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Hierarchical optimisation strategy in microgrid based on the consensus of multi-agent system

Hierarchical optimisation strategy in microgrid based on the consensus of multi-agent system

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To improve the automation level of distributed generation, a hierarchical optimisation strategy is proposed in this study. The strategy consists of day-ahead dispatch and scheduling implementation by power control. The energy management framework about the multi-agent system is also designed. Given the collaborative gaming process between microgrid and distributed network, a day-ahead dispatch is used to minimise the general expenses. Moreover, considering security constraints, the secondary control strategy is proposed to realise the precise control of the active power, which is adaptive to voltage inconsistency. Besides, the consensus algorithm is utilised to trace the dispatch target of tie-line power by monitoring power deviation at the point of common coupling. Finally, a series of simulation verifies the effectiveness of the method proposed. The influence of communication delay is also discussed.

References

    1. 1)
      • 1. Huang, A.Q., Crow, M.L., Heydt, G.T., et al: ‘The future renewable electric energy delivery and management (FREEDM) system: the energy internet’, Proc. IEEE., 2011, 99, (1), pp. 133148.
    2. 2)
      • 2. Rifkin, J.C.: ‘The third industrial revolution: how lateral power is transforming energy, the economy, and the world’ (Palgrave Macmillan, London, UK, 2011, 1st edn.).
    3. 3)
      • 3. Coronado Mondragon, A.E., Coronado, E.S., Coronado Mondragon, C.E.: ‘Defining a convergence network platform framework for smart grid and intelligent transport systems’, Energy., 2015, 89, (9), pp. 402411.
    4. 4)
      • 4. Chen, T., Sanchez-Aarnoutse, J.C., Buford, J.: ‘Petri net modeling of cyber-physical attacks on smart grid’, IEEE Trans. Smart Grid, 2011, 2, (4), pp. 741750.
    5. 5)
      • 5. Liang, H., Choi, B.J., Zhuang, W.H., et al: ‘Multiagent coordination in microgrids via wireless networks’, IEEE Wirel. Commun.., 2012, 19, (3), pp. 1422.
    6. 6)
      • 6. Qu, C., Chen, W., Song, J.B., et al: ‘Distributed data traffic scheduling with awareness of dynamics state in cyber physical systems with application in smart grid’, IEEE Trans. Smart Grid., 2015, PP, (99), pp. 18.
    7. 7)
      • 7. Loia, V., Vaccaro, A.: ‘Decentralized economic dispatch in smart grids by selforganizing dynamic agents’, IEEE Trans Power Syst. Man Cybern. A, Syst., 2012, 44, (4), pp. 397408.
    8. 8)
      • 8. Su, W., Wang, J., Roh, J.: ‘Stochastic energy scheduling in microgrids with intermittent renewable energy resources’, IEEE Trans. Smart Grid, 2014, 5, (4), pp. 18761883.
    9. 9)
      • 9. Zhang, X.S., Li, Q., Yu, T., et al: ‘Consensus transfer Q-learning for decentralized generation command dispatch based on virtual generation tribe’, IEEE Trans. Smart Grid, 2016, PP, (99), pp. 11.
    10. 10)
      • 10. Sharma, G., Ibraheem Niazi, K.R., et al: ‘Adaptive fuzzy critic based control design for AGC of power system connected via AC/DC tie-lines’, IET Gener. Transm. Distrib., 2017, 11, (2), pp. 560569.
    11. 11)
      • 11. Pan, I., Das, S.: ‘Fractional order AGC for distributed energy resources using robust optimization’, IEEE Trans. Smart Grid, 2016, 7, (5), pp. 21752186.
    12. 12)
      • 12. Tan, C., Liu, G.P.: ‘Consensus of discrete-time linear networked multiagent systems with communication delays’, IEEE Trans. Autom Control., 2013, 58, (11), pp. 29622983.
    13. 13)
      • 13. Yang, S., Tan, S., Xu, J.X.: ‘Consensus based approach for economic dispatch problem in a smart grid’, IEEE Trans. Power Syst., 2013, 28, (4), pp. 44164426.
    14. 14)
      • 14. Lv, T., Ai, Q.: ‘Interactive energy management of networked microgrids-based active distribution system considering large-scale integration of renewable energy resources’, Appl. Energy., 2016, 163, pp. 408422.
    15. 15)
