Federated Multiagent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multimicrogrid Energy...

Area of Science:

• Electrical Engineering
• Artificial Intelligence
• Energy Systems

Background:

• The growth of distributed renewable energy necessitates advanced energy management for multimicrogrids (MMGs).
• Traditional multi-agent deep reinforcement learning (MADRL) for MMG energy management requires extensive data, posing privacy and security risks.
• Existing methods struggle to balance economic cost minimization with self-energy sufficiency in MMGs.

Purpose of the Study:

• To propose a novel federated MADRL (F-MADRL) algorithm for secure and efficient MMG energy management.
• To address the challenge of data privacy and security in distributed energy management systems.
• To minimize economic costs and maintain self-energy sufficiency within MMGs.

Main Methods:

• Implementation of a federated learning (FL) mechanism to train MADRL agents without explicit data sharing.
• Development of a decentralized MMG model where each microgrid (MG) is managed by an agent.
• Introduction of a physics-informed reward function to guide agent decision-making for cost and self-sufficiency optimization.
• Individual MG self-training followed by global model aggregation and broadcasting.

Main Results:

• The proposed F-MADRL algorithm effectively ensures data privacy and security by avoiding direct data transmission.
• Experimental validation on the ORNL-MG test system demonstrates the effectiveness of the FL mechanism.
• The F-MADRL approach significantly outperforms existing methods in optimizing economic costs and self-energy sufficiency.

Conclusions:

• Federated learning integrated with MADRL provides a robust solution for secure and efficient MMG energy management.
• The physics-informed reward enhances the performance of agents in achieving economic and self-sufficiency goals.
• This F-MADRL framework offers a practical and scalable approach for future smart grid energy management.