Fusion of microgrid control with model-free reinforcement learning: Review and vision
Challenges and opportunities coexist in microgrids as a result of emerging large-scale
distributed energy resources (DERs) and advanced control techniques. In this paper, a …
distributed energy resources (DERs) and advanced control techniques. In this paper, a …
AI explainability and governance in smart energy systems: a review
Traditional electrical power grids have long suffered from operational unreliability, instability,
inflexibility, and inefficiency. Smart grids (or smart energy systems) continue to transform the …
inflexibility, and inefficiency. Smart grids (or smart energy systems) continue to transform the …
Consensus multi-agent reinforcement learning for volt-var control in power distribution networks
Volt-VAR control (VVC) is a critical application in active distribution network management
system to reduce network losses and improve voltage profile. To remove dependency on …
system to reduce network losses and improve voltage profile. To remove dependency on …
Multi-agent deep reinforcement learning for voltage control with coordinated active and reactive power optimization
The increasing penetration of distributed renewable energy resources causes voltage
fluctuations in distribution networks. The controllable active and reactive power resources …
fluctuations in distribution networks. The controllable active and reactive power resources …
Twin delayed deep deterministic policy gradient (TD3) based virtual inertia control for inverter-interfacing DGs in microgrids
Environmental and energy security concerns lead to the continuous displacement of
traditional fossil fuel-based power generation to power electronics interfaced distributed …
traditional fossil fuel-based power generation to power electronics interfaced distributed …
Powergym: A reinforcement learning environment for volt-var control in power distribution systems
Reinforcement learning for power distribution systems has so far been studied using
customized environments due to the proprietary nature of the power industry. To encourage …
customized environments due to the proprietary nature of the power industry. To encourage …
Reinforcement Learning for Efficient Power Systems Planning: A Review of Operational and Expansion Strategies
The efficient planning of electric power systems is essential to meet both the current and
future energy demands. In this context, reinforcement learning (RL) has emerged as a …
future energy demands. In this context, reinforcement learning (RL) has emerged as a …
Data-driven Volt/Var control based on constrained temporal convolutional networks with a corrective mechanism
The increasing renewable energy sources such as photovoltaics (PVs) systems in
distribution networks cause frequent voltage violations. Volt-Var control (VVC) has been …
distribution networks cause frequent voltage violations. Volt-Var control (VVC) has been …
Distribution System Optimization to Manage Distributed Energy Resources (DERs) for Grid Services
The proliferation of distributed energy resources (DERs) and the deployment of advanced
sensing and control technologies in electric power distribution systems calls for coordinated …
sensing and control technologies in electric power distribution systems calls for coordinated …
Reinforcement learning-based smart inverter control with polar action space in power distribution systems
To tackle the challenge of voltage regulation under high solar photovoltaics (PV)
penetration, the slow timescale control of conventional voltage regulating devices can be …
penetration, the slow timescale control of conventional voltage regulating devices can be …