Skip to main content
Log in

Continuous optimal control approaches to microgrid energy management

  • Original Paper
  • Published:
Energy Systems Aims and scope Submit manuscript

Abstract

We propose a novel method for the microgrid energy management problem by introducing a nonlinear, continuous-time, rolling horizon formulation. The method is linearization-free and gives a global optimal solution with closed loop controls. It allows for the modelling of switches. We formulate the energy management problem as a deterministic optimal control problem (OCP). We solve (OCP) with two classical approaches: the direct method and Bellman’s Dynamic Programming Principle (DPP). In both cases we use the optimal control toolbox Bocop for the numerical simulations. For the DPP approach we implement a semi-Lagrangian scheme adapted to handle the optimization of switching times for the on/off modes of the diesel generator. The DPP approach allows for accurate modelling and is computationally cheap. It finds the global optimum in less than one second, a CPU time similar to the time needed with a Mixed Integer Linear Programming approach used in previous works. We achieve this result by introducing a ‘trick’ based on the Pontryagin Maximum Principle. The trick reduces the computation time by several orders and improves the precision of the solution. For validation purposes, we performed simulations on datasets from an actual isolated microgrid located in northern Chile. The result shows that the DPP method is very well suited for this type of problem.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. U.S. Department of Energy.: The smart grid: an introduction. https://energy.gov/oe/downloads/smart-grid-introduction-0 (2010). Accessed 1 Sept 2016

  2. Olivares, D.E., Mehrizi-Sani, A., Etemadi, A.H., Cañizares, C.A., Iravani, R., Kazerani, M., Hajimiragha, A.H., Gomis-Bellmunt, O., Saeedifard, M., Palma-Behnke, R., Jiménez-Estévez, G.A., Hatziargyriou, N.D.: Trends in microgrid control. IEEE Trans. Smart Grid 5(6), 1905–1919 (2014). doi:10.1109/TSG.2013.2295514

  3. Conti, S., Nicolosi, R., Rizzo, S.A.: Optimal dispatching of distributed generators in an MV autonomous micro-grid to minimize operating costs and emissions. In: IEEE International Symposium on Industrial Electronics, pp. 2542–2547. IEEE, Bari (2010)

  4. Hatziargyriou, N., Contaxis, G., Matos, M., Lopes, J., Kariniotakis, G., Mayer, D., Halliday, J., Dutton, G., Dokopoulos, P., Bakirtzis, A., Stefanakis, J., Gigantidou, A., O’Donnell, P., McCoy, D., Fernandes, M., Cotrim, J., Figueira, A.: Energy management and control of island power systems with increased penetration from renewable sources. In: 2002 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.02CH37309), vol. 1 (2002)

  5. Colson, C.M., Nehrir, M.H., Wang, C.: Ant colony optimization for microgrid multi-objective power management. In: 2009 IEEE/PES Power Systems Conference and Exposition. PSCE, pp. 1–7. IEEE, Seattle, WA (2009)

  6. Zhao, B., Zhang, X., Chen, J., Wang, C., Guo, L.: Operation optimization of standalone microgrids considering lifetime characteristics of battery energy storage system. IEEE Trans. Sustain. Energy 4(4), 934–943 (2013)

    Article  Google Scholar 

  7. Garcia, F., Bordons, C.: Optimal economic dispatch for renewable energy microgrids with hybrid storage using Model Predictive Control. In: IECON 2013—39th Annual Conference of the IEEE Industrial Electronics Society, pp. 7932–937. IEEE, Vienna (2013)

  8. Pourmousavi, S.A., Sharma, R.K., Asghari, B.: A framework for real-time power management of a grid-tied microgrid to extend battery lifetime and reduce cost of energy. In: 2012 IEEE PES Innovative Smart Grid Technologies. ISGT 2012 (2012)

  9. Hooshmand, A., Asghari, B., Sharma, R.: A novel cost-aware multi-objective energy management method for microgrids. In: Innovative Smart Grid Technologies (ISGT), 2013 IEEE PES, pp. 1–6. IEEE, Washington, DC (2013)

  10. Khasawneh, H.J., Illindala, M.S.: Battery cycle life balancing in a microgrid through flexible distribution of energy and storage resources. J. Power Sources 261, 378–388 (2014)

  11. Kanchev, H., Francois, B., Lazarov, V.: Unit commitment by dynamic programming for microgrid operational planning optimization and emission reduction. In: 2011 International Aegean Conference on Electrical Machines and Power Electronics and 2011 Electromotion Joint Conference (ACEMP). IEEE (2011)

