access icon free Control optimisation for pumped storage unit in micro-grid with wind power penetration using improved grey wolf optimiser

As the large-scale wind power penetration and distributed generation become popular in modern power systems, the governing control of pumped storage unit has attracted many attentions in recent years for its flexible power adjustment capability. To provide a research platform for dynamic analysis of the hybrid power system, a micro-grid mainly including a pumped storage unit and a wind power plant is introduced in detail and taken as the system plant. Refined mathematical models of pump turbine, synchronous generator and wind turbine generator have been established considering the complicated non-linear dynamic characteristics of the multi-energy power system. Furthermore, a novel chaotic grey wolf optimiser algorithm is proposed to select the optimal control parameters of the pump turbine governing system for the sake of maintaining frequency stability and enhancing control performances under the complicated operating conditions. Simulation experiments have been conducted in the micro-grid under representative operating conditions to validate the effectiveness of the proposed control method and its preponderance in comparison with traditional ones.

Inspec keywords: distributed power generation; power generation control; power system simulation; synchronous generators; optimisation; wind power plants; pumped-storage power stations; optimal control; hybrid power systems; wind turbines

Other keywords: hybrid power system; pump turbine governing system; control optimisation; pumped storage unit; modern power system; optimal control parameter selection; nonlinear dynamic characteristics; wind power plant; wind turbine generator; improved grey wolf optimiser; large-scale wind power penetration; dynamic analysis; synchronous generator; multienergy power system; distributed generation; governing control; microgrid

Subjects: Control of electric power systems; Wind power plants; Optimisation techniques; Distributed power generation; Optimisation techniques; Optimal control; Synchronous machines; Pumped storage stations and plants

