Customizable Preference Models for Prosumers in Peer-to-Peer Energy Trading

Abstract

The transition toward decentralized energy systems necessitates advanced mechanisms for optimizing energy-sharing frameworks and dynamic supplier-consumer matching that aligns energy transactions with individual preferences. To this aim, this paper introduces a preference-driven approach for supplier-consumer matching in peer-to-peer (P2P) energy-sharing systems, addressing limitations in previous models that fail to capture the diversity and dynamism of consumer preferences. The proposed method introduces three customizable preference models and employs a multi-objective optimization model to evaluate suppliers based on critical attributes: cost, energy assurance, and security. Experimental findings validate the robustness of the proposed approach, demonstrating its ability to efficiently rank suppliers and accommodate consumer preferences across diverse scenarios involving large supplier pools and multiple attributes. The approach proves adaptable to varying consumer demands, balancing computational efficiency with responsiveness to consumer needs. The results underscore the potential of this approach to enhance energy-sharing systems by enabling more personalized and scalable supplier-consumer interactions.