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Revenue maximisation and storage utilisation for the Ocean Grazer wave energy converter: a sensitivity analysis

Revenue maximisation and storage utilisation for the Ocean Grazer wave energy converter: a sensitivity analysis

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This study presents a revenue maximisation strategy for market integration of a novel wave energy converter (WEC), part of the Ocean Grazer platform. In particular, the authors evaluate and validate the aforementioned revenue maximisation model predictive control (MPC) strategy through extensive simulations and by checking the underlying assumptions of the strategy implementation. Accordingly, an annual simulation of the MPC strategy is shown, which illustrates seasonality effects; furthermore, a benchmark against a heuristic strategy is presented, followed by analyses of the parameter sensitivity and the assumptions on the control loop information that the MPC receives. These efforts shed some light on the impact of variations of the considered parameters and variables on the total revenue and provide insights to optimally scale the WEC. Lastly, the challenges associated with the deployment of such a strategy are addressed, followed by concluding remarks.

References

    1. 1)
      • 1. Chen, C., Duan, S., Cai, T., et al: ‘Smart energy management system for optimal microgrid economic operation’, IET Renew. Power Gener., 2011, 5, (3), pp. 258267.
    2. 2)
      • 2. Roscoe, A. J., Ault, G.: ‘Supporting high penetrations of renewable generation via implementation of real-time electricity pricing and demand response’, IET Renew. Power Gener., 2010, 4, (4), pp. 369382.
    3. 3)
      • 3. Drew, B., Plummer, A., Sahinkaya, M. N.: ‘A review of wave energy converter technology’, Proc. Inst. Mech. Eng. A, J. Power Energy, 2009, 223, (8), pp. 887902.
    4. 4)
      • 4. Ringwood, J. V., Bacelli, G., Fusco, F.: ‘Energy-maximizing control of waveenergy converters: The development of control system technology to optimize their operation’, IEEE Control Syst., 2014, 34, (5), pp. 3055.
    5. 5)
      • 5. Vakis, A. I., Anagnostopoulos, J. S.: ‘Mechanical design and modeling of a single-piston pump for the novel power take-off system of a wave energy converter’, Renew. Energy, 2016, 96, pp. 531547.
    6. 6)
      • 6. Wei, Y., Barradas-Berglind, J., van Rooij, M., et al: ‘Investigating the adaptability of the multi-pump multi-piston power take-off system for a novel wave energy converter’, Renew. Energy, 2017, 111, pp. 598610. URL http://www.sciencedirect.com/science/article/pii/S096014811730349X.
    7. 7)
      • 7. Dijkstra, H., Barradas-Berglind, J., Meijer, H., et al: ‘Revenue optimization for the ocean grazer wave energy converter through storage utilization’. Proc. of the 2nd Int. Conf. on Renewable Energies Offshore - RENEW 2016, Lisbon, 2016, pp. 207213.
    8. 8)
      • 8. Camacho, E. F., Bordons, C.: ‘Model predictive control’ (Springer Science & Business Media, Boca Raton, 2013).
    9. 9)
      • 9. Rossiter, J. A.: ‘Model-based predictive control: a practical approach’ (CRC Press, 2003).
    10. 10)
      • 10. Faedo, N., Olaya, S., Ringwood, J. V.: ‘Optimal control, MPC and MPC-like algorithms for wave energy systems: An overview’, IFAC J. Syst. Control, 2017, 1, pp. 3756.
    11. 11)
      • 11. Edlund, K., Bendtsen, J. D., Jørgensen, J. B.: ‘Hierarchical model-based predictive control of a power plant portfolio’, Control Eng. Pract., 2011, 19, (10), pp. 11261136.
    12. 12)
      • 12. Ross, D. W., Kim, S.: ‘Dynamic economic dispatch of generation’, IEEE Trans. Power Appar. Syst., 1980, 99, (6), pp. 20602068.
    13. 13)
      • 13. Xia, X., Elaiw, A.: ‘Optimal dynamic economic dispatch of generation: A review’, Electr. Power Syst. Res., 2010, 80, (8), pp. 975986.
    14. 14)
      • 14. Pritchard, G., Philpott, A. B., Neame, P. J.: ‘Hydroelectric reservoir optimization in a pool market’, Math. Program., 2005, 103, (3), pp. 445461.
    15. 15)
      • 15. Steeger, G., Barroso, L. A., Rebennack, S.: ‘Optimal bidding strategies for hydro-electric producers: A literature survey’, IEEE Trans. Power Syst., 2014, 29, (4), pp. 17581766.
    16. 16)
      • 16. Zhao, G., Davison, M.: ‘Optimal control of hydroelectric facility incorporating pump storage’, Renew. Energy, 2009, 34, (4), pp. 10641077.
    17. 17)
      • 17. Falnes, J.: ‘Ocean waves and oscillating systems: linear interactions including wave-energy extraction’ (Cambridge university press, Cambridge, 2002).
    18. 18)
      • 18. Barradas-Berglind, J., Wisniewski, R.: ‘Representation of fatigue for wind turbine control’, Wind Energy, 2016, 19, (12), pp. 21892203.
    19. 19)
      • 19. Faber, T. E.: ‘Fluid dynamics for physicists’ (Cambridge University Press, Cambridge, 1995).
    20. 20)
      • 20. Data.marine.ie: ‘Wave buoy network real time data’, http://data.marine.ie/Dataset/Details/20973, 2016, accessed: 2016–05-12.
    21. 21)
      • 21. Single Electricity Market Operator (SEMO): ‘Market data’, http://www.sem-o.com/marketdata/Pages/default.aspx, 2014, accessed: 2016-05-12.
    22. 22)
      • 22. Brodtkorb, P., Johannesson, P., Lindgren, G., et al: ‘WAFO - a Matlab toolbox for the analysis of random waves and loads’. Proc. 10'th Int. Offshore and Polar Eng. Conf., ISOPE, Seattle, USA, 2000, vol. 3, pp. 343350.
    23. 23)
      • 23. Knudsen, T., Bak, T., Svenstrup, M.: ‘Survey of wind farm controlâĂŤpower and fatigue optimization’, Wind Energy, 2015, 18, (8), pp. 13331351.
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