A bi-level, many-objective stochastic optimization model for handling simultaneous uncertainties in the operation of multi-carrier integrated energy systems: A hospital case study

In this paper, the authors introduce a bi-level, many-objective stochastic optimization model developed to handle simultaneous operational uncertainties in multi-carrier integrated energy systems (MIESs), particularly at the building level. The model integrates seven key uncertainty parameters, including electrical, heating, and cooling power demands, oxygen demand, the prices for electricity and gas, and photovoltaic system production capacity. At the upper level, information-gap decision theory quantifies risk-averse (robustness) and risk-seeking (opportunity) impacts of uncertainties. A non-dominated sorting genetic algorithm III is used to solve the upper-level, many-objective optimization problem, generating a seven-dimensional Pareto efficient solution set. A combination of the fuzzy satisfying method and a conservative approach is applied to select the final solution from this set, ensuring that the decision-maker’s preferences for balancing multiple objectives are fully considered. At the lower level, mixed-integer, nonlinear programming is used to optimize operational decisions of MIESs, focusing on minimizing the costs of energy, emissions, and multi-carrier energy not supplied, while simultaneously satisfying technical and logical constraints. The model was validated through a case study of a hospital’s MIESs, thereby demonstrating its efficacy in managing deep uncertainties and providing robust operational strategies.