Voltage control

Two-Stage Deep Reinforcement Learning for Inverter-Based Volt-VAR Control in Active Distribution Networks

Model-based Vol/VAR optimization method is widely used to eliminate voltage violations and reduce network losses. However, the parameters of active distribution networks(ADNs) are not onsite identified, so significant errors may be involved in the …

Bi-Level Off-policy Reinforcement Learning for Two-Timescale Volt/VAR Control in Active Distribution Networks

In Volt/Var control (VVC) of active distribution networks (ADNs), both slow timescale discrete devices (STDDs, e.g. on-load tap changers) and fast timescale continuous devices (FTCDs, e.g. distributed generators) are involved and should be …

Federated Reinforcement Learning for Decentralized Voltage Control in Distribution Networks

Multi-agent reinforcement learning (MARL) with ``centralized training & decentralized execution'' framework has been widely investigated to implement decentralized voltage control for distribution networks (DNs). However, a centralized training …

Model-Free Voltage Control for Inverter-Based Energy Resources: Algorithm, Simulation and Field Test Verification

Dynamic voltage support is a type of critical ancillary service in electric power networks. With the increasing penetration of inverter-based renewable energy resources, utilizing their idle capacity to provide dynamic voltage support is becoming …