Analisi e ottimizzazione della configurazione di un macrosistema di conversione di energia

Abstract

The depletion of fossil fuel sources and the increasing difficulty in the search and development of alternative sources push to the search of more complex energy systems configurations to enhance efficiency and reduce costs. This, in turn, requires more efficient (but simple) tools to predict systems behavior with the required level of accuracy (simulation tools), and increases the number of options both in the system design and operation (optimization procedures). A general approach for energy systems simulation and optimization is first presented in this thesis, which applies at any level of system complexity and dimensions. The attention is then focused on Macro-Systems, consisting of a set of energy systems which convert primary energy sources into different energy forms required by the users, the former being variable both in time and space, the latter depending also on market rules and users characteristics. Reliable models are built to forecast Macro-Systems behavior. Binary variables are included in these models at design level to decide the inclusion or exclusion of a single plant or to decide its on-off status at off-design conditions depending on the objective being considered. At the same time detailed models of the single plants that are expected to be included in the Macro-Systems are developed to extract reliable Fuel-Products relationships of each plant to be included in the Macro-System model. These relationships are in general well approximated by linear functions. Constraints on the variability in time of each plant load are also included to model their real behavior. The design and off-design optimization problems of Macro-Systems described by these models belongs to the wider MILP (Mixed-Integer Linear Programming) dynamic optimization problems, which is analyzed in the first part of this thesis, describing in particular the “dynamic programming” technique that is used to simplify the search of the solution. An original approach for the decomposition of the dynamic optimization problems into two sub-problems is then proposed to simplify the search of the solution and reduce the computational effort without a significant loss in accuracy. Finally, two applications are presented: the first one refers to traditional steam and combined cycle power plants operating in the German power free market generation system. The reduction in profits generated by these plants deriving from the priority assigned to renewable power systems in the market is evaluated using real price and fuel cost data in a period of four years. The second application deals with a complex Macro-Systems for combined heat and power production feeding a district heating network with variable demand. The volume of a thermal storage system which keeps the electric power generation independent of the thermal demand is optimized according to the maximum profit of the whole Macro-System in a year of operation