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Economic Dispatch of Renewable Generators and BESS in DC Microgrids Using Second-Order Cone Optimization

Author

Listed:
  • Walter Gil-González

    (Laboratorio Inteligente de Energía, Universidad Tecnológica de Bolívar, km 1 vía Turbaco, Cartagena 131001, Colombia)

  • Oscar Danilo Montoya

    (Laboratorio Inteligente de Energía, Universidad Tecnológica de Bolívar, km 1 vía Turbaco, Cartagena 131001, Colombia
    Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Carrera 7 No. 40B-53, Bogotá D.C 11021, Colombia)

  • Luis Fernando Grisales-Noreña

    (Grupo GIIEN, Facultad de Ingeniería, Institución Universitaria Pascual Bravo, Campus Robledo, Medellín 050036, Colombia)

  • Fernando Cruz-Peragón

    (Departamento de Ingeniería Mecánica y Minera, Universidad de Jaén, Campus Las Lagunillas s/n. 23071 Jaén, Spain)

  • Gerardo Alcalá

    (Centro de Investigación en Recursos Energéticos y Sustentables, Universidad Veracruzana, Coatzacoalcos, Veracruz 96535, Mexico)

Abstract

A convex mathematical model based on second-order cone programming (SOCP) for the optimal operation in direct current microgrids (DCMGs) with high-level penetration of renewable energies and battery energy storage systems (BESSs) is developed in this paper. The SOCP formulation allows converting the non-convex model of economic dispatch into a convex approach that guarantees the global optimum and has an easy implementation in specialized software, i.e., CVX. This conversion is accomplished by performing a mathematical relaxation to ensure the global optimum in DCMG. The SOCP model includes changeable energy purchase prices in the DCMG operation, which makes it in a suitable formulation to be implemented in real-time operation. An energy short-term forecasting model based on a receding horizon control (RHC) plus an artificial neural network (ANN) is used to forecast primary sources of renewable energy for periods of 0.5h. The proposed mathematical approach is compared to the non-convex model and semidefinite programming (SDP) in three simulation scenarios to validate its accuracy and efficiency.

Suggested Citation

  • Walter Gil-González & Oscar Danilo Montoya & Luis Fernando Grisales-Noreña & Fernando Cruz-Peragón & Gerardo Alcalá, 2020. "Economic Dispatch of Renewable Generators and BESS in DC Microgrids Using Second-Order Cone Optimization," Energies, MDPI, vol. 13(7), pages 1-15, April.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:7:p:1703-:d:341210
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    References listed on IDEAS

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    10. Oscar Danilo Montoya & Walter Gil-González & Luis Grisales-Noreña & César Orozco-Henao & Federico Serra, 2019. "Economic Dispatch of BESS and Renewable Generators in DC Microgrids Using Voltage-Dependent Load Models," Energies, MDPI, vol. 12(23), pages 1-20, November.
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    Cited by:

    1. Md. Fatin Ishraque & Sk. A. Shezan & Md. Sohel Rana & S. M. Muyeen & Akhlaqur Rahman & Liton Chandra Paul & Md. Shafiul Islam, 2021. "Optimal Sizing and Assessment of a Renewable Rich Standalone Hybrid Microgrid Considering Conventional Dispatch Methodologies," Sustainability, MDPI, vol. 13(22), pages 1-23, November.
    2. Belqasem Aljafari & Subramanian Vasantharaj & Vairavasundaram Indragandhi & Rhanganath Vaibhav, 2022. "Optimization of DC, AC, and Hybrid AC/DC Microgrid-Based IoT Systems: A Review," Energies, MDPI, vol. 15(18), pages 1-30, September.
    3. Giacomo Talluri & Gabriele Maria Lozito & Francesco Grasso & Carlos Iturrino Garcia & Antonio Luchetta, 2021. "Optimal Battery Energy Storage System Scheduling within Renewable Energy Communities," Energies, MDPI, vol. 14(24), pages 1-23, December.
    4. Cristian Hoyos-Velandia & Lina Ramirez-Hurtado & Jaime Quintero-Restrepo & Ricardo Moreno-Chuquen & Francisco Gonzalez-Longatt, 2022. "Cost Functions for Generation Dispatching in Microgrids for Non-Interconnected Zones in Colombia," Energies, MDPI, vol. 15(7), pages 1-14, March.
    5. Oscar Danilo Montoya & Walter Gil-González & Edwin Rivas-Trujillo, 2020. "Optimal Location-Reallocation of Battery Energy Storage Systems in DC Microgrids," Energies, MDPI, vol. 13(9), pages 1-20, May.
    6. Zixia Yuan & Guojiang Xiong & Xiaofan Fu, 2022. "Artificial Neural Network for Fault Diagnosis of Solar Photovoltaic Systems: A Survey," Energies, MDPI, vol. 15(22), pages 1-18, November.
    7. Sergio Cantillo-Luna & Ricardo Moreno-Chuquen & Francisco Gonzalez-Longatt & Harold R. Chamorro, 2022. "A Type-2 Fuzzy Controller to Enable the EFR Service from a Battery Energy Storage System," Energies, MDPI, vol. 15(7), pages 1-13, March.
    8. O. D. Montoya & W. Gil-González & J. C. Hernández & D. A. Giral-Ramírez & A. Medina-Quesada, 2020. "A Mixed-Integer Nonlinear Programming Model for Optimal Reconfiguration of DC Distribution Feeders," Energies, MDPI, vol. 13(17), pages 1-22, August.
    9. Marija Miletić & Hrvoje Pandžić & Dechang Yang, 2020. "Operating and Investment Models for Energy Storage Systems," Energies, MDPI, vol. 13(18), pages 1-33, September.
    10. Chaoyang Chen & Hualing Liu & Yong Xiao & Fagen Zhu & Li Ding & Fuwen Yang, 2022. "Power Generation Scheduling for a Hydro-Wind-Solar Hybrid System: A Systematic Survey and Prospect," Energies, MDPI, vol. 15(22), pages 1-31, November.
    11. Wei Dai & Yang Gao & Hui Hwang Goh & Jiangyi Jian & Zhihong Zeng & Yuelin Liu, 2024. "A Non-Iterative Coordinated Scheduling Method for a AC-DC Hybrid Distribution Network Based on a Projection of the Feasible Region of Tie Line Transmission Power," Energies, MDPI, vol. 17(6), pages 1-20, March.
    12. Maria Carmela Di Piazza, 2022. "Recent Developments and Trends in Energy Management Systems for Microgrids," Energies, MDPI, vol. 15(21), pages 1-6, November.

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