Probabilistic production simulation of a wind/photovoltaic/energy storage hybrid power system based on sequence operation theory

Probabilistic production simulation of a wind/photovoltaic/energy storage hybrid power system based on sequence operation theory

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Probabilistic production simulation is an important tool used in conventional power systems to calculate generated energy and evaluate reliability. With the continuous expansion of the renewable energy plants, new characteristics such as intermittency and volatility of power are widely discussed, and it is necessary to study the application of probabilistic production simulation for renewable energy power generation. This study proposes a probabilistic production simulation method based on sequence operation theory (SOT) to simulate the operation of a wind/photovoltaic/energy storage power system. Both the uncertainty of renewable resources and the outage of wind turbines are considered in this study. Considering the complementary property of the renewable energy, pattern clustering is used to analyse the meteorological conditions and to assist in the probabilistic production simulation. Moreover, the output model of the energy storage device is developed using the Monte Carlo method and controlled using a smoothing strategy of the energy storage device. Ultimately, the simulation example shows the feasibility and the higher efficiency of the algorithm compared with Monte Carlo method and a production simulation method based on equivalent energy function.


    1. 1)
      • 1. Rau, N.S., Toy, P., Schenk, K.F.: ‘Expected energy production costs by the method of moments’, IEEE Trans. Power Appar. Syst., 1980, 99, (5), pp. 19081917.
    2. 2)
      • 2. Wang, X.F.: ‘Equivalent energy function approach to power system probabilistic modeling’, IEEE Trans. Power Syst., 1988, 3, (3), pp. 823829.
    3. 3)
      • 3. Wang, X.F., McDonald, J.R.: ‘Modern power system planning’ (McGraw Hill, New York, NY, USA, 1994).
    4. 4)
      • 4. Booth, R.R.: ‘Power system simulation model based on probability analysis’, IEEE Trans. Power Appar. Syst., 1972, 91, (1), pp. 6269.
    5. 5)
      • 5. Söder, L., Bubenko, J.: ‘Capacity credit and energy value of wind power in hydro-thermal power system’. Proc. Int. Conf. Power Systems Computation Conf., Lisbon, Portugal, September 1987, pp. 222225.
    6. 6)
      • 6. Marwali, M.K.C., Shahidehpour, S.M., Daneshdoost, M.: ‘Probabilistic production costing for photovoltaics-utility systems with battery storage’, IEEE Trans. Energy Convers., 1997, 12, (2), pp. 175180.
    7. 7)
      • 7. Lei, J., Wan, C., Chen, H., et al: ‘Studies on algorithms of power system probabilistic production simulation considering wind farms’. Proc. Int. Conf. IEEE Pes Asia-Pacific Power and Energy Engineering Conf., Hong Kong, 2015, pp. 16.
    8. 8)
      • 8. Malik, A.S.: ‘Simulating limited energy units within the framework of ELDC and FD methods’, Int. J. Electr. Power, 2004, 26, (8), pp. 645653.
    9. 9)
      • 9. Bie, Z., Zou, X., Wang, Z., et al: ‘Studies on models and algorithms of the power system probabilistic production simulation integrated with wind farm’. Proc. Int. Conf. Power & Energy Society General Meeting, Calgary, AB, Canada, July 2009, pp. 17.
    10. 10)
      • 10. Xu, T., Gross, G.: ‘A production simulation tool for systems with an integrated concentrated solar plant with thermal energy storage’, 2013 IREP Symposium on Bulk Power System Dynamics and Control - IX Optimization, Security and Control of the Emerging Power Grid (IREP), Rethymno, Greece, August 2013.
    11. 11)
      • 11. Degeilh, Y., Gross, G.: ‘Stochastic simulation of power systems with integrated intermittent renewable resources’, Int. J. Electr. Power, 2015, 64, pp. 542550.
    12. 12)
      • 12. Jin, T., Zhou, M., Gengyin, L.I.: ‘Universal generating function based probabilistic production simulation for wind power integrated power systems’, J. Mod. Power Syst. Clean, 2017, 5, (1), pp. 18.
    13. 13)
      • 13. Aien, M., Biglari, A., Rashidinejad, M.: ‘Probabilistic reliability evaluation of hybrid wind-photovoltaic power systems’. Proc. Int. Conf. Electrical Engineering, Mashhad, Iran, May 2013, pp. 16.
    14. 14)
      • 14. Zhu, Y., Jiang, P., Yang, S.: ‘An optimal capacity allocation scheme for the wind-PV hybrid power system based on probabilistic production simulation’. Proc. Int. Conf. Energy Conversion, Johor Bahru, Malaysia, October 2014.
    15. 15)
      • 15. Abbassi, R., Chebbi, S.: ‘Energy management strategy for a grid-connected wind-solar hybrid system with battery storage: policy for optimizing conventional energy generation’, Int. Rev. Electr. Eng.-I., 2012, 7, (2), pp. 39793990.
    16. 16)
      • 16. Lu, M.S., Chang, C.L., Lee, W.J., et al: ‘Combining the wind power generation system with energy storage equipments’. Proc. Int. Conf. Industry Applications Society Meeting, Edmonton, AB, Canada, October 2008, pp. 16.
    17. 17)
      • 17. Hartmann, B., Dan, A.: ‘Cooperation of a grid-connected wind farm and an energy storage unit—demonstration of a simulation tool’, IEEE Trans. Sustain. Energy, 2011, 3, (1), pp. 4956.
    18. 18)
      • 18. Delavaripour, H., Dehkordi, B.M.: ‘Reliability assessment of a standalone wind-conventional/energy storage system using probabilistic production simulation method’, Turk. J. Electr. Eng. Co., 2015, 23, (6), pp. 19962016.
    19. 19)
      • 19. Kang, C., Xia, Q., Xiang, N.: ‘Sequence operation theory and its application in power system reliability evaluation’, Reliab. Eng. Syst. Saf., 2002, 78, (2), pp. 101109.
    20. 20)
      • 20. Karaki, S.H., Chedid, R.B., Ramadan, R.: ‘Probabilistic performance assessment of autonomous solar-wind\nenergy conversion systems’, IEEE Trans. Energy Convers., 1999, 14, (3), pp. 766772.
    21. 21)
      • 21. Alexiadis, M.C., Dokopoulos, P.S., Sahsamanoglou, H.S.: ‘Wind speed and power forecasting based on spatial correlation models’, IEEE Trans. Energy Convers., 2002, 14, (3), pp. 836842.
    22. 22)
      • 22. Sulaeman, S., Benidris, M., Mitra, J., et al: ‘A wind farm reliability model considering both wind variability and turbine forced outages’, IEEE Trans. Sustain. Energy, 2017, 8, (2), pp. 629637.
    23. 23)
      • 23. Moharil, R.M., Kulkarni, P.S.: ‘Reliability analysis of solar photovoltaic system using hourly mean solar radiation data’, Sol. Energy, 2010, 84, (4), pp. 691702.
    24. 24)
      • 24. Billinton, R., Huang, D.: ‘Aleatory and epistemic uncertainty considerations in power system reliability evaluation’. Proc. Int. Conf. Int. Conf. on Probabilistic Methods Applied To Power Systems, Pueto Rico, 25–29 May 2008, pp. 18.
    25. 25)
      • 25. Yoshimoto, K., Nanahara, T., Koshimizu, G.: ‘New control method for regulating state-of-charge of a battery in hybrid wind power/battery energy storage system’. Proc. Int. Conf. Power Systems Conf. and Exposition, Atlanta, GA, USA, 29 October - 1 November 2006, pp. 12441251.

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