Multi-objective artificial lemming algorithm for optimal operation of carbon capture and power-to-methanol integrated microgrid

Abstract

Against the global low-carbon energy transition and booming AI power demand, coordinating microgrid economic operation with low-carbon, efficient scheduling has become a core challenge for new power systems. Therefore, this paper proposes a microgrid-integrated energy system with deep coupling of carbon capture and power-to-methanol (CCS-P2M). Facing the system’s complex multidimensional operational constraints, this paper establishes a three-objective scheduling model to minimize net operating cost, carbon emissions, and energy curtailment rate, and proposes a Multi-Objective Artificial Lemming Algorithm (MOALA) for efficient optimization. In the islanded scenario, net operating cost decreases from − 792.60 to − 2773.17 CNY, the curtailment rate decreases from 27.45 to 23.73%, and carbon emissions drop from 491.88 to − 3229.79 kg. In the grid-connected scenario, net operating cost further decreases, from − 9462.03 to − 9595.02 CNY, while the curtailment rate decreases from 0.39 to 0.05%. These results verify the effectiveness of the CCS-P2M strategy in enhancing economic performance, renewable energy accommodation, and deep decarbonization.