.png){kind=icon}
返回
Multi-Agent Deep Reinforcement Learning for Distributed Load Restoration
.png){kind=icon}
OA
AI
DOI：10.1109/TSG.2023.3310893.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper addresses the load restoration problem after power outage events. Our primary proposed methodology is using multi-agent deep reinforcement learning to optimize the load restoration process in distribution systems, modeled as networked microgrids, via determining the optimal operational sequence of circuit breakers (switches). An innovative invalid action masking technique is incorporated into the multi-agent method to handle both the physical constraints in the restoration process and the curse of dimensionality as the action space of operational decisions grows exponentially with the number of circuit breakers. The features of our proposed method include centralized training for multi-agents to overcome non-stationary environment problems, decentralized execution to ease the deployment, and zero constraint violations to prevent harmful actions. Our simulations are performed in OpenDSS and Python environments to demonstrate the effectiveness of the proposed approach using the IEEE 13, 123, and 8500-node distribution test feeders. The results show that the proposed algorithm can achieve a significantly better learning curve and stability than the conventional methods.
Keyword:
Microgrids
Circuit breakers
Switches
Linear programming
Training
Manganese
Load modeling
Deep reinforcement learning
invalid action masking
distribution systems
networked microgrids
multi-agent systems
load restoration
OpenDSS
期刊
IF:
9.8
论文数:
5.7K
被引数:
4.3W
机构
引用论文
Distribution System Resilience Under Asynchronous Information Using Deep Reinforcement Learning基于深度强化学习的异步信息下配电系统弹性
PREAI
分享收藏Study of bi-directional buck-boost converter topologies for application in electrical vehicle motor drives应用于电动汽车电机驱动的双向buck-boost变换器拓扑研究
PREAI
分享收藏Distributed Operation of Wind Farm for Maximizing Output Power: A Multi-Agent Deep Reinforcement Learning Approach.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏A Multi-Agent Deep Reinforcement Learning Approach for Enhancement of COVID-19 CT Image Segmentation一种用于增强新型冠状病毒肺炎CT图像分割的多Agent深度强化学习方法OAAI
OAAI分享收藏
查看更多内容