      • 15. Diaz, G., Gonzalez-Moran, C., Gomez-Aleixandre, J., et al: ‘Scheduling of droop coefficients for frequency and voltage regulation in isolated microgrids’, IEEE Trans. Power Syst., 2010, 25, (1), pp. 489496.
    16. 16)
      • 16. Majumder, R., Chaudhuri, B., Ghosh, A., et al: ‘Improvement of stability and load sharing in an autonomous microgrid using supplementary droop control loop’, IEEE Trans. Power Syst., 2010, 25, (2), pp. 796808.
    17. 17)
      • 17. Khazaei, J., Miao, Z.: ‘Consensus control for energy storage systems’, IEEE Trans. Smart Grid, 2016, 26, (2), pp. 17.
    18. 18)
      • 18. Shafiee, Q., Nasirian, V., Vasquez, J.C., et al: ‘A multi-functional fully distributed control framework for AC microgrids’, IEEE Trans. Smart Grid, 2016, PP, (99), pp. 13.
    19. 19)
      • 19. Lu, L.Y., Chu, C.C.: ‘Consensus-based secondary frequency and voltage droop control of VSG for isolated AC micro-grids’, IEEE J. Emerg. Sel. Top. Circuits Syst., 2015, 5, (3), pp. 443455.
    20. 20)
      • 20. Yang, J., Jin, X., Wu, X., et al: ‘Decentralised control method for DC microgrids with improved current sharing accuracy’, IET Gener. Transm. Distrib., 2016, 11, (3), pp. 696706.
    21. 21)
      • 21. Li, L., Ho, D.W.C., Xu, S.Y.: ‘A distributed event-triggered scheme for discrete-time multiagent consensus with communication delays’, IET Contr Theory Appl., 2014, 8, (10), pp. 830836.
    22. 22)
      • 22. Binetti, G., Davoudi, A., Lewis, F.L., et al: ‘Distributed consensus-based economic dispatch with transmission losses’, IEEE Trans. Power Syst., 2014, 29, (4), pp. 17111720.
    23. 23)
      • 23. Liu, S., Xie, L., Zhang, H.: ‘Distributed consensus for multiagent systems with delays and noises in transmission channels’, Automatica., 2011, 47, (5), pp. 920934.
    24. 24)
      • 24. Zhang, Z., Chow, M.Y.: ‘Convergence analysis of the incremental cost consensus algorithm under different communication network topologies in a smart grid’, IEEE Trans. Power Syst., 2012, 27, (4), pp. 17611768.
    25. 25)
      • 25. Bidram, A., Davoudi, A., Lewis, F.L., et al: ‘Secondary control of microgrids based on distributed cooperative control of multi-agent systems’, IET Gener. Transm. Distrib., 2013, 7, (8), pp. 822831.
    26. 26)
      • 26. Meiqin, M., Meihong, J., Wei, D., et al: ‘Multi-objective economic dispatch model for a microgrid considering reliability’. Proc. Int. Conf. Power Electronics for Distributed Generation Systems (PEDG), Austin, Texas, June 2010, pp. 993998.
    27. 27)
      • 27. Shamsi, P., Xie, H., Longe, A., et al: ‘Economic dispatch for an agent-based community microgrid’, IEEE Trans. Smart Grid, 2016, 7, (5), pp. 23172324.
    28. 28)
      • 28. Zhang, Z., Wang, J., Ding, T., et al: ‘A two-layer model for microgrid real-time dispatch based on energy storage system charging/discharging hidden costs’, IEEE Trans. Sust. Energy, 2017, 8, (1), pp. 3342.
    29. 29)
      • 29. Zhang, M., Chen, J.: ‘The energy management and optimized operation of electric vehicles based on microgrid’, IEEE Trans. Power Deliv., 2014, 29, (3), pp. 14271435.
    30. 30)
      • 30. Wang, L., Xiao, F.: ‘A new approach to consensus problems in discrete-time multiagent systems with time-delays’, Sci. China Ser. F, Inf. Sci., 2007, 50, (4), pp. 625635.
    31. 31)
      • 31. Wang, R., Wang, D., Jia, H., et al: ‘A battery and virtual energy storage coordinated control strategy for stabilizing power fluctuation of micro - network tie–line’, Proc. CSEE, 2015, 35, (20), pp. 51245134(in Chinese).
    32. 32)
      • 32. Xu, J., Zhang, G., Zeng, J., et al: ‘Robust guaranteed cost consensus for high-order discrete-time multi-agent systems with parameter uncertainties and time-varying delays’, IET Control Theory Applic., 2017, 11, (5), pp. 647667.
    33. 33)
      • 33. Gündüz, H., Sönmez, Ş., Ayasun, S.: ‘Comprehensive gain and phase margins based stability analysis of micro-grid frequency control system with constant communication time delays’, IET Gener. Transm. Distrib., 2016, 11, (3), pp. 719729.
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