This study presents an advanced control structure aimed at the optimal economic energy management of a renewable energy-based microgrid. This control scheme is applied to energy optimisation in a microgrid with non-dispatchable renewable sources, such as photovoltaic and wind power generation, as well as dispatchable sources, as distributed generators, hybrid storage systems compound by battery bank, supercapacitors, hydrogen storage unit, and one electric vehicle charging station. The proposed controller consists of a Lyapunov-based hybrid model predictive control based on mixed logical dynamical (MLD) framework. The main contribution of the proposed technique is the assurance of the closed-loop stability and recursive feasibility, by a novel approach focused on MLD models, using ellipsoidal terminal constraints and the Lyapunov decreasing condition. Finally, simulation tests under different operational conditions are performed and the attained results have shown the safe and reliable operation of the proposed control algorithm compared to existing and well-known energy management techniques.

References

1. 1)
  - 24. ‘Sonda – national environmental data organization system’. Available from: http://sonda.ccst.inpe.br.
2. 2)
  - 9. Parisio, A., Rikos, E., Glielmo, L.: ‘A model predictive control approach to microgrid operation optimization’, IEEE Trans. Control Syst. Technol., 2014, 22, (5), pp. 1813–1827.
3. 3)
  - 21. Boyd, S., Vandenberghe, L.: ‘Convex optimization’ (Cambridge University Press, Cambridge, UK, 2004).
4. 4)
  - 14. Mendes, P.R., Maestre, J.M., Bordons, C., et al: ‘A practical approach for hybrid distributed MPC’, J. Process Control, 2017, 55, pp. 30–41.
5. 5)
  - 15. Di Cairano, S., Heemels, W.M.H., Lazar, M., et al: ‘Stabilizing dynamic controllers for hybrid systems: a hybrid control lyapunov function approach’, IEEE Trans. Autom. Control, 2014, 59, (10), pp. 2629–2643.
6. 6)
  - 11. Hernández Hernández, C., Rodríguez, F., Moreno, J.C., et al: ‘The comparison study of short-term prediction methods to enhance the model predictive controller applied to microgrid energy management’, Energies, 2017, 10, (7), p. 884.
7. 7)
  - 16. Torrisi, F.D., Bemporad, A.: ‘Hysdel-a tool for generating computational hybrid models for analysis and synthesis problems’, IEEE Trans. Control Syst. Technol., 2004, 12, (2), pp. 235–249.
8. 8)
  - 19. Borrelli, F., Bemporad, A., Morari, M.: ‘Predictive control for linear and hybrid systems’ (Cambridge University Press, Cambridge, UK, 2017).
9. 9)
  - 5. Garcia Torres, F., Bordons, C., Ridao, M.A.: ‘Optimal economic schedule for a network of microgrids with hybrid energy storage system using distributed model predictive control’, IEEE Trans. Ind. Electron., 2018, doi: 10.1109/TIE.2018.2826476.
10. 10)
  - 1. Hao, R., Jiang, Z., Ai, Q., et al: ‘Hierarchical optimisation strategy in microgrid based on the consensus of multi-agent system’, IET Gener. Transm. Distrib., 2018, 12, (10), pp. 2444–2451.
11. 11)
  - 20. Bemporad, A., Morari, M.: ‘Control of systems integrating logic, dynamics, and constraints’, Automatica, 1999, 35, (3), pp. 407–427.
12. 12)
  - 8. Mendes, P.R.C., Isorna, L.V., Bordons, C., et al: ‘Energy management of an experimental microgrid coupled to a v2g system’, J. Power Sources, 2016, 327, pp. 702–713.
13. 13)
  - 3. Qiu, J., Zhao, J., Zheng, Y., et al: ‘Optimal allocation of BESS and MT in a microgrid’, IET Gener. Transm. Distrib., 2018, 12, (9), pp. 1988–1997.
14. 14)
  - 13. Pereira, M., Limon, D., de la Peña, D.M., et al: ‘Periodic economic control of a nonisolated microgrid’, IEEE Trans. Ind. Electron., 2015, 62, (8), pp. 5247–5255.
15. 15)
  - 6. Garcia Torres, F., Valverde, L., Bordons, C.: ‘Optimal load sharing of hydrogen-based microgrids with hybrid storage using model-predictive control’, IEEE Trans. Ind. Electron., 2016, 63, (8), pp. 4919–4928.
16. 16)
  - 23. Mayne, D.Q.: ‘Model predictive control: recent developments and future promise’, Automatica, 2014, 50, (12), pp. 2967–2986.
17. 17)
  - 17. Del Real, A., Galus, M., Bordons, C., et al: ‘Optimal power dispatch of energy networks including external power exchanges’. Control Conf. (ECC), 2009 European IEEE, Budapest, Hungary, August 2009, pp. 3616–3621.
18. 18)
  - 2. Ranga, V.P.S.R.V., Sesha, S., Kesanakurthy, S.S.: ‘Model predictive control approach for frequency and voltage control of standalone micro-grid’, IET Gener. Transm. Distrib., 2018, 12, (14), pp. 3405–3413.
19. 19)
  - 12. Alavi, F., Lee, E.P., van de Wouw, N., et al: ‘Fuel cell cars in a microgrid for synergies between hydrogen and electricity networks’, Appl. Energy, 2017, 192, pp. 296–304.
20. 20)
  - 7. Garcia Torres, F., Bordons, C.: ‘Optimal economical schedule of hydrogen-based microgrids with hybrid storage using model predictive control’, IEEE Trans. Ind. Electron., 2015, 62, (8), pp. 5195–5207.
21. 21)
  - 18. Valverde, L., Rosa, F., Bordons, C.: ‘Design, planning and management of a hydrogen-based microgrid’, IEEE Trans. Ind. Inf., 2013, 9, (3), pp. 1398–1404.
22. 22)
  - 10. Clarke, W.C., Manzie, C., Brear, M.J.: ‘An economic mpc approach to microgrid control’. 2016 Australian Control Conf. (AuCC), Newcastle, NSW, Australia, November 2016, pp. 276–281.
23. 23)
  - 22. Zhao, Y., Liu, S.: ‘Global optimization algorithm for mixed integer quadratically constrained quadratic program’, J. Comput. Appl. Math., 2017, 319, pp. 159–169.
24. 24)
  - 4. Olivares, D.E., Mehrizi Sani, A., Etemadi, A.H., et al: ‘Trends in microgrid control’, IEEE Trans. Smart Grid, 2014, 5, (4), pp. 1905–1919.

Lyapunov-based hybrid model predictive control for energy management of microgrids

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