References

    1. 1)
      • 9. Lin, X., Chen, N., Hui, Li.: ‘New method on pump-turbine characteristic description and its engineering applications’, J. Tsinghua Univ., 1999, 39, (4), pp. 7476, [in Chinese].
    2. 2)
      • 28. Yuan, X., Zhang, T., Dai, X., et al: ‘Master–slave model-based parallel chaos optimization algorithm for parameter identification problems’, Nonlinear Dynam., 2016, 83, (3), pp. 17271741.
    3. 3)
      • 4. Vieira, B., Viana, A., Matos, M., et al: ‘A multiple criteria utility-based approach for unit commitment with wind power and pumped storage hydro’, Electr. Power Syst. Res., 2016, 131, pp. 244254.
    4. 4)
      • 26. Kamel, R.M., Nagasaka, K.: ‘Effect of load type on standalone micro grid fault performance’, Appl. Energy, 2015, 160, pp. 532540.
    5. 5)
      • 18. Sarin, S., Hindersah, H., Prihatmanto, A.S.: ‘Fuzzy PID controllers using 8-Bit microcontroller for U-Board speed control’. System Engineering and Technology (ICSET), 2012 Int. Conf. on. IEEE, 2012, pp. 16.
    6. 6)
      • 13. Mirjalili, S., Mirjalili, S.M., Lewis, A.: ‘Grey wolf optimizer’, Adv. Eng. Softw., 2014, 69, pp. 4661.
    7. 7)
      • 8. Xu, Y., Zhou, J., Xue, X., et al: ‘An adaptively fast fuzzy fractional order PID control for pumped storage hydro unit using improved gravitational search algorithm’, Energy Convers. Manage., 2016, 111, pp. 6778.
    8. 8)
      • 16. Emary, E., Zawbaa, H.M., Hassanien, A.E.: ‘Binary grey wolf optimization approaches for feature selection’, Neurocomputing, 2016, 172, pp. 371381.
    9. 9)
      • 27. He, Y., Zhou, J., Xiang, X., et al: ‘Comparison of different chaotic maps in particle swarm optimization algorithm for long-term cascaded hydroelectric system scheduling’, Chaos Solitons Fractals, 2009, 42, (5), pp. 31693176.
    10. 10)
      • 11. Raju, S.K., Pillai, G.N.: ‘Design and real time implementation of type-2 fuzzy vector control for DFIG based wind generators’, Renew Energy, 2016, 88, pp. 4050.
    11. 11)
      • 2. Ma, T., Yang, H., Lu, L.: ‘Feasibility study and economic analysis of pumped hydro storage and battery storage for a renewable energy powered island’, Energy Convers. Manage., 2014, 79, pp. 387397.
    12. 12)
      • 22. Wamkeue, R., Jolette, C., Mabwe, A.B.M., et al: ‘Cross-identification of synchronous generator parameters from RTDR test time-domain analytical responses’, IEEE Trans. Energy Convers., 2011, 26, (3), pp. 776786.
    13. 13)
      • 25. Hansen, A.D., Sørensen, P., Iov, F., et al: ‘Overall control strategy of variable speed doubly-fed induction generator wind turbine’. Proc. of Wind Power Nordic Conf., Chalmers tekniska högskola, 2004.
    14. 14)
      • 15. Song, X., Tang, L., Zhao, S., et al: ‘Grey Wolf optimizer for parameter estimation in surface waves’, Soil Dyn. Earthq. Eng., 2015, 75, pp. 147157.
    15. 15)
      • 30. Scherer, L.G., de Camargo, R.F.: ‘Control of micro hydro power stations using nonlinear model of hydraulic turbine applied on microgrid systems’. XI Brazilian Power Electronics Conf. IEEE, 2011, pp. 812818.
    16. 16)
      • 14. Jayakumar, N., Subramanian, S., Ganesan, S., et al: ‘Grey wolf optimization for combined heat and power dispatch with cogeneration systems’, Int. J. Electr. Power Energy Syst., 2016, 74, pp. 252264.
    17. 17)
      • 6. Kusakana, K.: ‘Optimal scheduling for distributed hybrid system with pumped hydro storage’, Energy Convers. Manage., 2016, 111, pp. 253260.
    18. 18)
      • 10. Rakhtala, S.M., Shafiee Roudbari, E.: ‘Fuzzy PID control of a stand-alone system based on PEM fuel cell’, Int. J. Electr. Power Energy Syst., 2016, 78, pp. 576590.
    19. 19)
      • 29. Liu, B., Wang, L., Jin, Y., et al: ‘Improved particle swarm optimization combined with chaos’, Chaos Solitons Fractals, 2005, 25, (5), pp. 12611271.
    20. 20)
      • 7. Martínez-Lucas, G., Sarasúa, J.I., Sánchez-Fernández, J.Á., et al: ‘Frequency control support of a wind-solar isolated system by a hydropower plant with long tail-race tunnel’, Renew. Energy, 2016, 90, pp. 362376.
    21. 21)
      • 20. Fan, R., Zhao, J., Pan, B., et al: ‘Automatic generation control of three-area small hydro system based on fuzzy PID control’. 2014 Int. Conf. on. IEEE, Power System Technology (POWERCON), 2014, pp. 25222528.
    22. 22)
      • 24. Li, S., Haskew, T.A.: ‘Characteristic study of vector-controlled doubly-fed induction generator in stator-flux-oriented frame’, Electr. Power Compon. Syst., 2008, 36, (9), pp. 9901015.
    23. 23)
      • 23. Ma, F., Zhang, T., Niu, W.: ‘Nonlinear inverse modeling of synchronous generator based on improved resource allocating networks’. Intelligent Computation Technology and Automation, 2009. ICICTA'09. Second Int. Conf. on. IEEE, 2009, vol. 2, pp. 124127.
    24. 24)
      • 17. Xiang, T., Liao, X., Wong, K.: ‘An improved particle swarm optimization algorithm combined with piecewise linear chaotic map’, Appl. Math. Comput., 2007, 190, (2), pp. 16371645.
    25. 25)
      • 19. Sahu, B.K., Pati, T.K., Nayak, J.R., et al: ‘A novel hybrid LUS–TLBO optimized fuzzy-PID controller for load frequency control of multi-source power system’, Int. J. Electr. Power Energy Syst., 2016, 74, pp. 5869.
    26. 26)
      • 1. Shivarama Krishna K. Sathish Kumar, K.: ‘A review on hybrid renewable energy systems’, Renew. Sustain. Energy Rev., 2015, 52, pp. 907916.
    27. 27)
      • 5. Chen, C., Chen, H., Lee, J.: ‘Application of a generic superstructure-based formulation to the design of wind-pumped-storage hybrid systems on remote islands’, Energy Convers. Manage., 2016, 111, pp. 339351.
    28. 28)
      • 21. De Jaeger, E., Janssens, N., Malfliet, B., et al: ‘Hydro turbine model for system dynamic studies’, IEEE Trans. Power Syst., 1994, 9, (4), pp. 17091715.
    29. 29)
      • 12. Hansen, A.D., Michalke, G.: ‘Fault ride-through capability of DFIG wind turbines’, Renew. Energy, 2007, 32, (9), pp. 15941610.
    30. 30)
      • 3. Liu, Z., Chen, C., Yuan, J.: ‘Hybrid energy scheduling in a renewable micro grid’, Appl. Sci., 2015, 5, (3), pp. 516531.
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