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access icon free Optimisation of grid connected hybrid photovoltaic–wind–battery system using model predictive control design

© The Institution of Engineering and Technology
Received 06/06/2017, Accepted 25/08/2017, Revised 16/08/2017, Published 29/08/2017

This study explores optimisation of the hybrid power system in the smart grid framework, in conjunction with the model predictive control (MPC) design. This study also creates a strategy that can maximise the use of renewable energy, e.g. photovoltaic, the wind turbine with battery storage and minimise the utilisation of the utility grid for electricity usage in the industry. This is devised by modelling a discrete state-space model of the hybrid power system for a given industry application. The system design is implemented within a real-time electricity pricing environment that is integrated with renewable energy to optimally meet the demand according to a specific performance of the consumer. The emphasis of this approach is on its capacity to supply optimal power to the demand side by selecting the appropriate source; and its robustness against uncertainties. The results show that MPC design for hybrid power system not only optimises the energy flow but also improves the overall process of energy management. It was also observed that the optimal solution minimises the delay cost of energy demand from the utility grid according to a given reference from the consumer for the specified tuning parameter values of the performance index.

Inspec keywords: power generation control; hybrid power systems; power generation economics; wind turbines; state-space methods; discrete systems; control system synthesis; pricing; photovoltaic power systems; energy management systems; battery storage plants; smart power grids; minimisation; predictive control; wind power plants; demand side management; power markets

Other keywords: real-time electricity pricing environment; MPC design; demand side management; utility grid; discrete state-space model; wind turbine; industry application; model predictive control design; hybrid power system; energy demand delay cost minimization; performance index; energy management process; renewable energy; smart grid framework; battery storage; grid connected hybrid photovoltaic-wind-battery system optimization

Subjects: Discrete control systems; Control of electric power systems; Solar power stations and photovoltaic power systems; Optimisation techniques; Wind power plants; Optimal control; Power system management, operation and economics; Control system analysis and synthesis methods; Optimisation techniques; Other power stations and plants

