With large-scale integration of renewable generation and distributed energy resources,
modern power systems are confronted with new operational challenges, such as growing …

The widespread penetration of inverter-based resources has profoundly impacted the
electrical stability of power systems (PSs). Deepening grid integration of photovoltaic and …

随着新能源并网规模不断扩大, 能源形式更加灵活多变, 电力系统调度运行面临新的挑战.
随着系统复杂度和不确定性增加, 传统基于物理模型的优化方法难以建立精确的模型进行实时 …

Volt-var control (VVC) is the problem of operating power distribution systems within healthy
regimes by controlling actuators in power systems. Existing works have mostly adopted the …

Recently, numerous data-driven approaches to control an electric grid using machine
learning techniques have been investigated. Reinforcement learning (RL)-based techniques …

Distributed energy resources (DERs) are gaining prominence due to their advantages in
improving energy efficiency, reducing carbon emissions, and enhancing grid resilience …

Reinforcement learning is well-studied under discrete actions. Integer actions setting is
popular in the industry yet still challenging due to its high dimensionality. To this end, we …

The rise of renewable energy and distributed generation requires new approaches to
overcome the limitations of traditional methods. In this context, Graph Neural Networks are …

The Volt/Var optimization (VVO) enables advanced control strategy development for voltage
regulation. With the recent advancement of data-driven approaches and communication …

The growing complexity of power system management has led to an increased interest in
the use of reinforcement learning (RL). However, no tool for comprehensive and realistic …