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Article

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

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Laboratorio Inteligente de Energía, Universidad Tecnológica de Bolívar, km 1 vía Turbaco, Cartagena 131001, Colombia
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Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Carrera 7 No. 40B-53, Bogotá D.C 11021, Colombia
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Grupo GIIEN, Facultad de Ingeniería, Institución Universitaria Pascual Bravo, Campus Robledo, Medellín 050036, Colombia
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Departamento de Ingeniería Mecánica y Minera, Universidad de Jaén, Campus Las Lagunillas s/n. 23071 Jaén, Spain
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Centro de Investigación en Recursos Energéticos y Sustentables, Universidad Veracruzana, Coatzacoalcos, Veracruz 96535, Mexico
*
Author to whom correspondence should be addressed.
Energies 2020, 13(7), 1703; https://doi.org/10.3390/en13071703
Received: 13 February 2020 / Revised: 23 March 2020 / Accepted: 27 March 2020 / Published: 3 April 2020
(This article belongs to the Special Issue Solar and Wind Power and Energy Forecasting)
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. View Full-Text
Keywords: second-order cone programming; economic dispatch problem; artificial neural networks; battery energy storage system second-order cone programming; economic dispatch problem; artificial neural networks; battery energy storage system
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MDPI and ACS Style

Gil-González, W.; Montoya, O.D.; Grisales-Noreña, L.F.; Cruz-Peragón, F.; Alcalá, G. Economic Dispatch of Renewable Generators and BESS in DC Microgrids Using Second-Order Cone Optimization. Energies 2020, 13, 1703. https://doi.org/10.3390/en13071703

AMA Style

Gil-González W, Montoya OD, Grisales-Noreña LF, Cruz-Peragón F, Alcalá G. Economic Dispatch of Renewable Generators and BESS in DC Microgrids Using Second-Order Cone Optimization. Energies. 2020; 13(7):1703. https://doi.org/10.3390/en13071703

Chicago/Turabian Style

Gil-González, Walter, Oscar D. Montoya, Luis F. Grisales-Noreña, Fernando Cruz-Peragón, and Gerardo Alcalá. 2020. "Economic Dispatch of Renewable Generators and BESS in DC Microgrids Using Second-Order Cone Optimization" Energies 13, no. 7: 1703. https://doi.org/10.3390/en13071703

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