AI-Based Faster-Than-Real-Time Stability Assessment of Large Power Systems with Applications on WECC System

• Published in: Energies (Basel) 2023-01, Vol.16 (3), p.1401
• Main Authors: Dong, Jiaojiao, Mandich, Mirka, Zhao, Yinfeng, Liu, Yang, You, Shutang, Liu, Yilu, Zhang, Hongming
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Alternative energy sources; Artificial intelligence; Clean energy; Computer applications; Datasets; Decision trees; Dynamic models; Electric power grids; Electric power systems; Electricity distribution; Frequency stability; Machine learning; Methods; Neural networks; Ordinary differential equations; Power; power system stability; POWER TRANSMISSION AND DISTRIBUTION; Real time; Real-time control; Real-time systems; Reliability (Engineering); Simulation; Software; Stability analysis; Systems stability; Technology assessment; Transient stability; United States
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en16031401

Achieving clean energy goals will require significant advances in regard to addressing the computational needs for next-generation renewable-dominated power grids. One critical obstacle that lies in the way of transitioning today’s power grid to a renewable-dominated power grid is the lack of a faster-than-real-time stability assessment technology for operating a fast-changing power grid. This paper proposes an artificial intelligence (AI) -based method that predicts the system’s stability margin information (e.g., the frequency nadir in the frequency stability assessment and the critical clearing time (CCT) value in the transient stability assessment) directly from the system operating conditions without performing the conventional time-consuming time-domain simulations over detailed dynamic models. Since the AI method shifts the majority of the computational burden to offline training, the online evaluation is extremely fast. This paper has tested the AI-based stability assessment method using multiple dispatch cases that are converted and tuned from actual dispatch cases of the Western Electricity Coordinating Council (WECC) system model with more than 20,000 buses. The results show that the AI-based method could accurately predict the stability margin of such a large power system in less than 0.2 milliseconds using the offline-trained AI agent. Therefore, the proposed method has great potential to achieve faster-than-real-time stability assessment for practical large power systems while preserving sufficient accuracy.