-
Online Multi-agent Reinforcement Learning for Decentralized Inverter-based Volt-VAR Control
The distributed Volt/Var control (VVC) methods have been widely studied ...
read it
-
Provably Efficient Causal Reinforcement Learning with Confounded Observational Data
Empowered by expressive function approximators such as neural networks, ...
read it
-
Modeling Attention in Panoramic Video: A Deep Reinforcement Learning Approach
Panoramic video provides immersive and interactive experience by enablin...
read it
-
Distributed Voltage Regulation of Active Distribution System Based on Enhanced Multi-agent Deep Reinforcement Learning
This paper proposes a data-driven distributed voltage control approach b...
read it
-
Deep Reinforcement Learning for Real-Time Optimization of Pumps in Water Distribution Systems
Real-time control of pumps can be an infeasible task in water distributi...
read it
-
Obtain Employee Turnover Rate and Optimal Reduction Strategy Based On Neural Network and Reinforcement Learning
Nowadays, human resource is an important part of various resources of en...
read it
-
Smart Train Operation Algorithms based on Expert Knowledge and Reinforcement Learning
During recent decades, the automatic train operation (ATO) system has be...
read it
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 model and make the model-based method infeasible. To cope with this critical issue, we propose a novel two-stage deep reinforcement learning (DRL) method to improve the voltage profile by regulating inverter-based energy resources, which consists of offline stage and online stage. In the offline stage, a highly efficient adversarial reinforcement learning algorithm is developed to train an offline agent robust to the model mismatch. In the sequential online stage, we transfer the offline agent safely as the online agent to perform continuous learning and controlling online with significantly improved safety and efficiency. Numerical simulations on IEEE test cases not only demonstrate that the proposed adversarial reinforcement learning algorithm outperforms the state-of-art algorithm, but also show that our proposed two-stage method achieves much better performance than the existing DRL based methods in the online application.
READ FULL TEXT
Comments
There are no comments yet.