References

    1. 1)
      • 28. Solar Resource Data of NREL, accessed on http://pvwatts.nrel.gov/pvwatts.php.
    2. 2)
      • 22. Siti, M., Tiako, R., Bansal, R.C.: ‘A model predictive control strategy for grid-connected solar-wind with pumped hydro storage’. 5th IET Int. Conf. Renewable Power Generation (RPG), London, UK, 21–23 September 2016.
    3. 3)
      • 19. Taha, M., Yasser, A.: ‘Robust energy management of a hybrid wind and flywheel energy storage system considering flywheel power losses minimization and grid-code constraints’, IEEE Trans. Ind. Electron., 2016, 63, (7), pp. 4242–4254.
    4. 4)
      • 24. Kou, P., Liang, D., Gao, L.: ‘Distributed EMPC of multiple microgrids for coordinated stochastic energy management’, Appl. Energy, 2017, 185, pp. 939–952.
    5. 5)
      • 4. Askarzadeh, A.: ‘Distribution generation by photovoltaic and diesel generator systems: energy management and size optimization by a new approach for a stand-alone application’, Energy, 2017, 122, pp. 542–551.
    6. 6)
      • 15. Abdeltawab, H., Mohamed, Y.: ‘Market-oriented energy management of a hybrid wind-battery energy storage system via model predictive control with constraint optimizer’, IEEE Trans. Ind. Electron., 2015, 62, (11), pp. 6658–6670.
    7. 7)
      • 13. Roscoe, A., 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. 369–382.
    8. 8)
      • 10. Zhan, Y., Liu, B., Zhang, T., et al: ‘An intelligent control strategy of battery energy storage system for microgrid energy management under forecast uncertainties’, Int. J. Electricotechem. Sci., 2014, 9, pp. 4190–4204.
    9. 9)
      • 16. Trifkovic, M., Sheikhzadeh, M., Nigim, K., et al: ‘Modeling and control of a renewable hybrid energy system with hydrogen storage’, IEEE Trans. Control Syst. Technol., 2014, 22, (1), pp. 169–179.
    10. 10)
      • 17. Garcia-Torres, F.,, Bordons, C.: ‘Optimal economical schedule of hydrogen-based microgrids with hybrid storage using model predictive control’, IEEE Trans. Ind. Electron., 2015, 62, (8), pp. 5195–5207.
    11. 11)
      • 23. Kim, S., Kim, J., Cho, K., et al: ‘Optimal operation control of multiple BESSs for a large-scale customer under time-based pricing’, IEEE Trans. Power Syst., 2017, PP, (99), pp. 1–13.
    12. 12)
      • 27. CSIR: Department of Environmental Affairs, Republic of South Africa, 2014, October. Available at https://redzs.csir.co.za/.
    13. 13)
      • 29. Eskom: ‘Tariff & charges booklet’, Eskom Demand Side Management, Johannesburg, Brochure 2014/2015, accessed on 20 March 2016.
    14. 14)
      • 25. Khakimova, A., Kusatayeva, A., Shamshimova, A., et al: ‘Optimal energy management of a small-size building via hybrid model predictive control’, Energy Build., 2017, 140, pp. 1–8.
    15. 15)
      • 30. Gilbert, M.: ‘Renewable and efficient electric power systems’ (John Wiley & Sons, 2013).
    16. 16)
      • 21. Henerica, T., Bing, Z., Xiaohua, X.: ‘Switched model predictive control for energy dispatching of a photovoltaic-diesel-battery hybrid power system’, IEEE Trans. Control Syst. Technol., 2015, 23, (3), pp. 1229–1236.
    17. 17)
      • 18. Jonglak, P., Issarachai, N.: ‘PHEVs bidirectional charging/discharging and SoC control for microgrid frequency stabilization using multiple MPC’, IEEE Trans. Smart Grid, 2015, 6, (2), pp. 526–533.
    18. 18)
      • 2. Negi, S., Mathew, L.: ‘Hybrid renewable energy system: a review’, Int. J. Electron. Electr. Eng., 2014, 7, (5), pp. 535–542.
    19. 19)
      • 11. Mbungu, N.T., Naidoo, R., Bansal, R.C., et al: ‘Smart SISO-MPC based energy management system for commercial buildings: technology trends’. IEEE Future Technologies Conf. (FTC), San Francisco, USA, 6–7 December 2016, pp. 750–753.
    20. 20)
      • 14. Zhang, Y., Wang, R., Zhang, T., et al: ‘Model predictive control-based operation management for a residential microgrid with considering forecast uncertainties and demand response strategies’, IET. Gener. Transm. Distrib., 2016, 10, (10), pp. 2367–2378.
    21. 21)
      • 3. Askarzadeh, A.: ‘Electrical power generation by an optimised autonomous PV/wind/tidal/battery system’, IET Renew. Power Gener., 2016, 11, (1), pp. 152–164.
    22. 22)
      • 26. Wang, L.: ‘Model predictive control system design and implementation using MATLAB®’ (Springer Science & Business Media, 2009).
    23. 23)
      • 6. Ursun, B., Gokcol, C., Umut, I., et al: ‘Techno-economic evaluation of a hybrid PV-wind power generation system’, Int. J. Green Energy, 2013, 10, (2), pp. 117–136.
    24. 24)
      • 8. Maleki, A., Khajeh, M., Morteza, Ameri M.: ‘Optimal sizing of a grid independent hybrid renewable energy system incorporating resource uncertainty, and load uncertainty’, Int. J. Electr. Power Energy Syst., 2014, 3, (1), pp. 122–125.
    25. 25)
      • 20. Taha, M., Yasser, A.: ‘Robust MPC-based energy management system of a hybrid energy source for remote communities’. IEEE Electrical Power and Energy Conf. (EPEC), 2016, pp. 1–6.
    26. 26)
      • 5. Pachor, A., Suhane, P.: ‘Modeling and simulation of photovoltaic/wind/diesel/battery hybrid power generation system’, Int. J. Electr. Electron. Comput. Eng., 2014, 3, (1), pp. 122–125.
    27. 27)
      • 7. Suchitra, D., Jegatheesan, R., Deepika, T.: ‘Optimal design of hybrid power generation system and its integration in the distribution network’, Int. J. Electr. Power Energy Syst., 2016, 82, pp. 136–149.
    28. 28)
      • 12. Mbungu, N.T., Naidoo, R.M., Bansal, R.C.: ‘Real-time electricity pricing: TOU-MPC based energy management for commercial buildings’. 8th Int. Conf. Applied Energy, Beijing, China, May 2017, vol. 105, pp. 3419–3424.
    29. 29)
      • 9. Abeer, A.M., Shawky, H., Maged, N.F., et al: ‘Modeling and simulation for hybrid of PV-wind system’, Int. J. Eng. Res., 2015, 4, (4), pp. 178–183.
    30. 30)
      • 1. Zobaa, A.F., Bansal, R.C.: ‘Handbook of renewable energy technology’ (World Scientific, Singapore, 2011).
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