Uncertainty-Driven Optimization of Photovoltaic-Integrated Building-Level Energy Hubs: Advancing SDG 7 Targets

A fundamental challenge in accelerating progress toward Sustainable Development Goal 7’s targets, precisely energy efficiency (EE), is operating building-level energy hubs (BEHs) that must contend with multiple concurrent uncertainties. Therefore, this paper develops a bi-level uncertainty-driven optimization model for operating BESs with integrated photovoltaic capabilities to cover it. The architecture’s upper level combines info-gap decision theory with the non-dominated sorting genetic algorithm II to model and optimize the horizon of four key uncertain input parameters, considering their complex interdependencies, yielding a four-dimensional Pareto frontier (4DPF). To identify the robust and practically implementable solution from the 4DPF, a hybrid decision-making approach that integrates fuzzy satisfaction with distance metrics principles is used. The lower level completes the architecture by modeling BEHs’ operational process as a complex mixed-integer nonlinear programming formulation, leveraging the energy hub tool to handle the intricacies and interdependencies among various energy carriers and components. To verify performance and practical applicability, the model underwent extensive testing across multiple cases in an industrial building. The results confirm the model’s ability to improve EE in buildings. These promising outcomes suggest the model could be valuable for other building types seeking to enhance their energy efficiency, from commercial complexes to residential ones.