Multicriteria Decision-Making Under Multiple Deep Uncertainties: A Building-Level Integrated Energy System Application

The operation of building-level integrated energy systems (BL-IESs) faces multiple deep uncertainties. Nevertheless, most studies use deterministic frameworks that overlook uncertainties and potentially result in suboptimal operational plans. To make optimal operational decisions, it is critical to comprehend the impacts of uncertainties. In this paper, then, the authors develop a bi-level multicriteria decision-making framework to incorporate, model, and investigate deep uncertainties in the operation of BL-IESs. The upper level simultaneously optimizes the horizon of quadruple uncertainties, considering their interactions through information-gap decision theory and non-dominated sorting genetic algorithm II. The lower level uses the energy hub concept to characterize the building as an IES. The operation problem is then developed as a mixed-integer linear optimization problem to minimize energy and emission costs within technical constraints. This framework was applied and showcased in an industrial building. The results showed the framework's ability to effectively scrutinize the effects of uncertainties on the operation of BL-IESs.