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Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Direct Current Electrical Networks

Author

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  • Andrés Alfonso Rosales-Muñoz

    (Grupo MATyER, Facultad de Ingeniería, Instituto Tecnológico Metropolitano, Campus Robledo, Medellín 050036, Colombia
    These authors contributed equally to this work.)

  • Luis Fernando Grisales-Noreña

    (Grupo MATyER, Facultad de Ingeniería, Instituto Tecnológico Metropolitano, Campus Robledo, Medellín 050036, Colombia
    These authors contributed equally to this work.)

  • Jhon Montano

    (Grupo MATyER, Facultad de Ingeniería, Instituto Tecnológico Metropolitano, Campus Robledo, Medellín 050036, Colombia
    These authors contributed equally to this work.)

  • Oscar Danilo Montoya

    (Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Bogotá 110231, Colombia
    Laboratorio Inteligente de Energía, Universidad Tecnológica de Bolívar, Cartagena 131001, Colombia
    These authors contributed equally to this work.)

  • Alberto-Jesus Perea-Moreno

    (Departamento de Física Aplicada, Radiología y Medicina Física, Campus Universitario de Rabanales, Universidad de Córdoba, 14071 Córdoba, Spain
    These authors contributed equally to this work.)

Abstract

This paper addresses the optimal power flow problem in direct current (DC) networks employing a master–slave solution methodology that combines an optimization algorithm based on the multiverse theory (master stage) and the numerical method of successive approximation (slave stage). The master stage proposes power levels to be injected by each distributed generator in the DC network, and the slave stage evaluates the impact of each power configuration (proposed by the master stage) on the objective function and the set of constraints that compose the problem. In this study, the objective function is the reduction of electrical power losses associated with energy transmission. In addition, the constraints are the global power balance, nodal voltage limits, current limits, and a maximum level of penetration of distributed generators. In order to validate the robustness and repeatability of the solution, this study used four other optimization methods that have been reported in the specialized literature to solve the problem addressed here: ant lion optimization, particle swarm optimization, continuous genetic algorithm, and black hole optimization algorithm. All of them employed the method based on successive approximation to solve the load flow problem (slave stage). The 21- and 69-node test systems were used for this purpose, enabling the distributed generators to inject 20 % , 40 % , and 60 % of the power provided by the slack node in a scenario without distributed generation. The results revealed that the multiverse optimizer offers the best solution quality and repeatability in networks of different sizes with several penetration levels of distributed power generation.

Suggested Citation

  • Andrés Alfonso Rosales-Muñoz & Luis Fernando Grisales-Noreña & Jhon Montano & Oscar Danilo Montoya & Alberto-Jesus Perea-Moreno, 2021. "Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Direct Current Electrical Networks," Sustainability, MDPI, vol. 13(16), pages 1-28, August.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:16:p:8703-:d:608197
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    References listed on IDEAS

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    1. Lipowski, Adam & Lipowska, Dorota, 2012. "Roulette-wheel selection via stochastic acceptance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(6), pages 2193-2196.
    2. Luis Fernando Grisales-Noreña & Daniel Gonzalez Montoya & Carlos Andres Ramos-Paja, 2018. "Optimal Sizing and Location of Distributed Generators Based on PBIL and PSO Techniques," Energies, MDPI, vol. 11(4), pages 1-27, April.
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    Cited by:

    1. Andrés Alfonso Rosales-Muñoz & Jhon Montano & Luis Fernando Grisales-Noreña & Oscar Danilo Montoya & Fabio Andrade, 2022. "Optimal Power Dispatch of DGs in Radial and Mesh AC Grids: A Hybrid Solution Methodology between the Salps Swarm Algorithm and Successive Approximation Power Flow Method," Sustainability, MDPI, vol. 14(20), pages 1-32, October.
    2. Daniel Sanin-Villa & Oscar Danilo Montoya & Luis Fernando Grisales-Noreña, 2023. "Material Property Characterization and Parameter Estimation of Thermoelectric Generator by Using a Master–Slave Strategy Based on Metaheuristics Techniques," Mathematics, MDPI, vol. 11(6), pages 1-19, March.
    3. Muhammad Riaz & Aamir Hanif & Haris Masood & Muhammad Attique Khan & Kamran Afaq & Byeong-Gwon Kang & Yunyoung Nam, 2021. "An Optimal Power Flow Solution of a System Integrated with Renewable Sources Using a Hybrid Optimizer," Sustainability, MDPI, vol. 13(23), pages 1-12, December.
    4. Luis Fernando Grisales-Noreña & Andrés Alfonso Rosales-Muñoz & Oscar Danilo Montoya, 2023. "An Effective Power Dispatch of Photovoltaic Generators in DC Networks via the Antlion Optimizer," Energies, MDPI, vol. 16(3), pages 1-28, January.
    5. Luis Fernando Grisales-Noreña & Andrés Alfonso Rosales-Muñoz & Brandon Cortés-Caicedo & Oscar Danilo Montoya & Fabio Andrade, 2022. "Optimal Operation of PV Sources in DC Grids for Improving Technical, Economical, and Environmental Conditions by Using Vortex Search Algorithm and a Matrix Hourly Power Flow," Mathematics, MDPI, vol. 11(1), pages 1-28, December.
    6. Luis Fernando Grisales-Noreña & Jauder Alexander Ocampo-Toro & Andrés Alfonso Rosales-Muñoz & Brandon Cortes-Caicedo & Oscar Danilo Montoya, 2022. "An Energy Management System for PV Sources in Standalone and Connected DC Networks Considering Economic, Technical, and Environmental Indices," Sustainability, MDPI, vol. 14(24), pages 1-25, December.

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