Abstracts:In this letter, the instantaneous differential equations of a simple grid-following converter connected to an infinite bus are derived. It is shown that the Root Mean Square model that assumes steady-state relationships between voltages and currents is unable to capture Phase-Locked Loop. The RMS Model Cannot Capture PLL Small-Signal instability. This claim is backed with time-domain simulations, eigenvalue analysis, and by benchmarking the regions of attraction in the phase plane.

Abstracts:Electricity price data demonstrate spike-induced heteroscedasticity and non-stationary market dynamics. To tackle these challenges, this paper proposes a spike-resilient temporal-adaptive neural framework for day-ahead electricity price interval forecasting. The framework integrates a robust Box-Cox transformation with Huber loss to mitigate outliers and heavy tails. It optimizes the parameters of the robust Box-Cox transformation via gradient descent instead of Newton’s method for improved computational efficiency. Additionally, a dynamic weighted moving average algorithm is embedded to construct prediction intervals, adaptively capturing time-varying patterns. Experimental results confirm that the proposed approach outperforms existing benchmarks in electricity price interval forecasting, enhancing predictive reliability and sharpness.

Abstracts:Power flow (PF) calculation is essential for power system analysis. In recent years, data-driven methods have emerged as a promising approach to accelerate PF calculations. However, these methods require high-quality labeled data and often suffer from poor generalization. To address these issues, an unsupervised physics-informed neural network (UPINN) method is proposed for AC PF calculations. The proposed method follows the general process of Newton-Raphson’s method. By minimizing the physics-informed loss function, which is designed based on active and reactive power mismatches, the PF equations will be satisfied directly without the need to calculate the Jacobian matrix’s inverse. Proofs of the proposed UPINN training method’s convergence are provided. Case study results on the IEEE 24-bus and 118-bus systems demonstrate the feasibility of the proposed approach, showing that UPINN’s power flow model can achieve high generalization performance without relying on labeled data.

Abstracts:This letter proposes a failure consequence estimation method on the cyclic coupled interaction network. Compared to the simplified tree-structure based approach, the proposed method is more accurate and efficient, retaining useful information in the adjusted loops. Results on the IEEE 300-bus system show that the mitigation strategy that upgrades the critical components identified by the proposed method greatly reduces the cascading risk, outperforming the tree-based approach.

Abstracts:The generalized Hamiltonian system theory (GHST) is a powerful tool for excitation control in high-dimensional non-linear power systems, but relies on reduced-order dynamics, given the analytical non-tractability of realistic high-order systems and incomplete sub-modules, causing control errors. To address this, a nearly Hamiltonian neural network (NHNN)-based nonlinear excitation control is proposed. This method learns the structured Hamiltonian for each generator from measurements, mitigating Hamiltonian realization errors arising from reduced-order. A global energy function preserving system responses is then presented by assembling these Hamiltonians for stabilization control. Simulations on a dual-machine system justified improved stability compared to GHST and PID rivals, achieving reductions of 19.11% and 27.19% in average overshoot, as well as 27.42% and 44.06% in integral squared error (ISE), respectively.

Abstracts:Accurate estimation of parameters for Distribution Network Feeders (DNFs) is crucial yet quite challenging, especially with limited synchronized measurements. This letter introduces a Γ-Model (HGM) that leverages the circuit properties of DNFs to establish a linear relationship between unknown feeder parameters and unsynchronized terminal measurements. By combining two Γ-models, the HGM effectively mitigates the inaccuracies and biases of simplified models. This model balances the accuracy of the equivalent П-model with the linearity of the short-line and Γ-models. An effective parameter estimation method is developed based on HGM, operating without requiring synchronized data. This method is applicable to both overhead lines and underground cables and is particularly useful in the latter case, where shunt susceptance is more significant. By avoiding iterative solutions, the proposed method ensures convergence and eliminates the risk of multiple outcomes.

Abstracts:Frequency nadir is one of the key indicators for power system frequency security under power disturbances. However, its inherent nonlinearity presents significant challenges in optimization. This letter proposes a new approximation of frequency nadir security constraints, which is accurate, adaptative and easily solvable for frequency-constrained optimization problems. By investigating the analytical frequency nadir expression, we find that, within the range of actual system parameters, the highly nonlinear expression can be accurately approximated by a linear expression through variable substitutions. This approximation can be used to formulate second-order cone (SOC) constraints that commercial solvers can efficiently handle. Compared to existing methods, the proposed method provides a global and accurate approximation without increasing the complexity of optimization model.

Abstracts:The power system, as vital national infrastructure, is confronted with increasingly severe external attack threats. Current research has primarily focused on defensive layouts and loss analyses in the context of specific attack levels, neglecting the unpredictability of attacker's capability and lacking an endurance safety assessment for the power system. Compared to mainstream studies, the work reported in this paper presents a novel analytical scenario where a regional power system experiences complete failure due to the physical attack, aiming to develop a holistic vulnerability assessment method for localized power grids facing unknown attacker capability. The proposed method specifically establishes an Attack-Defense game model including two distinct objective functions with interdependent decision-making to maintain the resistance characteristic of the defender. Besides, based on the characteristics of the model, a customized method is proposed to solve and validate the original optimization problem through permutation problem. Simulation results based on the IEEE 14-bus and 118-bus systems verified the correctness of the proposed models. And a comparative analysis of various resource allocation schemes for national territorial defense has been conducted to validate the effectiveness of the assessment methodology.

Abstracts:Power system oscillations are a significant concern for system operators, a problem that has grown due to the interconnection of inverter-based resources. To address this issue, various methods have been proposed to locate the sources of oscillations, which is essential for effective mitigation actions. A common characteristic of these methods is that they rely on phasor representation of oscillation phenomena. This paper takes a different approach by examining the actual voltage and current waveforms underlying the phasors. It is found that the presence of interharmonic components is both the necessary and sufficient condition for phasor oscillations. Oscillation is the appearance of a beating waveform viewed from the phasor domain, and the beating waveform is created by interharmonics interacting with the fundamental frequency wave. As a result, the generation and propagation of interharmonics are the general cause of oscillation phenomena. Based on these insights, two new methods are developed for locating oscillation sources: one for measurement-based monitoring applications and another for model-based system studies. These findings are validated through four field data-based and one simulation-based case studies.

Abstracts:Due to the significant uncertainty in photovoltaic (PV) power generation, grid operation scenarios with a high proportion of PV integration are complex and varied. To accurately extract representative scenarios for PV power generation, this paper proposes a novel clustering model that simultaneously considers PV power, energy, and variability. Compared to traditional clustering models that rely on Euclidean distance, the proposed clustering model not only takes into account the Euclidean distance, but also incorporates the daily PV power generation and the characteristics of PV power curves, enabling a more accurate quantification and analysis of the impact of PV on the electricity networks. To solve the proposed clustering model, an alternating optimization algorithm is proposed, based on linear optimization, Lagrange multipliers, and eigenvalue decomposition. The highlights of this paper are the dual verification of the proposed method through theoretical proof and simulation examples. Theoretically, the computational complexity of the algorithm is illustrated, and the convergence of the algorithm is demonstrated. The proposed method is tested using real PV data from Australia and the IEEE 69-bus system, successfully generating 13 representative PV generation scenarios with a maximum similarity distance of the morphological trend as low as 0.3062, ensuring the most representative PV generation peak times.