A robust optimization framework for energy hub operation considering different time resolutions: A real case study

In this paper, a robust optimization framework is proposed for solving the energy hub operation problem in the presence of renewable energy resources (RERs) and three electrical, cooling and thermal storage systems. The study hub is a real case study in a hospital building in Hamedan, Iran. All the input parameters are obtained from real data by connecting a dw-6095 Lutron power analyzer to the hub equipment. Furthermore, the study problem is modeled as a mixed-integer non-linear programming (MINLP) problem and is solved in the form of 8 different case studies using the DICOPT solver in GAMS software. Our analysis of the simulation results reveals that considering the uncertainties led to a 6.41% increase in the total operation costs. The results also indicate that storage systems increased flexibility and reduced the operation costs by 0.87%. Moreover, the problem was solved with three different time resolutions, and the results mirrored an increase in the accuracy and solution time due to the increase in the resolution. Finally, the hub performance is assessed under three contingency events, and the results show that the exclusion of the boiler leads to about 5563.3 kWh of energy not served (ENS). (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

A robust optimization framework is proposed in this paper to solve the energy hub operation problem with renewable energy resources and multiple systems. The study uses a real case of a hospital building and solves the mixed-integer non-linear programming problem through multiple case studies. Results show that storage systems increase flexibility and reduce operation costs.