Optimal Location and Sizing of PV Generation Units in Electrical Networks to Reduce the Total Annual Operating Costs: An Application of the Crow Search Algorithm

This study presents a master–slave methodology to solve the problem of optimally locating and sizing photovoltaic (PV) generation units in electrical networks. This problem is represented by means of a Mixed-Integer Nonlinear Programming (MINLP) model, whose objective function is to reduce the total...

Full description

Autores:
Cortés-Caicedo, Brandon
Grisales-Noreña, Luis Fernando
Danilo Montoya, Oscar
Perea-Moreno, Miguel-Angel
Perea-Moreno, Alberto-Jesus
Tipo de recurso:
Fecha de publicación:
2022
Institución:
Universidad Tecnológica de Bolívar
Repositorio:
Repositorio Institucional UTB
Idioma:
eng
OAI Identifier:
oai:repositorio.utb.edu.co:20.500.12585/12411
Acceso en línea:
https://hdl.handle.net/20.500.12585/12411
Palabra clave:
Placement;
Active Distribution Network;
Voltage Stability
LEMB
Rights
openAccess
License
http://creativecommons.org/licenses/by-nc-nd/4.0/
Description
Summary:This study presents a master–slave methodology to solve the problem of optimally locating and sizing photovoltaic (PV) generation units in electrical networks. This problem is represented by means of a Mixed-Integer Nonlinear Programming (MINLP) model, whose objective function is to reduce the total annual operating costs of a network for a 20-year planning period. Such costs include (i) the costs of purchasing energy at the conventional generators (the main supply node in this particular case), (ii) the investment in the PV generation units, and (iii) their corresponding operation and maintenance costs. In the proposed master–slave method, the master stage uses the Discrete–Continuous version of the Crow Search Algorithm (DCCSA) to define the set of nodes where the PV generation units will be installed (location), as well as their nominal power (sizing), and the slave stage employs the successive approximation power flow technique to find the value of the objective function of each individual provided by the master stage. The numerical results obtained in the 33- and 69-node test systems demonstrated its applicability, efficiency, and robustness when compared to other methods reported in the specialized literature, such as the vortex search algorithm, the generalized normal distribution optimizer, and the particle swarm optimization algorithm. All simulations were performed in MATLAB using our own scripts. © 2022 by the authors.

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