The use of Model Predictive Control (MPC) in the optimal distribution of electrical energy in a microgrid located in southeastern of Spain: A case study simulation César Hernández-Hernández, Francisco Rodríguez, José Carlos Moreno, Paulo Renato da Costa Mendes and Julio Elías Normey-Rico 2017/04/25

Abstrac

The microgrids allow the integration of renewable sources of energy such as solar and wind and distributed energy resources such as combined heat and power, energy storage, and demand response. In addition, the use of local sources of energy to serve local loads helps reduce energy losses in transmission and distribution, further increasing efficiency of the electric delivery system. In this paper, the optimization problem of the energy in a microgrid (MG) located in southeastern of Spain, with Energy Storage System (ESS), which exchanges energy with the utility grid is developed using Model Predictive Control techniques. System modelling use the methodology of the Energy Hubs. The MPC techniques allow maximizing the economic benefit of the microgrid and to minimize the degradation of storage system.

Authors and affiliations

César Hernández-Hernández(1), Francisco Rodríguez(1), José Carlos Moreno(1), Paulo Renato da Costa Mendes(2) and Julio Elías Normey-Rico(2)
1. Department of Informatics, Agrifood Campus of International Excellence ciaA3, CIESOL Research Center on Solar Energy, University of Almeria, (Spain)
2 Federal University of Santa Catarina, Florianopolis, Brazil

Key word

Microgrid, Energy Hubs, Model Predictive Control.

References

[1] A. Karabiber, C. Keles, A. Kaygusuz, and B. B. Alagoz, “An approach for the integration of renewable distributed generation in hybrid DC/AC microgrids,” Renew. Energy, vol. 52, pp. 251–259, 2013.
[2] M. S. Carmeli, F. Castelli-Dezza, M. Mauri, G. Marchegiani, and D. Rosati, “Control strategies and configurations of hybrid distributed generation systems,” Renew. Energy, vol. 41, pp. 294–305, 2012.
[3] K. Kurohane, A. Uehara, T. Senjyu, A. Yona, N. Urasaki, T. Funabashi, and C.-H. Kim, “Control strategy for a distributed DC power system with renewable energy,” Renew. Energy, vol. 36, no. 1, pp. 42–49, 2011.
[4] H. Morais, P. Kádár, P. Faria, Z. A. Vale, and H. M. Khodr, “Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming,” Renew. Energy, vol. 35, no. 1, pp. 151–156, 2010.
[5] E. Dursun and O. Kilic, “Comparative evaluation of different power management strategies of a stand-alone PV/Wind/PEMFC hybrid power system,” Int. J. Electr. Power Energy Syst., vol. 34, no. 1, pp. 81–89, 2012.
[6] B. Indu Rani, G. Saravana Ilango, and C. Nagamani, “Power flow management algorithm for photovoltaic systems feeding DC/AC loads,” Renew. Energy, vol. 43, pp. 267–275, 2012.
[7] H. Chitsaz, H. Shaker, H. Zareipour, D. Wood, and N. Amjady, “Short-term Electricity Load Forecasting of Buildings in Microgrids,” Energy Build., vol. 99, pp. 50–60, Apr. 2015.
[8] W. Huang, M. Lu, and L. Zhang, “Survey on microgrid control strategies,” Energy Procedia, vol. 12, pp. 206–212, 2011.
[9] A. Etxeberria, I. Vechiu, H. Camblong, and J.-M. Vinassa, “Hybrid energy storage systems for renewable energy sources integration in microgrids: A review,” in IPEC, 2010 Conference Proceedings, 2010, pp. 532–537.
[10] M. H. Nehrir, C. Wang, K. Strunz, H. Aki, R. Ramakumar, J. Bing, Z. Miao, and Z. Salameh, “A review of hybrid renewable/alternative energy systems for electric power generation: Configurations, control, and applications,” Sustain. Energy, IEEE Trans., vol. 2, no. 4, pp. 392–403, 2011.
[11] W. Gu, Z. Wu, and X. Yuan, “Microgrid economic optimal operation of the combined heat and power system with renewable energy,” IEEE Power Energy Soc. Gen. Meet., pp. 1–6, 2010.
[12] H. Karami, M. J. Sanjari, S. H. Hosseinian, and G. B. Gharehpetian, “An Optimal Dispatch Algorithm for Managing Residential Distributed Energy Resources,” IEEE Trans. Smart Grid, vol. 5, no. 5, pp. 2360–2367, Sep. 2014.
[13] G. Ferrari-Trecate, E. Gallestey, P. Letizia, M. Spedicato, M. Morari, and M. Antonine, “Modeling and control of co-generation power plants: a hybrid system approach,” IEEE Trans. Control Syst. Technol., vol. 12, no. 5, pp. 694–705, 2004.
[14] L. Valverde, C. Bordons, and F. Rosa, “Power Management using Model Predictive Control in a hydrogen-based microgrid,” Annu. Conf. IEEE Ind. Electron. Soc., pp. 5669–5676, 2012.
[15] E. F. Camacho and C. B. Alba, Model predictive control. Springer Science & Business Media, 2013.
[16] M. Geidl, G. Koeppel, P. Favre-Perrod, B. Klockl, G. Andersson, and K. Frohlich, “Energy hubs for the future,” IEEE Power Energy Mag., vol. 5, no. 1, p. 24, 2007.
[17] M. Geidl and G. Andersson, “A modeling and optimization approach for multiple energy carrier power flow,” in Power Tech, 2005 IEEE Russia, 2005, pp. 1–7.
[18] P. R. da C. Mendes, J. E. Normey-Rico, and C. B. Alba, “Economic energy management of a microgrid including electric vehicles,” in Innovative Smart Grid Technologies Latin America (ISGT LATAM), 2015 IEEE PES, 2015, pp. 869–874.
[19] P. R. C. Mendes, L. V. Isorna, C. Bordons, and J. E. Normey-Rico, “Energy management of an experimental microgrid coupled to a V2G system,” J. Power Sources, vol. 327, pp. 702–713, 2016.
[20] A. Bemporad and M. Morari, “Control of systems integrating logic, dynamics, and constraints,” Automatica, vol. 35, no. 3, pp. 407–427, 1999.
[21] MATLAB, version 8.4.0 (R2014b). The MathWorks Inc., 2014.
[22] J. Lofberg, “YALMIP : A Toolbox for Modeling and Optimization in MATLAB.,” in CACSD Conference, Taipei, Taiwan., 2004.
[23] ILOG, “CPLEX,” 2007.