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Related Experiment Videos

Robust multi-agent reinforcement learning framework for intelligent PV-integrated smart energy systems under

Syed Bilal Arshad¹, Yanbo Che², Ayaz Ahmad³

¹School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China. syedbilalarshad@yahoo.com.

Scientific Reports

|May 7, 2026

Summary

This study introduces a multi-agent reinforcement learning (MARL) framework for smart energy communities, optimizing residential energy management. The approach balances cost, comfort, and asset health under uncertainty, outperforming traditional methods.

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Area of Science:

Smart Energy Systems
Artificial Intelligence in Energy
Renewable Energy Integration

Background:

Increasing residential photovoltaics (PV), energy storage, and flexible demand create uncertainty and coordination challenges.
Current energy management methods lack robustness and scalability for complex residential energy communities.
Asset degradation and user comfort are critical factors often overlooked in existing approaches.

Purpose of the Study:

To propose a unified framework integrating uncertainty, asset degradation, comfort constraints, and peer-to-peer (P2P) energy exchange.
To develop a decentralized, coordinated decision-making system for residential energy communities using multi-agent reinforcement learning (MARL).
To enhance the robustness and scalability of residential energy management.

Main Methods:

Formulating the residential energy community as a Markov game with autonomous prosumer agents.
Implementing a multi-agent reinforcement learning (MARL) framework to handle decentralized decision-making.
Incorporating economic cost, comfort preservation, and asset degradation into a single learning objective.

Main Results:

The proposed MARL framework demonstrates competitive performance compared to centralized benchmarks.
The system exhibits consistent performance across varying uncertainty levels and community sizes.
Significant reductions in asset degradation and effective comfort preservation were achieved.

Conclusions:

The unified MARL framework offers a robust and scalable solution for managing residential energy communities.
Decentralized decision-making through MARL effectively balances economic, comfort, and asset health objectives.
This approach addresses key challenges posed by the growing penetration of distributed energy resources.