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Coordinated predictive control for wind farm with BESS considering power dispatching and equipment ageing

Coordinated predictive control for wind farm with BESS considering power dispatching and equipment ageing

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This study presents a supervisory model predictive control (MPC) scheme for coordinated active power control of a wind farm with a centralised battery energy storage system (BESS). The control target is wind turbine generator (WTG) and BESS coordination to respond to transmission system operator power dispatching, along with equipment ageing deceleration. To alleviate the WTG mechanical fatigue and extend the BESS expected lifetime, the twist angle variation and an index introduced by a weighted ampere-hour (Ah) throughput model are considered in the cost function. The damage equivalent load and weighted Ah throughput are used for the equipment ageing evaluation. Simulations and comparisons are conducted to demonstrate the effectiveness and applicability of the proposed MPC-based supervisory controller design.

References

    1. 1)
      • 1. Wang, X.Y., Vilathgamuwa, D.M., Choi, S.S.: ‘Determination of battery storage capacity in energy buffer for wind farm’, IEEE Trans. Energy Convers., 2008, 23, (3), pp. 868878.
    2. 2)
      • 2. Teleke, S., Baran, M.E., Bhattacharya, S., et al: ‘Optimal control of battery energy storage for wind farm dispatching’, IEEE Trans. Energy Convers., 2010, 25, (3), pp. 787794.
    3. 3)
      • 3. Chang-Chien, L.R., Sun, C.C., Yeh, Y.J.: ‘Modeling of wind farm participation in AGC’, IEEE Trans. Power Syst., 2014, 29, (3), pp. 12041211.
    4. 4)
      • 4. Tsili, M., Papathanassiou, S.: ‘A review of grid code technical requirements for wind farms’, IET Renew. Power Gener., 2009, 3, (3), pp. 308332.
    5. 5)
      • 5. Zhao, H., Wu, Q., Guo, Q., et al: ‘Distributed model predictive control of a wind farm for optimal active power control – part II’, IEEE Trans. Sust. Energy, 2015, 6, (3), pp. 840849.
    6. 6)
      • 6. Zhao, H., Wu, Q., Guo, Q., et al: ‘Optimal active power control of a wind farm equipped with energy storage system based on distributed model predictive control’, IET Gener. Transm. Distrib., 2016, 10, (3), pp. 669677.
    7. 7)
      • 7. Teleke, S., Baran, M.E., Bhattacharya, S., et al: ‘Rule-based control of battery energy storage for dispatching intermittent renewable sources’, IEEE Trans. Sust. Energy, 2010, 1, (3), pp. 117124.
    8. 8)
      • 8. Khalid, M., Savkin, A.V.: ‘An optimal operation of wind energy storage system for frequency control based on model predictive control’, Renew. Energy, 2012, 48, (6), pp. 127132.
    9. 9)
      • 9. Khatamianfar, A., Khalid, M., Savkin, A.V., et al: ‘Improving wind farm dispatch in the Australian electricity market with battery energy storage using model predictive control’, IEEE Trans. Sust. Energy, 2013, 4, (3), pp. 745755.
    10. 10)
      • 10. Kou, P., Liang, D., Gao, F., et al: ‘Coordinated predictive control of DFIG-based wind-battery hybrid systems: using non-Gaussian wind power predictive distributions’, IEEE Trans. Energy Conver., 2015, 30, (2), pp. 681695.
    11. 11)
      • 11. Cai, Y., Lin, J., Song, Y., et al: ‘Model predictive control- based wind farm power control with energy storage’. Int. Conf. Power System Technolology, Chengdu, China, December 2014, pp. 26742679.
    12. 12)
