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

The increasing penetration of residential photovoltaics (PV), energy storage, and flexible demand introduces significant uncertainty, coordination challenges, and long-term asset degradation in smart energy communities. Existing residential energy management approaches often rely on deterministic optimization or single-agent learning, limiting robustness, scalability, and the ability to balance economic performance, asset health, and user comfort under stochastic operating conditions. This paper proposes a unified, practically oriented integration of uncertainty, asset degradation, comfort constraints, and peer-to-peer (P2P) energy exchange within a multi-agent reinforcement learning (MARL) framework for residential energy communities. The community is formulated as a Markov game in which each prosumer operates as an autonomous agent with PV generation, battery storage, and flexible demand. Economic cost, comfort preservation, and asset degradation are incorporated into a single learning objective. This enables decentralized and coordinated decision-making through shared interactions with the environment. Simulation results under varying levels of uncertainty and community sizes demonstrate that the proposed framework achieves performance competitive with a centralized benchmark while exhibiting consistent performance, reduced asset degradation, and effective comfort preservation.