) frequency is critical for maintaining system frequency stability. Numerous studies have demonstrated the possibility of monitoring COI frequency through phasor measurement units (PMUs) placed at system buses. Nevertheless, the optimal placement of PMUs, especially when limited devices are available, is rarely investigated. In this letter, a systematic strategy is designed to determine the locations and numbers of PMUs. The formula governing the linear relationship between the COI frequency and few bus frequencies is firstly derived. In order to evaluate the monitoring accuracy for different numbers and locations of PMUs, a L2 norm-based performance metric is proposed. Further, the closed-form solution of linear coefficients corresponding to a certain PMU placement is deduced to minimize the performance metric. The optimal placement is finally determined by selecting the best performance metric among different placement combinations. The proposed method ensures that the complete dynamics of COI frequency can be monitored accurately with few PMUs deployed at system buses, which is tested in the IEEE 39 bus system and the IEEE 118 bus system.

Abstracts:This paper proposes a compositional grid code framework that guarantees power system stability and dynamic performance simultaneously. By reformulating device-grid interactions based on passivity theory, we derive compositional stability indices compatible with heterogeneous dynamics. Then, the proposed grid codes decompose system-level stability certificates into quantitative constraints upon individual devices while accommodating standard dynamic specifications. Compared with existing approaches, this method overcomes the previous limitations in closed-loop stability assurance and facilitates the plug-and-play integration of heterogeneous dynamic devices.

Abstracts:Identifying anomalies in Phasor Measurement Unit (PMU) data is of critical importance. Existing methods can be broadly categorized into model-based and data-driven ones. The former requires an accurate system model, while the latter resorts to specific statistical assumptions, such as the Gaussian, symmetric, or single-mode, etc, which may not hold in practice. In view of this, we propose a data-driven anomaly identification method using the fourth-order cumulant. Its tensor structure can intrinsically preserve the tail characteristics of PMUs data, thereby inherently revealing latent anomalies without assuming any statistical distributions. Additionally, we incorporate Copula to distinguish anomalies from system events or measurement outliers. Simulation results on real-world PMU data demonstrate the excellent performance of the proposed method.

Abstracts:DC Optimal Power Flow (DC-OPF) problems optimize the generators’ active power setpoints while satisfying constraints based on the DC power flow linearization. The computational tractability advantages of DC-OPF problems come at the expense of inaccuracies relative to AC Optimal Power Flow (AC-OPF) problems that accurately model the nonlinear steady-state behavior of power grids. This paper proposes an algorithm that significantly improves the accuracy of the generators’ active power setpoints from DC-OPF problems with respect to the corresponding AC-OPF problems over a specified range of operating conditions. Using sensitivity information in a machine learning-inspired methodology, this algorithm tunes coefficient and bias parameters in the DC power flow approximation to improve the accuracy of the resulting DC-OPF solutions. Employing the Truncated Newton Conjugate-Gradient (TNC) method, a Quasi-Newton optimization technique, this parameter tuning occurs during an offline training phase, with the resulting parameters then used in online computations. Numerical results underscore the algorithm’s efficacy with accuracy improvements in squared two-norm and $\infty$-norm losses of up to 90% and 79%, respectively, relative to traditional DC-OPF formulations.

Abstracts:Data centers play a pivotal role in supporting digital transformation. However, they are among the most energy-intensive infrastructures as a result of increasing global computing load over the years. As data center power consumption continues to increase, examining their dynamic performance and interaction with the electrical grid is essential to enhance efficiency and ensure grid stability. This paper presents a comprehensive dynamic model of a 10 MW data center, incorporating 44,250 servers with a tandem hybrid heating ventilation and air-conditioning (HVAC) system to simulate real-world data center under varying workloads. The developed model is validated using experimental data that demonstrate high accuracy in predicting server power consumption, heat generation, and dynamic temperature. Furthermore, to verify the fidelity of the model to operate in dynamic power systems, the data center model is connected to WSCC 9-bus test system to analyze the impact of various grid disturbances on the data center operations, such as load variations and three-phase short-circuit faults. The model can be scaled up and is valuable for improving the accuracy of dynamic stability analysis of power systems under high integration of data center load. In addition, the model is proposed to provide a practical framework for assessing compliance with fault ride-through (FRT) requirements and enabling demand response participation.

Abstracts:This paper proposes an attack-resilient distributed secondary controller for microgrids. It is designed to restore frequency even in the presence of compromised distributed generators (DGs). The proposed algorithm is capable of detecting and compensating the effect of various types of attack (e.g., scale, replay, and data integrity attacks) on DGs by utilizing two-hop communication information. The proposed method consists of two key stages: detection and compensation. In the detection stage, each DG’s controller uses its own data, information from neighboring DGs, and three attack detection strategies to identify compromised DGs. Subsequently, in the compensation stage, each DG’s controller determines the effect of attacks on the consensus control law and generates a compensation signal accordingly. In instances where attacks are not compensable, compromised DGs are isolated to ensure a consensus. The effectiveness of the proposed method is demonstrated over a modified IEEE 16-bus test system in MATLAB/Simulink. The simulation results show that the proposed method is resilient to various types of attacks, including scaling attacks, false data injection (FDI) attacks, and replay attacks.

Abstracts:This article proposes a hierarchical site selection framework of grid-forming energy storage (GFM-ESS) devices. This framework enhances stability of both the device itself and the power system across multi-timescales with respect to static, dynamic, and transient stability layouts. Specifically, the first layout optimizes static voltage stability margin and system losses. The second layout evaluates small-signal stability based on the generalized short-circuit ratio (gSCR). Finally, the third layout maximizes the GFM-ESS supporting performance in response to the system dynamics. A case study based on Western China serves to validate the effectiveness of the proposed GFM-ESS site selection strategy and to prove its scalability for large real-world power systems.