In the paper a multi-objective optimization model for distributed energy supply systems optimization is
presented. The superstructure of the system comprehends a distri1ct heating network that connects the
users to each other, small scale CHP systems (e.g. micro gas turbines or internal combustion engine),
conventional integration boilers, large centralized solar plant and a seasonal thermal storage. The
optimization has to determine the optimal structure of the system, the size and the load of each component
inside the optimal solution, as well as the optimal operation strategy. The multi-objective optimization is
based on a Mixed Integer Linear Programming model (MILP) and it takes into account as objective function a
linear combination of the total annual cost (for owning, maintaining and operating the whole system) and the
CO2 emissions amount, associated to the system operation. The model allows to obtain different optimal
solutions by varying the relative weight of the economic and the environmental objectives. In this way the
Pareto Front is identified and the possible improvements in both economic and environmental terms can be
highlighted. The model has been applied as an example to a specific case study made of nine industrial
facilities and it has been optimized for different superstructure configurations and for two different values of
the electricity greenhouse emissions factor. The obtained results shows that the solar plant, coupled with the
seasonal thermal storage, allows reaching both environmental and economic goals. If the centralized solar
plant is not considered in the superstructure, CO2 emissions related to electricity affect the optimal structure
of the energy supply system.
