Integration of PV Distributed Generators into Electrical Networks for Investment and Energy Purchase Costs Reduction by Using a Discrete–Continuous Parallel PSO

Luis Fernando Grisales-Noreña (), Oscar Danilo Montoya, Edward-J. Marín-García, Carlos Andres Ramos-Paja and Alberto-Jesus Perea-Moreno ()
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Luis Fernando Grisales-Noreña: Facultad de Ingenierías, Campus Robledo, Instituto Tecnológico Metropolitano de Medellín, Medellín 050036, Colombia
Oscar Danilo Montoya: Grupo de Compatibilidad e Interferencia Electromagnética, Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Bogotá 110231, Colombia
Edward-J. Marín-García: Grupo de Investigación en Innovación y Desarrollo en Electrónica Aplicada—GiiDEA, Universidad del Valle, Cartago 762021, Colombia
Carlos Andres Ramos-Paja: Facultad de Minas, Universidad Nacional de Colombia, Medellin 050041, Colombia
Alberto-Jesus Perea-Moreno: Departamento de Física Aplicada, Radiología y Medicina Física, Universidad de Córdoba, Campus de Rabanales, 14071 Córdoba, Spain

Energies, 2022, vol. 15, issue 20, 1-20

Abstract: The problem of optimally integrating PV DGs into electrical networks to reduce annual costs (which include energy purchase and investment costs) was addressed in this research by presenting a new solution methodology. For such purpose, we used a Discrete–Continuous Parallel Particle Swarm Optimization method (DCPPSO), which considers both the discrete and continuous variables associated with the location and sizing of DGs in an electrical network and employs a parallel processing tool to reduce processing times. The optimization parameters of the proposed solution methodology were tuned using an external optimization algorithm. To validate the performance of DCPPSO, we employed the 33- and 69-bus test systems and compared it with five other solution methods: the BONMIN solver of the General Algebraic Modeling System (GAMS) and other four discrete–continuous methodologies that have been recently proposed. According to the findings, the DCPPSO produced the best results in terms of quality of the solution, processing time, and repeatability in electrical networks of any size, since it showed a better performance as the size of the electrical system increased.

Keywords: metaheuristic methods; parallel processing; PV generation; economic analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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