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A Robust Conic Programming Approximation to Design an EMS in Monopolar DC Networks with a High Penetration of PV Plants

Author

Listed:
  • Oscar Danilo Montoya

    (Grupo de Compatibilidad e Interferencia Electromagnética (GCEM), Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Bogotá 110231, Colombia)

  • Federico Martin Serra

    (Laboratorio de Control Automático (LCA), Facultad de Ingeniería y Ciencias Agropecuarias, Universidad Nacional de San Luis—CONICET, Villa Mercedes, San Luis 5730, Argentina)

  • Walter Gil-González

    (Department of Electrical Engineering, Universidad Tecnológica de Pereira, Pereira 660003, Colombia)

Abstract

This research addresses the problem regarding the efficient operation of photovoltaic (PV) plants in monopolar direct current (DC) distribution networks from a perspective of convex optimization. PV plant operation is formulated as a nonlinear programming (NLP) problem while considering two single-objective functions: the minimization of the expected daily energy losses and the reduction in the expected CO 2 emissions at the terminals of conventional generation systems. The NLP model that represents the energy management system (EMS) design is transformed into a convex optimization problem via the second-order cone equivalent of the product between two positive variables. The main contribution of this research is that it considers the uncertain nature of solar generation and expected demand curves through robust convex optimization. Numerical results in the monopolar DC version of the IEEE 33-bus grid demonstrate the effectiveness and robustness of the proposed second-order cone programming model in defining an EMS for a monopolar DC distribution network. A comparative analysis with four different combinatorial optimizers is carried out, i.e., multiverse optimization (MVO), the salp swarm algorithm (SSA), the particle swarm optimizer (PSO), and the crow search algorithm (CSA). All this is achieved while including an iterative convex method (ICM). This analysis shows that the proposed robust model can find the global optimum for two single-objective functions. The daily energy losses are reduced by 44.0082% with respect to the benchmark case, while the CO 2 emissions (kg) are reduced by 27.3771%. As for the inclusion of uncertainties, during daily operation, the energy losses increase by 22.8157%, 0.2023%, and 23.7893% with respect to the benchmark case when considering demand uncertainty, PV generation uncertainty, and both. Similarly, CO 2 emissions increase by 11.1854%, 0.9102%, and 12.1198% with regard to the benchmark case. All simulations were carried out using the Mosek solver in the Yalmip tool of the MATLAB software.

Suggested Citation

  • Oscar Danilo Montoya & Federico Martin Serra & Walter Gil-González, 2023. "A Robust Conic Programming Approximation to Design an EMS in Monopolar DC Networks with a High Penetration of PV Plants," Energies, MDPI, vol. 16(18), pages 1-17, September.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:18:p:6470-:d:1234966
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    References listed on IDEAS

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    1. Nibaldo Rodríguez & Abhishek Gupta & Paula L. Zabala & Guillermo Cabrera-Guerrero, 2018. "Optimization Algorithms Combining (Meta)heuristics and Mathematical Programming and Its Application in Engineering," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-3, September.
    2. Oscar Danilo Montoya & Luis Fernando Grisales-Noreña & Diego Armando Giral-Ramírez, 2023. "Multi-Objective Dispatch of PV Plants in Monopolar DC Grids Using a Weighted-Based Iterative Convex Solution Methodology," Energies, MDPI, vol. 16(2), pages 1-20, January.
    3. Jannet Jamii & Mohamed Trabelsi & Majdi Mansouri & Mohamed Fouazi Mimouni & Wasfi Shatanawi, 2022. "Non-Linear Programming-Based Energy Management for a Wind Farm Coupled with Pumped Hydro Storage System," Sustainability, MDPI, vol. 14(18), pages 1-17, September.
    4. Brandon Cortés-Caicedo & Luis Fernando Grisales-Noreña & Oscar Danilo Montoya & Miguel Angel Rodriguez-Cabal & Javier Alveiro Rosero, 2022. "Energy Management System for the Optimal Operation of PV Generators in Distribution Systems Using the Antlion Optimizer: A Colombian Urban and Rural Case Study," Sustainability, MDPI, vol. 14(23), pages 1-35, December.
    5. Luis Fernando Grisales-Noreña & Carlos Andrés Ramos-Paja & Daniel Gonzalez-Montoya & Gerardo Alcalá & Quetzalcoatl Hernandez-Escobedo, 2020. "Energy Management in PV Based Microgrids Designed for the Universidad Nacional de Colombia," Sustainability, MDPI, vol. 12(3), pages 1-24, February.
    6. Chu Donatus Iweh & Samuel Gyamfi & Emmanuel Tanyi & Eric Effah-Donyina, 2021. "Distributed Generation and Renewable Energy Integration into the Grid: Prerequisites, Push Factors, Practical Options, Issues and Merits," Energies, MDPI, vol. 14(17), pages 1-34, August.
    7. 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.
    8. Diego Jose da Silva & Edmarcio Antonio Belati & Jesús M. López-Lezama, 2023. "A Mathematical Programming Approach for the Optimal Operation of Storage Systems, Photovoltaic and Wind Power Generation," Energies, MDPI, vol. 16(3), pages 1-24, January.
    9. 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.
    10. Sun, Wei & Harrison, Gareth P., 2019. "Wind-solar complementarity and effective use of distribution network capacity," Applied Energy, Elsevier, vol. 247(C), pages 89-101.
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