The latest political initiatives on energy saving are forcing distributors to implement actions that increase the energy efficiency of their system. Among these actions distributors can drive the development of the Distributed Generation (DG) on their networks. The inclusion of the energy saving goal among the terms of the objective function makes the DG allocation problem more and more complicated, due to the non homogeneity of its terms and the presence of several technical and environmental constraints, specific for each generation technology. Hence, a Multi-Objective approach, based on the Non-dominated Sorting Genetic Algorithm, has been adopted to solve the optimal placement of different types of generators simultaneously. The energy saving goal has been considered in the form of greenhouse gas emission reduction, characterizing each generation technology depending on its CO2 emission per kWh produced. Load demand and DG production have been modelled probabilistically and by assuming specific daily load/generation curves.

A multi-objective approach for the optimal distributed generation allocation with environmental constraints

PILO, FABRIZIO GIULIO LUCA;MOCCI, SUSANNA;CELLI, GIANNI;SOMA, GIAN GIUSEPPE
2008-01-01

Abstract

The latest political initiatives on energy saving are forcing distributors to implement actions that increase the energy efficiency of their system. Among these actions distributors can drive the development of the Distributed Generation (DG) on their networks. The inclusion of the energy saving goal among the terms of the objective function makes the DG allocation problem more and more complicated, due to the non homogeneity of its terms and the presence of several technical and environmental constraints, specific for each generation technology. Hence, a Multi-Objective approach, based on the Non-dominated Sorting Genetic Algorithm, has been adopted to solve the optimal placement of different types of generators simultaneously. The energy saving goal has been considered in the form of greenhouse gas emission reduction, characterizing each generation technology depending on its CO2 emission per kWh produced. Load demand and DG production have been modelled probabilistically and by assuming specific daily load/generation curves.
2008
9781934325407; 9781934325216
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/107188
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