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

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.