Optimal Operation of Multi-Vector Energy Systems Considering Multiple Uncertainties

This paper develops a mixed-integer nonlinear programming model to optimize the operation of multi-vector energy systems (MESs) under different sources of uncertainty. Considering nonlinear components and uncertainties, the operation of the MESs is a complicated challenge because conventional methods cannot be used in this condition. The proposed model relies on the energy hub tool and the Monte Carlo experiment as its key elements to alleviate this challenge. The former reflects the degrees of freedom in the structure of MESs, which can be used for optimization by creating links between multi-carrier energy resources and energy demands through conversion, storage, condition, and distribution processes. The latter augments the reliability and resilience of the model's forecasts, resulting in more informed decision-making under high-impact uncertainties related to multi-carrier energy demands, the price of electricity and gas, and renewable power production capacity. The performance of the model was assessed through various case studies on an industrial building. The results proved the effectiveness of the model.