Automatic guided vehicle scheduling based photovoltaic-energy storage-charging station low-carbon optimal sizing via a computational intelligence framework

Automated container terminals (ACTs) utilizing Automatic Guided Vehicles (AGVs) require low-carbon charging infrastructure to support the global transition to carbon neutrality. Photovoltaic‑energy storage‑charging stations (PECSs) represent a novel charging infrastructure solution that integrates photovoltaic and energy storage to serve both AGVs and electric vehicles operated by terminal personnel. Against this background, this paper proposes a capacity sizing model for PECS tailored for ACTs, considering the AGV charging load modeling and lifecycle carbon emissions across multiple equipment types and lifecycle stages. Firstly, a multi-cycle AGV scheduling and charging model is developed incorporating internal-external vehicle segregation constraints for typical ACT layouts. Secondly, a lifecycle carbon emission model for PECS is constructed by clearly delineating the model boundary, which encompasses six lifecycle stages and incorporates four types of critical equipment. Thirdly, the carbon emission model is systematically integrated with economic objectives through interface variables, including equipment capacity and purchased electricity power. Therefore, a bi-objective optimal sizing model for PECSs is developed to maximize annual economic benefits while minimizing annual carbon emissions. The model is solved effectively using computational intelligence, specifically the Non-dominated Sorting Dung Beetle Optimizer, and validated through an experiment of a representative ACT. The experimental results demonstrate that the proposed model effectively optimizes PECS’s capacity to balance carbon emissions and economic objectives.