DOI : https://doi.org/10.18517/ijaseit.10.3.11343

Modeling and Simulation of a DC Micro-Grid with a Model Predictive Controller

Vladimir Prada (1), Oscar I. Caldas (2), Edilberto Mejía-Ruda (3), Mauricio Mauledoux (4), Oscar F Avilés (5)
(1) Davinci Research Group, Universidad Militar Nueva Granada, Bogotá, Colombia
(2) Davinci Research Group, Universidad Militar Nueva Granada, Bogotá, Colombia
(3) Davinci Research Group, Universidad Militar Nueva Granada, Bogotá, Colombia
(4) Davinci Research Group, Universidad Militar Nueva Granada, Bogotá, Colombia
(5) Davinci Research Group, Universidad Militar Nueva Granada, Bogotá, Colombia
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How to cite (IJASEIT) :
[1]
V. Prada, O. I. Caldas, E. Mejía-Ruda, M. Mauledoux, and O. F. Avilés, “Modeling and Simulation of a DC Micro-Grid with a Model Predictive Controller”, Int. J. Adv. Sci. Eng. Inf. Technol., vol. 10, no. 3, pp. 1091–1098, Jun. 2020.
Citation Format :
This paper presents the modeling strategies of a micro-grid to control the power supply of a battery bank by adopting a Model Predictive Controller (MPC). The grid was sized to light two tennis courts at a university sports complex, which is not connected to the national power grid and thus must be a stand-alone setup. The paper starts with an introduction that defines the statement of purpose and the state of the art. Then continue with the power generators and storage modeling: photovoltaic (PV) modules, wind turbine, buck converter (takes power from the rectified national grid) and the battery bank. The MPC was designed to effectively manage the energy supplied to the batteries, depending on the state of charge, hence the controller output is the signal used to regulate the charging current. The data used for prediction is the meteorological measures taken during three years using an in-situ weather station that collected irradiance, wind speed and direction, temperature, and pressure. Finally, as the entire control system was simulated step by step using MATLAB/Simulink, the components and systems behavior graphs are shown to lead to analysis and conclusion remarks.

J.F. Manwell, J.G. McGowan and A.L. Rogers. Wind Energy Explained: Theory, Design and Application, Second edition. Wiley John + Sons. 2010

B. Ruí­z and V. Rodrí­guez-Padilla. Renewable energy sources in the Colombian energy policy, analysis and perspectives. Energy Policy, 34(18), 3684-3690. 2006

C. Chuanchuan, Z. Jun and J. Panpan. Multi-agent system applied to energy management system for renewable energy micro-grid. 5th IEEE International Conference on Power Electronics Systems and Applications PESA. December 2013.

F. Chen, L.V. Snyder and S. Kishore. Efficient algorithms and policies for demand response scheduling. Journal of Energy Engineering, 141(1). 2015.

M. Arnold and G. Anderson. Model predictive control of energy storage including uncertain forecasts. 17th Power Systems Computation Conference PSCC. August 2011.

C. Bordons, F. Garcí­a-Torres and L. Valverde. Optimal energy management for renewable energy microgrids. Revista Iberoamericana de Automí¡tica e Informí¡tica Industrial RIAI. 12(2), 117-132. 2015.

F. Garcí­a and C. Bordons. Regulation service for the short-term management of renewable energy microgrids with hybrid storage using model predictive control. 39th Annual Conference of the IEEE Industrial Society IECON. November, 2013.

J. Rawlings and K. Muske. The stability of constrained receding horizon control. IEEE Transactions on Automatic Control. 38(10), 1512-1516. 1993.

M.S. Tsoeu and T. Koetje. Unconstrained MPC tuning for prediction accuracy in networked control systems. IEEE International Conference on Networking, Sensing and Control. 2011.

L. Valverde, C. Bordons and F. Rosa. Power management using model predictive control in a hydrogen-based microgrid. 38th Annual Conference on IEEE Industrial Electronics Society IECON. October, 2012.

H. Bellia, R. Youcef and M. Fatima. A detailed modelling of photovoltaic module using MATLAB. Journal of Astronomy and Geophysics. 3(1), 53-61. 2014.

K. Ishaque, Z. Salam, H. Taheri and Syafaruddin. Modeling and simulation of photovoltaic (PV) system during partial shading based on a two-diode model. Simulation Modelling Practice and Theory. 19(7), 1613-1626. 2011.

M. Mauledoux, O. Avilí©s, E. Mejí­a-Ruda and O.I. Caldas. Analysis of autoregressive predictive models and artificial neural networks for irradiance estimation. Indian Journal of Science and Technology. 9(38), 1-7. 2016.

S. Georg, H. Schulte and H. Aschemann. Control-oriented modelling of wind turbines using a takagi-sugeno model structure. IEEE International Conference on Fuzzy Systems. June, 2012.

A. Abo-Khalil and D.C. Lee. Dynamic modelling and control of wind turbines for grid-connected wind generation system. 37th IEEE Power Electronics Specialists Conference. 2006.

C.Y. Tang, Y. Guo and J.N. Jiang. Nonlinear dual-mode control of variable-speed wind turbines with doubly fed induction generators. IEEE Transactions on Control Systems Technology. 19(4), 744-756. 2011.

M.S. Alam and D.W. Gao. Modelling and analysis of a wind/PV/fuel cell hybrid power system in HOMER. 2nd IEEE Conference on Industrial Electronics and Applications. Mayo, 2007.

O. Tremblay and T. Koetje. Unconstrained MPC tuning for prediction accuracy in networked control systems. IEEE International Conference on Networking, Sensing and Control. April, 2011.

A. Almendros. Regulación eólica con baterí­as en vehí­culos elí©ctricos. MasterThesis. Universidad de Sevilla, España. 2012

K. Bae, S. Choi, Y. Wong and C. Jung. LiFePO4 dynamic battery modelling for battery simulator. IEEE International Conference on Industrial Technology (ICIT). February, 2014

A. Bragado. Aní¡lisis y diseño de un rectificador trifí¡sico elevador PWM. MasterThesis Universidad Carlos III de Madrid. July 2010.

L. Herní¡ndez-Fonseca, D. Gómez-León and O. Herní¡ndez-Gómez. Rectificador monofí¡sico con corrección del factor de potencia usando un convertidor boost. Tecnura. 16(33), 23-34. 2012

J. Pí©rez, C, Níºñez and V. Cí¡rdenas. Control lineal para un rectificador monofí¡sico PWM Puente completo. RIEE&C. 7(2), 8-15. 2009

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