Integrated multi-objective optimization algorithm for energy aggregator and new energy vehicle market interaction scheduling

With the accelerating global shift toward carbon neutrality and the rapid adoption of new energy vehicles, developed regions are entering a phase of heightened energy demand. This evolution has introduced a competitive dynamic between energy aggregators and new energy vehicle owners, underscoring the need for effective regulatory oversight. This study models the trading relationship between energy aggregators and new energy vehicle owners as a constrained, nonlinear multi-objective optimization problem. The model accounts for both electricity and hydrogen transactions, incorporating contract and spot market interactions with upstream suppliers, along with the diverse charging schedules of new energy vehicles. To manage the model's complexity, simplification strategies were applied, and an integrated algorithm, I-IMTCMO-BS, was proposed. Experimental results show that the algorithm generated 285 high-quality approximate Pareto solutions, outperforming 12 benchmark multi-objective optimization algorithms in feasibility and dominance. Furthermore, a market supervision mechanism based on Pareto classification is introduced to evaluate transaction outcomes providing positive, acceptable, or unacceptable as a basis for informed regulatory intervention. Overall, the research offers a full-process analytical framework and algorithmic toolkit for market regulators tasked with managing the emerging dynamics between energy aggregators and new energy vehicle owners—an issue poised to become increasingly relevant in the global clean energy transition.