Multi-agent Bayesian Deep Reinforcement Learning for Microgrid Energy Management under Communication Failures

@article{Zhou2021MultiagentBD,
  title={Multi-agent Bayesian Deep Reinforcement Learning for Microgrid Energy Management under Communication Failures},
  author={Hao Zhou and Atakan Aral and Ivona Brandi{\'c} and Melike Erol-Kantarci},
  journal={ArXiv},
  year={2021},
  volume={abs/2111.11868}
}
Microgrids (MGs) are important players for the future transactive energy systems where a number of intelligent Internet of Things (IoT) devices interact for energy management in the smart grid. Although there have been many works on MG energy management, most studies assume a perfect communication environment, where communication failures are not considered. In this paper, we consider the MG as a multiagent environment with IoT devices in which AI agents exchange information with their peers… 
Cellular Network Capacity and Coverage Enhancement with MDT Data and Deep Reinforcement Learning
TLDR
This paper investigates a Minimization of Drive Tests (MDT)-driven Deep Reinforcement Learning (DRL) algorithm to optimize coverage and capacity by tuning antennas tilts on a cluster of cells from TIM’s cellular network.
Learning from Peers: Deep Transfer Reinforcement Learning for Joint Radio and Cache Resource Allocation in 5G RAN Slicing
TLDR
A deep transfer reinforcement learning (DTRL) scheme for joint radio and cache resource allocation to serve 5G RAN slicing is proposed and two DTRL algorithms are proposed: Q-value-based deep transfer reinforce learning (QD TRL) and action selection-basedDeep transfer reinforcementLearning (ADTRL).

References

SHOWING 1-10 OF 41 REFERENCES
Decentralized Microgrid Energy Management: A Multi-agent Correlated Q-learning Approach
  • Hao Zhou, M. Erol-Kantarci
  • Engineering
    2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)
  • 2020
TLDR
This paper builds an energy trading model based on mid-market rate, and proposes a correlated Q-learning (CEQ) algorithm to maximize the revenue of each agent, and shows that CEQ is able to balance the Revenue of agents without harming total benefit.
Reinforcement Learning Approach for Optimal Distributed Energy Management in a Microgrid
TLDR
A reinforcement learning algorithm was developed to allow generation resources, distributed storages, and customers to develop optimal strategies for energy management and load scheduling without prior information about each other and the MG system.
Correlated Deep Q-learning based Microgrid Energy Management
  • Hao Zhou, M. Erol-Kantarci
  • Engineering
    2020 IEEE 25th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)
  • 2020
TLDR
The Long Short Term Memory networks (LSTM) based deep Q-learning algorithm is introduced and the correlated equilibrium is proposed to coordinate agents and the result shows 40.9% and 9.62% higher profit for ESS agent and photovoltaic (PV) agent, respectively.
Distributed Economic Dispatch in Microgrids Based on Cooperative Reinforcement Learning
TLDR
A cooperative reinforcement learning algorithm for distributed economic dispatch in microgrids that can avoid the difficulty of stochastic modeling and high computational complexity is proposed.
Power Loss-Aware Transactive Microgrid Coalitions under Uncertainty
TLDR
A novel Bayesian Coalition Game (BCG) based algorithm is proposed, which allows the MGs and EVs to reduce the overall power loss by allowing them to form coalitions intelligently and shows significant improvement over other compared techniques.
Reinforcement Learning-Based Microgrid Energy Trading With a Reduced Power Plant Schedule
TLDR
This scheme designs a deep RL-based energy trading algorithm to address the supply–demand mismatch problem for a smart grid with a large number of MGs without relying on the renewable energy generation and power demand models of other MGs.
A Comprehensive Survey of Multiagent Reinforcement Learning
TLDR
The benefits and challenges of MARL are described along with some of the problem domains where the MARL techniques have been applied, and an outlook for the field is provided.
A Survey of Energy Management in Interconnected Multi-Microgrids
TLDR
An overview of the current energy management systems (EMS) in IMMGs is provided, focusing on the IMMG structure, EMS objectives, timescales, and scheduling optimization structure, and a review of the distributed optimization algorithms in IM MGs.
A Survey on Enhanced Smart Micro-Grid Management System with Modern Wireless Technology Contribution
TLDR
This paper investigates communication technology applications in the micro-grid management system (MG) and focuses on their classification in a way that determines standard gaps when applying wireless for MG control levels, and explores and categorizes the literature that has applied wireless technologies to MG.
...
1
2
3
4
5
...