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

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  • Luis Fernando Grisales-Noreña

    (Facultad de Ingenierías, Campus Robledo, Instituto Tecnológico Metropolitano de Medellín, Medellín 050036, Colombia
    Estudiante de Doctorado, Departamento de Ingeniería Eléctrica, Campus Lagunillas s/n, University of Jaén, Edificio A3, 23071 Jaén, Spain)

  • Oscar Danilo Montoya

    (Grupo de Compatibilidad e Interferencia Electromagnética, Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Bogotá 110231, Colombia
    Laboratorio Inteligente de Energía, Facultad de Ingeniería, Universidad Tecnológica de Bolívar, Cartagena 131001, 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)

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.

Suggested Citation

  • Luis Fernando Grisales-Noreña & Oscar Danilo Montoya & Edward-J. Marín-García & Carlos Andres Ramos-Paja & Alberto-Jesus Perea-Moreno, 2022. "Integration of PV Distributed Generators into Electrical Networks for Investment and Energy Purchase Costs Reduction by Using a Discrete–Continuous Parallel PSO," Energies, MDPI, vol. 15(20), pages 1-20, October.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:20:p:7465-:d:938729
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    References listed on IDEAS

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    1. Zhang, Fang & Deng, Hao & Margolis, Robert & Su, Jun, 2015. "Analysis of distributed-generation photovoltaic deployment, installation time and cost, market barriers, and policies in China," Energy Policy, Elsevier, vol. 81(C), pages 43-55.
    2. Tri Phuoc Nguyen & Vo Ngoc Dieu & Pandian Vasant, 2017. "Symbiotic Organism Search Algorithm for Optimal Size and Siting of Distributed Generators in Distribution Systems," International Journal of Energy Optimization and Engineering (IJEOE), IGI Global, vol. 6(3), pages 1-28, July.
    3. Pereira da Silva, Patrícia & Dantas, Guilherme & Pereira, Guillermo Ivan & Câmara, Lorrane & De Castro, Nivalde J., 2019. "Photovoltaic distributed generation – An international review on diffusion, support policies, and electricity sector regulatory adaptation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 30-39.
    4. Oscar Danilo Montoya & Luis Fernando Grisales-Noreña & Alberto-Jesus Perea-Moreno, 2021. "Optimal Investments in PV Sources for Grid-Connected Distribution Networks: An Application of the Discrete–Continuous Genetic Algorithm," Sustainability, MDPI, vol. 13(24), pages 1-19, December.
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    Cited by:

    1. Luis Fernando Grisales-Noreña & Brandon Cortés-Caicedo & Gerardo Alcalá & Oscar Danilo Montoya, 2023. "Applying the Crow Search Algorithm for the Optimal Integration of PV Generation Units in DC Networks," Mathematics, MDPI, vol. 11(2), pages 1-18, January.
    2. Luis Fernando Grisales-Noreña & Oscar Danilo Montoya & Carlos Andres Ramos-Paja, 2022. "Optimal Location and Operation of PV Sources in DC Grids to Reduce Annual Operating Costs While Considering Variable Power Demand and Generation," Mathematics, MDPI, vol. 10(23), pages 1-17, November.

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