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Dynamic state estimation (DSE) is becoming increasingly important for monitoring inverter-dominated power systems. Due to their cascading control structures, inverter-based resources (IBRs) exhibit multi-timescale dynamics, leading to stiff…
This paper presents a novel hierarchical deep reinforcement learning (DRL) based design for the voltage control of power grids. DRL agents are trained for fast, and adaptive selection of control actions such that the voltage recovery…
Under conditions of high penetration of renewables, the low-voltage (LV) distribution network needs to be carefully managed. In such a scenario, an accurate real-time low-voltage power network model is an important prerequisite, which opens…
Network analysis has played a key role in knowledge discovery and data mining. In many real-world applications in recent years, we are interested in mining multilayer networks, where we have a number of edge sets called layers, which encode…
Loss minimization in distribution networks (DN) is of great significance since the trend to the distributed generation (DG) requires the most efficient operating scenario possible for economic viability variations. Moreover, voltage…
The emerging deep learning (DL) technology has recently exhibited great potential in data-driven short-term voltage stability (SVS) assessment of complex power grids. However, without sufficient attention to the time-varying topological…
Long-term voltage stability assessment (VSA) of independent transmission (T-VSA) and distribution (D-VSA)systems have been studied since long to estimate load margins. However, their impacts on each other have been neglected due to…
Optimal dispatch of energy storage systems (ESSs) in distribution networks involves jointly improving operating economy and voltage security under time-varying conditions and possible topology changes. To support fast online decision…
Coordinated optimization and control of distribution-level assets can enable a reliable and optimal integration of massive amount of distributed energy resources (DERs) and facilitate distribution system management (DSM). Accordingly, the…
Deep reinforcement learning (DRL) holds significant promise for managing voltage control challenges in simulated power grid environments. However, its real-world application in power system operations remains underexplored. This study…
Inverter-interfaced microgrids results in challenges when designing protection systems. Traditional time-overcurrent, admittance, and differential protection methods are unsuitable on account of lack of fault current, excessively short…
We propose to execute deep neural networks (DNNs) with dynamic and sparse graph (DSG) structure for compressive memory and accelerative execution during both training and inference. The great success of DNNs motivates the pursuing of…
State estimation is highly critical for accurately observing the dynamic behavior of the power grids and minimizing risks from cyber threats. However, existing state estimation methods encounter challenges in accurately capturing power…
Accurate and scalable surrogate models for AC power flow are essential for real-time grid monitoring, contingency analysis, and decision support in increasingly dynamic and inverter-dominated power systems. However, most existing surrogates…
This paper presents a novel framework for collective control of Distributed Energy Resources (DERs) in active Distribution Networks (DNs). The proposed approach unifies the commonly employed local (i.e., decentralized) voltage and frequency…
Reliable and accurate distribution system modeling, including the secondary network, is essential in examining distribution system performance with high penetration of distributed energy resources (DERs). This paper presents a highly…
Semi-supervised learning (SSL) over graph-structured data emerges in many network science applications. To efficiently manage learning over graphs, variants of graph neural networks (GNNs) have been developed recently. By succinctly…
The increasing penetration of variable renewable energy (VRE) has brought significant challenges for power systems planning and operation. These highly variable sources are typically distributed in the grid; therefore, a detailed…
Distribution networks with high penetration of Distributed Energy Resources (DERs) increasingly rely on communication networks to coordinate grid-interactive control. While many distributed control schemes have been proposed, they are often…
The needs for improving observability of medium and low voltage distribution networks has been significantly increased, in recent year. In this paper, we focus on practical approaches for placement of affordable Measurement Devices (MDs),…