Optimizing multi-objective peer-to-peer energy trading in green homes: Robust strategies to address non-probabilistic uncertainty using IGDT with integrated demand response

Residential renewable energy systems, including Photovoltaic (PV), Energy Storage Systems (ESS), and hydrogen storage, are increasingly widespread, fostering a shift towards sustainable energy consumption. Peer-to-peer (P2P) energy trading emerges as a promising avenue for consumers to exchange self-generated renewable energy, promoting community resilience and green energy adoption. This study investigates the implications of P2P energy trading within microgrids, where homes are equipped with renewable sources and energy storage systems. Leveraging Information Gap Decision Theory (IGDT), we address uncertainties inherent in consumer demand and renewable energy generation, enhancing the robustness of microgrid operations. Through Sequential Linear Goal Programming (SLGP), we analyze case studies with and without P2P energy exchange, revealing diverse outcomes. P2P energy trading leads to a significant 17 % reduction in residential energy costs, stimulating the uptake of renewables and fostering local energy autonomy. Furthermore, strategic optimization of energy exchange pathways within the green homes yields a notable 20.5 % decrease in losses, enhancing overall system efficiency. Integrating demand response program (DRP) proves pivotal in reshaping energy consumption patterns, bolstering microgrid resilience, and mitigating residential expenses. These findings underscore the efficacy of DR in microgrid management, empowering communities with enhanced adaptability, load management capabilities, and energy reliability.