Multi-Objective Optimization of Peer-to-Peer Transactions in Arizona State University's Microgrid by NSGA II

This study presents a novel approach to modeling and solving the multi-objective optimization problem of peer-to-peer transactions among agents within microgrids. A key innovation lies in considering each trade, including internal trades, as a decision variable, unlike prior research that focused on optimizing whole internal trades and grid exchanges. Using Arizona State University's microgrid as a case study with varying exchange rates, the study concludes that the proposed Non-dominated Sorting Genetic Algorithm II with modified mutation outperforms existing studies by finding superior solutions in select scenarios, showcasing significant advancements in optimizing microgrid transactions.