[Objective] To address the critical challenges of unreasonable battery management and heightened vulnerability to various sources of uncertainty interference that emerge during the process of deep integration between urban electric vehicle battery charging and swapping stations and renewable energy sources

this study is dedicated to significantly improving both the operational economy and robustness of battery charging and swapping station systems within the context of high-proportion renewable energy penetration and complex grid interactions. [Methods] To achieve this goal

an innovative and comprehensive optimal scheduling strategy for battery charging and swapping stations is proposed

based on the synergistic combination of information gap decision theory and robust optimization(IGDT-RO). First

a detailed optimal scheduling model is constructed that integrally encompasses multiple key components: the external power grid

photovoltaic generation units

gas turbines

battery energy storage systems

and diverse charging and swapping facilities. Second

a sophisticated energy management strategy is designed

which is founded on refined battery state-of-charge(SOC) interval management and a welldefined charging/discharging prioritization mechanism

thereby enabling the dynamic maintenance of an adequate inventory of fully charged batteries to meet fluctuating demand. Furthermore

a hybrid uncertainty handling method is innovatively developed: robust optimization is specifically adopted to effectively mitigate the inherent fluctuations in photovoltaic power output

while the information gap decision theory(IGDT) risk-averse model is skillfully integrated to manage the uncertainties associated with load demand. This combined approach fundamentally eliminates the dependency on pre-defined probability distributions or extensive scenario generation

thereby substantially reducing the computational complexity and burden of the scheduling model. [Results] A comprehensive case study

based on a typical industrial park in east China

is meticulously conducted for simulation analysis and validation. By constructing and analyzing multiple simulation scenarios that reflect different decisionmaker risk attitudes

the corresponding optimal scheduling schemes for the electric vehicle battery charging and swapping station are derived. The simulation results unequivocally and comprehensively verify the reliability and practical effectiveness of the proposed IGDT-RO-based scheduling model in real-world operational environments

demonstrating its capability to yield both feasible and economically advantageous scheduling outcomes. [Conclusion] The proposed IGDT-RO-based optimal scheduling strategy for battery charging and swapping stations provides a robust and effective solution to the critical challenges of unreasonable battery management and the detrimental impacts of uncertainty interference that arise from the deep integration of renewable energy. It offers robust theoretical and technical support for formulating both economically viable and operationally feasible scheduling schemes for such stations. Consequently

this strategy can significantly enhance the operational economy and bolster the robustness of these station systems

thereby facilitating their reliable and efficient operation within the context of high penetration of renewable energy and supporting the broader goals of sustainable urban energy development.