Open Access

ARTICLE

Energy-Based Approach for Short-Term Voltage Stability Analysis and Assessment

Wenbiao Li^1,2, Zhichong Cao^1,*, Zhengyu Li³, Wenbiao Tao³, Cheng Liu¹, Yuxin Shi³, Rundong Tian¹

1 School of Electrical Engineering, Northeast Electric Power University, Jilin, 132012, China
2 State Grid Shanxi Electric Power Company, Taiyuan, 030002, China
3 State Grid Shanxi Electric Power Company Electric Power Research Institute, Taiyuan, 030002, China

* Corresponding Author: Zhichong Cao. Email:

(This article belongs to the Special Issue: Integration of Renewable Energies with the Grid: An Integrated Study of Solar, Wind, Storage, Electric Vehicles, PV and Wind Materials and AI-Driven Technologies)

Energy Engineering 2025, 122(11), 4733-4754. https://doi.org/10.32604/ee.2025.068683

Received 04 June 2025; Accepted 16 July 2025; Issue published 27 October 2025

Abstract

With the increasing penetration of renewable energy in power systems, grid structures and operational paradigms are undergoing profound transformations. When subjected to disturbances, the interaction between power electronic devices and dynamic loads introduces strongly nonlinear dynamic characteristics in grid voltage responses, posing significant threats to system security and stability. To achieve reliable short-term voltage stability assessment under large-scale renewable integration, this paper innovatively proposes a response-driven online assessment method based on energy function theory. First, energy modeling of system components is performed based on energy function theory, followed by analysis of energy interaction mechanisms during voltage instability. To address the challenge of traditional energy functions in online applications, a convolutional neural network-long short-term memory (CNN-LSTM) hybrid artificial Intelligence approach is introduced. By quantifying the contribution of each energy component to voltage stability, key energy terms are identified. The measurable electrical quantities corresponding to these key energies serve as inputs, while the energy at the voltage unstable equilibrium point (UEP) obtained from offline simulations is used as both the energy threshold and the output of the artificial intelligence model, enabling the construction of an artificial intelligence model for energy threshold prediction. The measurable electrical quantities corresponding to these key energies serve as inputs, while the energy at the unstable equilibrium point (UEP) obtained from offline simulations acts as the output, enabling the construction of an artificial intelligence model for energy threshold prediction. Real-time response data are fed into the model to predict the system’s instantaneous energy threshold, which is then compared with the transient energy at fault clearance to evaluate stability. Validation on both a 3-machine, 10-bus system and the New England 10-machine, 39-bus system confirms the method’s adaptability and accuracy. The simulation results demonstrate that the proposed short-term voltage stability assessment model outperforms other methods in both accuracy and computational efficiency.

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Keywords

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