The paper presents a development of the Mixed Integer Linearized Exergoeconomic (MILE) optimization method, proposed in 2010. The method is based on an improvement of the original Fuel Impact Relation, proposed in the ambit of Thermoeconomics, where on-off operation and presence-absence of components can be modelled by means of binary decision variables and inequality constraints. In particular, the introduction of binary decision variables allows the synthesis and the operation of an energy system to be optimized at the same time. In this new development, an evolutionary algorithm is used at the higher optimization level, to choose the main binary decision variables, whilst a MILP algorithm is used at the lower level, to choose the optimal operation of the system and to supply the merit function to the Evolutionary algorithm. The method is then applied to a distributed generation system, which has to be designed for a set of users located in the centre of a small town in the North-East of Italy. The results show the advantage of distributed cogeneration, when the optimal synthesis and operation of the whole system are adopted, and an important reduction in the computing time by using the proposed bi-level optimization procedure, with respect to the direct implementation of the whole problem in a single level MILP solver.

Application of the Mixed Integer Linearized Exergoeconomic method with evolutionary optimization to a cogeneration and district heating system

REINI, MAURO;PINAMONTI, PIERO;COSTANZO, STEFANO;CASISI, MELCHIORRE
2016

Abstract

The paper presents a development of the Mixed Integer Linearized Exergoeconomic (MILE) optimization method, proposed in 2010. The method is based on an improvement of the original Fuel Impact Relation, proposed in the ambit of Thermoeconomics, where on-off operation and presence-absence of components can be modelled by means of binary decision variables and inequality constraints. In particular, the introduction of binary decision variables allows the synthesis and the operation of an energy system to be optimized at the same time. In this new development, an evolutionary algorithm is used at the higher optimization level, to choose the main binary decision variables, whilst a MILP algorithm is used at the lower level, to choose the optimal operation of the system and to supply the merit function to the Evolutionary algorithm. The method is then applied to a distributed generation system, which has to be designed for a set of users located in the centre of a small town in the North-East of Italy. The results show the advantage of distributed cogeneration, when the optimal synthesis and operation of the whole system are adopted, and an important reduction in the computing time by using the proposed bi-level optimization procedure, with respect to the direct implementation of the whole problem in a single level MILP solver.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11368/2883198
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