AN INNOVATIVE TOOL FOR OPTIMIZING THE ENERGY HUB OF LARGE BUILDINGS: THE CASE STUDY OF A NEW HOSPITAL IN NORTHERN ITALY

The challenge of achieving net zero emissions by 2050 necessitates to drastically reduce the energy consumption of the building sector, which alone contributes for almost 40% of the anthropic CO_2 emissions. In recent years, growing attention has been paid to the minimization of costs and emissions of large buildings by optimizing the design and operation of their energy systems. Most of the works available in the literature consider a limited set of technologies to meet only the electrical and heating demands. In this work an innovative model is developed for the optimization of the energy hub of a hospital, characterized by 5 distinct energy demands: Electrical, Heating, Cooling, Steam and Sanitary Hot Water. To satisfy such demands, the proposed model considers a large set of technologies including cogeneration units, absorbers, reversible and multipurpose heat pumps. The tool consists of two sequential Python codes for the size optimization and the operation optimization, respectively. The first code defines the size of each technology considering the energy needs of four representative days obtained using the k-medoid method, with two extreme days added to ensure load supply. A preliminary investigation demonstrated that this clustering technique leads to an overestimation in the objective function of less than 3%, compared to a 365-days simulation. Therefore, the computational time is drastically reduced with a small impact on the results accuracy. The optimal sizes obtained from the first part of the model are used as input for the second code, which defines the optimal operation of the system throughout the year. The optimization problems are solved using the Python extension of Gurobi, a software that allows for the assessment of MIQP and MILP models by exploiting the branch and bound method. Two different objective functions are considered: total annual costs and CO_2 emissions. The first code results show that, according to the considered objective function, a reduction of 14.4% in the annual costs or a reduction of 12.36% in the CO_2 emissions can be reached with respect to a non-optimized reference configuration, with a difference between the two optimized solutions of 20.60\% in the CO_2 emissions and of 42.07% in the total costs. Between the two solutions, the one provided by the economical optimization is chosen for the entire year simulation. Once the technologies to be used were selected, the simulation of the model was performed resulting in a cost increase with respect to the first code of 4.44 %, and an increase of CO_2 emissions of 4.5%.