The increasing penetration of photovoltaic (PV) generation and distributed energy resources in residential communities requires intelligent, scalable energy management strategies that go beyond conventional cost-based approaches. This paper proposes a unified multi-agent optimization framework for PV-integrated smart energy communities that incorporates design-aware PV modeling, degradation-aware energy storage control, indoor comfort preservation, and peer-to-peer (P2P) energy exchange. The community energy management problem is formulated as a multi-agent Markov game, in which each household operates as an autonomous agent that coordinates local energy resources and demand. To address the resulting nonlinear and multi-objective optimization problem, a multi-agent reinforcement learning (MARL) approach is developed under a centralized training and decentralized execution paradigm. Simulation results demonstrate that the proposed CTDE-based MARL framework significantly improves operational efficiency and scalability. Compared to baseline strategies, it reduces unified operational cost by up to 69% relative to greedy control and by more than 37% compared to single-agent reinforcement learning, while consistently outperforming Independent Q-Learning (IQL). The framework also minimizes asset degradation, preserves indoor comfort, and achieves performance close to a centralized optimal solution, highlighting its potential for practical deployment in intelligent residential energy systems.
Khan et al. (2026) studied this question.