Stochastic Energy Planning of Microgrids with MPC via Multi-Scenario Probabilistic Analysis with: UncertaintyManagement
DOI:
https://doi.org/10.17979/ja-cea.2026.47.13635Keywords:
Predictive control, Complex systems, Energy systems, Smart grids, Optimal operation and control of power systemsAbstract
This work presents and validates a procedure for managing the uncertainty inherent in renewable energy generation within microgrids equipped with energy storage systems. The proposed method integrates a Multi-Scenario Stochastic Model Predictive Control (MS-SMPC) scheme with probabilistic constraints. This methodology enhances microgrid planning under uncertainty and increases operational flexibility. The optimization problem is formulated and solved as a set of Mixed-Integer Quadratic Programming (MIQP) subproblems, ensuring that a consistent energy exchange with the main grid is maintained across all considered scenarios. Notably, the approach addresses not only economic criteria but also improvements in system operability. The results show that this procedure enhances the reliability and flexibility of the microgrid under uncertain conditions.
References
[1] P. Pinson, C. Chevallier, and G. N. Kariniotakis, “Trading wind generation from short-term probabilistic forecasts of wind power,” IEEE Transactions on Power Systems, vol. 22. no. 3. pp. 1148–1156. 2007.
[2] H. Holttinen, “Optimal electricity market for wind power,” Energy Policy, vol. 33. no. 16. pp. 2052–2063. 2005.
[3] F. Garcia-Torres and C. Bordons “Optimal economical schedule of Hydrogen-Based microgrids with Hybrid Storage using Model Predictive Control,” IEEE Trans. Ind. Electron, vol. 62. no. 8. pp. 5195-5207. 2015.
[4] Y. Yang, Y. Ji, G. Geng, Q. Jiang, “Probabilistic revenue analysis of microgrid considering source-load and forecast uncertainties” IEEE Access, 10. 2469-2479. 2022.
[5] Y. Huang, L. Wang, W. Guo, Q. Kang, Q. Wu, “Chance constraint optimization in a home energy management system” IEEE Trans. Smart Grid, 9 (1). 252-260. 2018.
[6] P. Zhao, H. Wu, C. Gu, I. Hernando, “Optimal home energy management under hybrid photovoltaic-storage uncertainty: a distributionally robust chance-constraint approach” IET Renew. Power Gener. 13. 1911-1919. 2019.
[7] H. Wang, Z. Yan, M. Shahidehpour, X. Xu and Q. Zhou, “Quantitative evaluations of uncertainties in multivariate operation of microgrids,” IEEE Trans. Smart Grid, vol. 11. no. 4. pp. 2892-2903. 2020.
[8] A. Khodaei, S. Bahramirad and M. Shahidehpour, “Microgrid planning under uncertainty,” IEEE Trans. Pow. Syst., vol. 30. no. 5. 2417-2425. 2015.
[9] H. Qiu, H. Long, W. Gu and G. Pan, “Recourse-Cost constrained robust optimization for microgrid dispatch with correlated uncertainties,” IEEE Trans. Ind. Electr., vol. 68. no. 3. pp. 2266-2278. 2020.
[10] H. Qiu, W. Gu, Y. Xu, W. Yu, G. Pan and P. Liu, “Tri-level mixed integer optimization for two stage microgrid dispatch with uncertainties,” IEEE Trans. Pow. Syst., vol. 35. no. 5. pp. 3636-3647. 2020.
[11] H. Farzin, M. Fotuhi-Firuzabad and M. Moeini-Aghtaie, “A stochastic multi-objective framework for optimal scheduling of energy storage systems in microgrids,” IEEE Trans. Smart Grid, vol. 8. no. 1. pp. 117-127. 2017.
[12] L. Alvarado-Barrios, Á. R. del Nozal, J. B. Valerino, I. Garcia Vera and J. L. Martinez-Ramos, “Stochastic unit commitment in microgrids: Influence of the load forecasting error and the availability of energy storage”, Renew. Energy, vol. 146. pp. 2060-2069. 2020.
[13] Y. Zhang, S. Shen and J. L. Mathieu, “Distributionally robust chance constraint optimal power flow with uncertain renewables and uncertain reserves provides by load”, IEEE Trans. Power Syst., 32(2). 1378-1388. 2017.
[14] C. Bordons, F. Garcia-Torres and M.A. Ridao, Model Predictive Control of Microgrids, Cham, Switzerland: Springer, 2020.
[15] A. Parisio, E. Rikos and L. Glielmo, “Stochastic model predictive control for economic/environmental operation management of microgrids: An experimental case of study,” J. Proc. Control, 43. pp. 24-37. 2016.
[16] S.R. Cominesi, M. Farina, L. Giulioni, B. Picasso and R. Scattolini, “A two-layer stochastic model predictive control scheme for microgrids,” IEEE Trans. Control Syst. Tech., vol. 26. no. 1. pp. 1-13. 2017.
[17] N. Bazmohammadi, A. Tahsiri, A. Anvari-Moghaddam and J. M. Guerrero, “Stochastic predictive control of muti-microgrid systems,” IEEE Trans. Ind. App., vol. 55. no. 5. pp.5311-5319. 2019.
[18] P. Velarde, L. Valverde, J. Maestre, C. M. Ocampo-Martinez and C. Bordons, “On the comparison of stochastic model predictive control strategies applied to a hydrogen-based microgrid,” J. Pow. Sources, 343. 161-173. 2017.
[19] C. Lu, X. Zhang, X. Wang,Y. Han, “Mathematical expectation modeling of Wide-Area Controlled power systems with stochastic time delay,” IEEE Trans. Smart Grid, vol. 6. no. 3. pp. 1511-1519. 2015.
[20] M.I. Saleem, S. Saha, U. Izhar, L. Ang, “A stochastic MPC-Based energy management system for integrating solar PV, Battery storage and EV charging in residential complexes”, Energy & buildings 325. 2024.
[21] F. García-Torres, C. Bordons, J. Tobajas, R. Real-Calvo, I. Santiago and S. Greu, “Stochastic optimization of microgrids with hybrid energy storage systems for grid flexibility services considering energy forecast uncertainties” IEEE Trans. Power Syst., vol. 36. no. 6. pp. 5537-5547. 2021
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Copyright (c) 2026 Víctor Girona-García, Antonio Enrique González Reina, Álvaro Sánchez-Sánchez-de-Puerta, Francisco J. Vázquez Serrano, Jorge E. Jiménez-Hornero, Félix García-Torres
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