In the pursuit of sustainable and efficient energy management within residential settings, peer-to-peer (P2P) energy trading offers an effective strategy. This paper introduces a novel simulation framework that enhances the study and optimization of P2P energy transactions between consumer and prosumer households. Our framework, using an agent-based modeling approach, dynamically simulates interactions among diverse agents, incorporating various home appliances with distinct energy consumption profiles and priorities, along with renewable energy sources like solar panels and wind turbines. The framework’s design allows for the integration of multiple P2P trading algorithms, providing significant flexibility and adaptability for testing scenarios. The proposed framework supports both deterministic and stochastic meteorological data inputs, for accurate energy production simulations. Initial simulations highlight the framework’s capability to effectively simulate energy exchanges and improve the overall P2P energy trading experience. We also discuss potential implications for future scalability and possibility of integrate with machine learning (ML) technologies.