  12. Liu, H., Wu, Y., Qian, C., Liu, X.: The application of dynamic programming in the stand-alone micro-grid optimal operation. In: 2012 Asia-Pacific Power and Energy Engineering Conference, pp. 1–5. IEEE, Shanghai (2012)

  13. Zhang, Y., Jia, Q.S.: Optimal operation for energy-efficient building micro-grid. In: 2015 34th Chinese Control Conference (CCC), pp. 8936–8940. IEEE, Hangzhou (2015)

  14. Babazadeh, H., Gao, W., Wu, Z., Li, Y.: Optimal energy management of wind power generation system in islanded microgrid system. In: North American Power Symposium (NAPS), 2013. IEEE (2013)

  15. Nguyen, M.Y., Yoon, Y.T., Choi, N.H.: Dynamic programming formulation of micro-grid operation with heat and electricity constraints. In: Transmission and Distribution Conference and Exposition: Asia and Pacific, 2009. IEEE (2009)

  16. Huang, C.C., Chen, M.J., Liao, Y.T., Lu, C.N.: DC microgrid operation planning. In: 2012 International Conference on Renewable Energy Research and Applications (ICRERA). IEEE (2012)

  17. Betts, J.T.: Practical Methods for Optimal Control and Estimation using Nonlinear Programming. Society for Industrial and Applied Mathematics (SIAM), Philadelphia (2010)

    Book  MATH  Google Scholar 

  18. Bellman, R.: Dynamic Programming. Princeton University Press, Princeton (1957)

    MATH  Google Scholar 

  19. Falcone, M., Ferretti, R.: Semi-Lagrangian Approximation Schemes for Linear and Hamilton-Jacobi Equations. Society for Industrial and Applied Mathematics (SIAM), Philadelphia (2014)

    MATH  Google Scholar 

  20. Bonnans, F., Martinon, P., Giorgi, D., Grélard, V., Heymann, B., Jinyan, L., Maindrault, S., Tissot, O.: Bocop—a collection of examples. Technical report (2016). http://bocop.saclay.inria.fr/

  21. Bonnans, F., Giorgi, D., Heymann, B., Martinon, P., Tissot, O.: Bocophjb 1.0. 1-User guide. Technical report (2015). https://hal.inria.fr/hal-01192610

  22. Palma-Behnke, R., Benavides, C., Lanas, F., Severino, B., Reyes, L., Llanos, J., Saez, D.: A microgrid energy management system based on the rolling horizon strategy. IEEE Trans. Smart Grid 4, 996–1006 (2013)

    Article  Google Scholar 

  23. Bryson Jr., A.E., Ho, Y.C.: Applied Optimal Control. Hemisphere Publishing Corporation, Washington (1975)

    Google Scholar 

  24. Waechter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Math. Program. 106(1), 25–57 (2006). doi:10.1007/s10107-004-0559-y

  25. Pontryagin, L., Boltyanski, V., Gamkrelidze, R., Michtchenko, E.: The Mathematical Theory of Optimal Processes. Wiley Interscience, New York (1962)

    Google Scholar 

  26. Heymann, B., Bonnans, J.F., Jiménez, G., Silva, F.: Stochastic continuous time model for microgrid energy management. In: European Control Conference (ECC), Aalborg, Denmark, June 29–July 1 (2016)

  27. Heymann, B., Martinon, p., Bonnans, F.: Long term aging : an adaptative weights dynamic programming algorithm. https://hal.archives-ouvertes.fr/hal-01349932 (2016). Accessed 1 Sept 2016

Download references

Acknowledgements

This work is product of collaboration between the COMMANDS (INRIA, France) and Centro de Energía teams (Universidad de Chile, Chile), it was also supported in part by CONICYT/FONDAP/15110019. FJS was supported by project iCODE: “Large-scale systems and Smart grids: distributed decision making” and from the Gaspar Monge Program for Optimization and Operation Research (PGMO). JFB was supported by the laboratory Dauphine CREST EDF R&D Finance des Marchés d’Energies.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Benjamin Heymann.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Heymann, B., Bonnans, J.F., Martinon, P. et al. Continuous optimal control approaches to microgrid energy management. Energy Syst 9, 59–77 (2018). https://doi.org/10.1007/s12667-016-0228-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12667-016-0228-2

Keywords

Mathematics Subject Classification

Navigation