      • 12. Tan, J., Zhang, Y.: ‘Coordinated control strategy of a battery energy storage system to support a wind power plant providing multi-timescale frequency ancillary services’, IEEE Trans. Sust. Energy, 2017, 8, (3), pp. 11401153.
    13. 13)
      • 13. Akhmatov, V.: ‘Analysis of dynamic behaviour of electric power systems with large amount of wind power’. PhD thesis, Technical University of Denmark, 2003.
    14. 14)
      • 14. Spudić, V., Baotić, M., Jelavić, M.: ‘Wind turbine power references in coordinated control of wind farms’, Automatika J. Control Meas. Electron. Comput. Commun., 2011, 52, (2), pp. 8294.
    15. 15)
      • 15. Zhao, H., Wu, Q., Huang, S., et al: ‘Fatigue load sensitivity based optimal active power dispatch for wind farms’, IEEE Trans. Sust. Energy, 2017, 8, (3), pp. 12471259.
    16. 16)
      • 16. Aspragathos, N., Dimarogonas, A.D.: ‘Fatigue damage of turbine-generator shafts due to fast reclosing’, IEE Proc. C, 1982, 129, (1), pp. 19.
    17. 17)
      • 17. Sauer, D.U., Wenzl, H.: ‘Comparison of different approaches for lifetime prediction of electrochemical systems-using lead-acid batteries as example’, J. Power Sources, 2008, 176, (2), pp. 534546.
    18. 18)
      • 18. Borhan, H., Rotea, M.A., Viassolo, D.: ‘Optimization-based power management of a wind farm with battery storage’, Wind Energy, 2013, 16, (8), pp. 11971211.
    19. 19)
      • 19. Qiao, W.: ‘Dynamic modeling and control of doubly fed induction generators driven by wind turbines’. Power Systems Conf. and Exposition, Seattle, USA, March 2009, pp. 18.
    20. 20)
      • 20. Pena, R., Clare, J.C., Asher, G.M.: ‘Doubly fed induction generator using back-to-back PWM converters and its application to variable-speed wind-energy generation’, IEE Proc. Electr. Power Appl., 1996, 143, (3), pp. 231241.
    21. 21)
      • 21. Ni, Y., Du, Z., Li, C., et al: ‘Cross-gramian-based dynamic equivalence of wind farms’, IET Gener. Transm. Distrib., 2016, 10, (6), pp. 14221430.
    22. 22)
      • 22. Ceraolo, M.: ‘New dynamical models of lead-acid batteries’, IEEE Trans. Power Syst., 2000, 15, (4), pp. 11841190.
    23. 23)
      • 23. Jackey, R.A.: ‘A simple, effective lead-acid battery modeling process for electrical system component selection’, SAE Technical Paper, 2007.
    24. 24)
      • 24. Barsali, S., Ceraolo, M.: ‘Dynamical models of lead-acid batteries: implementation issues’, IEEE Trans. Energy Convers, 2002, 17, (1), pp. 1623.
    25. 25)
      • 25. Schiffer, J., Sauer, D.U., Bindner, H., et al: ‘Model prediction for ranking lead-acid batteries according to expected lifetime in renewable energy systems and autonomous power-supply systems’, J. Power Sources, 2007, 168, (1), pp. 6678.
    26. 26)
      • 26. Liuping, W.: ‘Model predictive control system design and implementation using MATLAB’ (Springer Science & Business Media, London, UK, 2009, 1st edn.).
    27. 27)
      • 27. Camacho, E.F., Alba, C.B.: ‘Model predictive control’ (Springer Science & Business Media, London, UK, 2013, 2nd edn.).
    28. 28)
      • 28. Haohuai, W., Yong, T., Junxian, H., et al: ‘Composition modeling and equivalence of an integrated power generation system of wind, photovoltaic and energy storage unit’, Proc. CSEE, 2011, 31, (34), pp. 19.
    29. 29)
      • 29. Bossanyi, E.A.: ‘GH bladed theory manual’ (GH & Partners Ltd, Bristol, UK, 11 edn.).
    30. 30)
      • 30. Bossanyi, E.A.: ‘Individual blade pitch control for load reduction’, Wind Energy, 2003, 6, (2), pp. 119128.
    31. 31)
      • 31. ‘NWTC Information Portal (MCrunch)’. Available at https://nwtc.nrel.gov/MCrunch, accessed 14 December 2017.
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