One of the on-going research topic in smart grid planning and assessment is the definition of suitable time evolution of load profiles in micro grids by using the information about the network topology and the available electrical measurements. This paper presents an approach for a heuristic definition of nodal load profiles in micro grids when the available measurements are not exhaustive for its state evaluation. In particular, in order to develop the preliminary micro grids assessment, a Genetic Algorithm (GA) has been employed to determine possible evolution of nodal load profiles that satisfy the power system constraints and input measurements. In order to verify the effectiveness of proposed methodology a real micro grid has been considered as case of study. The micro grid has been simulated in Digsilent and the used GA has been implemented in Matlab environment. Finally, Digsilent Programming Language (DPL) has been employed for interfacing the GA with Digsilent.

A Genetic Algorithm for the Definition of Nodal Load Time Evolutions in Micro Grids Assessment

KORJANI, SAMAN;PORRU, MARIO;SERPI, ALESSANDRO;DAMIANO, ALFONSO
2016

Abstract

One of the on-going research topic in smart grid planning and assessment is the definition of suitable time evolution of load profiles in micro grids by using the information about the network topology and the available electrical measurements. This paper presents an approach for a heuristic definition of nodal load profiles in micro grids when the available measurements are not exhaustive for its state evaluation. In particular, in order to develop the preliminary micro grids assessment, a Genetic Algorithm (GA) has been employed to determine possible evolution of nodal load profiles that satisfy the power system constraints and input measurements. In order to verify the effectiveness of proposed methodology a real micro grid has been considered as case of study. The micro grid has been simulated in Digsilent and the used GA has been implemented in Matlab environment. Finally, Digsilent Programming Language (DPL) has been employed for interfacing the GA with Digsilent.
978-1-5090-3388-1
Genetic algorithm; Micro grid assessment; Micro grid design; Nodal Load profiles
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11584/187639
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