Power flow approximation for DC networks with constant power loads via logarithmic transform of voltage magnitudes

This paper proposes a logarithmic transformation of voltages (LTVM) for the power flow in DC grids. This problem is non-linear due to the presence of constant power loads (CPLs), which also introduce a negative resistance effect that can create numerical instability for conventional algorithms. The...

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Autores:
Tipo de recurso:
Fecha de publicación:
2019
Institución:
Universidad Tecnológica de Bolívar
Repositorio:
Repositorio Institucional UTB
Idioma:
eng
OAI Identifier:
oai:repositorio.utb.edu.co:20.500.12585/9028
Acceso en línea:
https://hdl.handle.net/20.500.12585/9028
Palabra clave:
Direct-current networks
Linear power flow analysis
Logarithmic transformation of voltage magnitudes
Processing times
Voltage estimation errors
Electric load flow
Electric power transmission networks
Linear transformations
Mathematical transformations
MATLAB
Direct current
Power flow analysis
Processing time
Voltage estimation
Voltage magnitude
HVDC power transmission
Rights
restrictedAccess
License
http://creativecommons.org/licenses/by-nc-nd/4.0/
Description
Summary:This paper proposes a logarithmic transformation of voltages (LTVM) for the power flow in DC grids. This problem is non-linear due to the presence of constant power loads (CPLs), which also introduce a negative resistance effect that can create numerical instability for conventional algorithms. The proposed methodology is applied to dc-microgrids, dc-distribution and multi-terminal high voltage DC transmission (MT-HVDC). Two main approximations are presented and compared in terms of computational performance and the accuracy of the solution. Simulation results performed in Matlab/Octave demonstrate the advantages of the proposed methodology using a complete set of test systems, from low to high voltage applications. The proposed methodology does not require any consideration about the topology of the grid (radial or meshed) or the number of constant power loads. © 2019 Elsevier B.V.