Resource Management and Electric Vehicle Charging Stations in an MPC-Based Microgrid
DOI:
https://doi.org/10.70567/mc.v42.ocsid8588Keywords:
Optimal control, Energy management system, Economic model predictive control, Electric vehicles, Microgrid, Renewable generationAbstract
Microgrids and electric vehicles are closely related concepts, as both aim to change the energy matrix towards more environmentally friendly resources. The need for distributed charging stations that do not compromise the stability of the electricity grid is becoming increasingly necessary, and a microgrid has the appropriate structure to provide a local and efficient solution. This paper proposes an economic model predictive control strategy as an energy management system for a microgrid with vehicle charging stations. To demonstrate performance, simulations were conducted on a microgrid featuring renewable generation, a storage system, three charging stations, and an operating connection to the grid. Two modes are considered for charging points: controlled charging and the vehicle-to-grid concept. The results demonstrate correct operation in various scenarios, where the optimal control actions align with the guidelines of the proposed functional in the controller. Finally, these results will also serve to establish incentive policies in the vehicle-to-grid mode.
References
Alarcón M.A., Alarcón R.G., González A.H., y Ferramosca A. Economic model predictive control for energy management of a microgrid connected to the main electrical grid. Journal of Process Control, 117:40–51, 2022.
Alarcón R.G., Alarcón M.A., González A.H., y Ferramosca A. Artificial neural networks for energy demand prediction in an economic mpc-based energy management system. International Journal of Robust and Nonlinear Control, 35(2):642–658, 2025.
Andersson J.A.E., Gillis J., Horn G., Rawlings J.B., y Diehl M. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019.
Bhatia A.A. y Das D. Demand response strategy for microgrid energy management integrating electric vehicles, battery energy storage system, and distributed generators considering uncertainties. Sustainable Energy, Grids and Networks, 41:101594, 2025.
Ferramosca A., Limon D., y Camacho E.F. Economic mpc for a changing economic criterion for linear systems. IEEE Transactions on Automatic Control, 59(10):2657–2667, 2014.
Guo S., He J., Ma K., Yang J., Wang Y., y Li P. Robust economic dispatch for industrial microgrids with electric vehicle demand response. Renewable Energy, 240:122210, 2025.
Lasseter B. Microgrids [distributed power generation]. En 2001 IEEE power engineering society winter meeting. Conference proceedings (Cat. No. 01CH37194), volumen 1, páginas 146–149. IEEE, 2001.
Li Y., Cao X., y Li Z. Real-time energy management strategy for shore power hybrid energy supply system based on pg-mpc. Journal of Energy Storage, 125:116807, 2025.
Limón D., Alvarado I., Alamo T., y Camacho E.F. Mpc for tracking piecewise constant references for constrained linear systems. Automatica, 44(9):2382–2387, 2008.
Pereira M., Limon D., de la Peña D.M., Valverde L., y Alamo T. Periodic economic control of a nonisolated microgrid. IEEE Transactions on industrial electronics, 62(8):5247–5255, 2015.
Rawlings J.B., Mayne D.Q., y Diehl M. Model Predictive Control: Theory, Computation, and Design. Santa Barbara, California: Nob Hill Publishing, 2nd edición, 2024. ISBN 978-0-9759377-8-5.
Zheng Y., Niu S., Shang Y., Shao Z., y Jian L. Integrating plug-in electric vehicles into power grids: A comprehensive review on power interaction mode, scheduling methodology and mathematical foundation. Renewable and Sustainable Energy Reviews, 112:424–439, 2019.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Argentine Association for Computational Mechanics

This work is licensed under a Creative Commons Attribution 4.0 International License.
This publication is open access diamond, with no cost to authors or readers.
Only those papers that have been accepted for publication and have been presented at the AMCA congress will be published.

