As a benchmark exercise for testing software and methods developed in Hycon2 for decentralized and distributed control, we address the problem of designing the Automatic Generation Control (AGC) layer in power network systems. In particular, we present three different scenarios and discuss performance levels that can be reached using Centralized Model Predictive Control (MPC). These results can be used as a milestone for comparing the performance of alternative control schemes. Matlab software for simulating the scenarios is also provided in an accompanying file.
PowerAgentBench-Dyn: A Benchmark for Agentic AI in Power System Dynamic Studies
School of Engineering and Applied Sciences, Harvard University（哈佛大学工程与应用科学学院）；Department of Energy, Politecnico di Milano（米兰理工大学能源系）；Energy Digitalization Group, UBITECH（UBITECH能源数字化集团）
An increased energy demand, and environmental pressure to accommodate higher levels of renewable energy and flexible loads like electric vehicles have led to numerous smart transformations in the modern power systems. These transformations make the cyber-physical power system highly susceptible to cyber-adversaries targeting its numerous operations. In this work, a novel black box adversarial attack strategy is proposed targeting the AC state estimation operation of an unknown power system using historical data. Specifically, false data is injected into the measurements obtained from a small s…
Benchmarking the performance of controllers for power grid transient stability
Queen Mary University of London（伦敦大学玛丽女王学院）；Università di Catania（卡塔尼亚大学）；INFN（意大利国家核物理研究所）；Technical University of Dresden（德累斯顿工业大学）；Max Planck Institute for Dynamics and Self-Organization（马克斯·普朗克动态与自组织研究所）
Network redundancy is one of the spatial network structural properties critical to robustness against cascading failures in power networks. The waiting-time distributions for network partitions in cascading failures explain how the spatial network structures affect the cascading behaviors. Two waiting time events associated with the first and largest network partitions are studied for cascading failures under different network redundancies. With the synthetic power networks, the waiting-time distributions of network partitions can be systematically analyzed for various network redundancies. Wa…
PowerMamba: A Deep State Space Model and Comprehensive Benchmark for Time Series Prediction in Electric Power Systems
Department of Electrical and Computer Engineering, Texas A&M University（电气与计算机工程系，德克萨斯A&M大学）；School of Engineering and Applied Sciences, Harvard University（工程与应用科学系，哈佛大学）
Triadic subgraph analysis reveals the structural features in power networks based on higher-order connectivity patterns. Power networks have a unique triad significance profile (TSP) of the five unidirectional triadic subgraphs in comparison with the scale-free, small-world and random networks. Notably, the triadic closure has the highest significance in power networks. Thus, the unique TSP can serve as a structural identifier to differentiate power networks from other complex networks. Power networks form a network superfamily. Furthermore, synthetic power networks based on the random growth …
