Impact of SOC variations on the battery bank sizing of a stand-alone system fed by a passive wind turbine

Abstract

In this paper, the authors compare and analyse two passive wind turbine system models in order to show their equivalence through a storage bank sizing procedures. The main differences between both models reside in the design accuracy and the computational time needed for each model to simulate the wind turbine system behaviour. On the one hand, a first “mixed reduced model” neglects the electrical mode effect and assumes that the DC battery bus voltage is constant (i.e. invariable State Of Charge: SOC). On the other hand, the second “full analytic model” couples SOC fluctuations (i.e. bus voltage variations) in the whole system. When compared to the second model, the “mixed reduced model” allows reducing computational time, which is a major factor in the context of systemic design by optimization. The analysis is performed to put in evidence the correspondence between both sizing approaches with the two corresponding models. The results are finally discussed from the point of view of the compromise design accuracy and computational time reduction. In this paper, the authors compare and analyse two passive wind turbine system models in order to show their equivalence through a storage bank sizing procedures. The main differences between both models reside in the design accuracy and the computational time needed for each model to simulate the wind turbine system behaviour. On the one hand, a first “mixed reduced model” neglects the electrical mode effect and assumes that the DC battery bus voltage is constant (i.e. invariable State Of Charge: SOC). On the other hand, the second “full analytic model” couples SOC fluctuations (i.e. bus voltage variations) in the whole system. When compared to the second model, the “mixed reduced model” allows reducing computational time, which is a major factor in the context of systemic design by optimization. The analysis is performed to put in evidence the correspondence between both sizing approaches with the two corresponding models. The results are finally discussed from the point of view of the compromise design accuracy and computational time reduction.. Do not use abbreviations in the title unless they are unavoidable.

Keywords

• Wind energy systems, energy storage, battery sizing, mixed reduced model, full analytic model, SOC variations.

Kaynakça

1. M. Uzunoglu, OC. Onar, MS. Alam, “Modelling, control and simulation of a PV/FC/UC based hybrid power generation system for stand-alone applications”, Renewable Energy; 34(3):509–20, 2009.
2. J. Lagorse, D. Paire, A. Miraoui, “Sizing optimization of a stand-alone street lighting system powered by a hybrid system using fuel cell, PV and battery”, Renewable Energy;34(3):683–91, 2009.
3. El-T. Shatter, M. Eskander, M. El-Hagry. “Energy flow and management of a hybrid wind/PV/fuel cell generation Management, 47(9–10):1264–80, 2006. Energy Conversion and
4. JA. Baroudi, V. Dinavahi, AM.Knight, “A review of power converter topologies for wind generators”, Renewable Energy;32:2369–85, 2007.
5. B. Sareni, A. Abdelli, X. Roboam, D.H. Tran, “Model simplification and optimization of a passive wind turbine generator”, ISSN: 0960-1481, Elsevier Renewable Energy 3, pp. 2640-2650, 2009.
6. J. Belhadj, X. Roboam, “Investigation of different methods to control a small Variable Speed Wind Turbine with PMSM Drives”, ASME Journal of Energy Resources Technology, September 2007 -- Volume 129, Issue 3, pp. 200-213, 2007.
7. A. Mirecki, X. Roboam, F. Richardeau, “Architecture cost and energy efficiency of small wind turbines: which system tradeoff?”, IEEE Transactions on Industrial Electronics, February;54(1):660–70, 2007.
8. D.H. Tran, B. Sareni, X. Roboam, C. Espanet, “Integrated Optimal Design of a Passive Wind Turbine System: An experimental validation”, IEEE Transactions on Sustainable Energy, Vol. 1, n°1, pp. 48-56, 2010.

9. Y. Fefermann, S. A. Randi, S. Astier, X. Roboam, ‘’Synthesis models of PM Brushless Motors for the design of complex and heterogeneous system’’, EPE’01, Graz, Austria, September 2001.
10. X. Roboam, A. Abdelli, B. Sareni, "Optimization of a passive small wind turbine based on mixed Weibull- turbulence statistics of wind", Electrimacs 2008, Québec, Canada, 2008.
11. X. Roboam, A. Abdelli, B. Sareni, "Optimization of a passive small wind turbine based on mixed Weibull- turbulence statistics of wind", Electrimacs 2008, Québec, Canada, 2008.
12. Y.-K. Chin, J. Soulard “Modelling of iron losses in permanent magnet synchronous motors with field weakening capability for electric vehicles”, International Journal of Automotive Technology, Vol. 4, No. 2, pp. 87- 94 ,2003.
13. E. Hoang, B. Multon, M. Gabsi, “Enhanced accuracy method for magnetic loss measurement in switched reluctance motor”, ICEM’94; 2:437–42, 1994.
14. http://www1.futureelectronics.com/doc/IXYS
15. S. Piller, M. Perrin, A. Jossen, “Methods for state-of- charge determination and their applications”, Journal of Power Sources, Elsevier, pp. 113-120, 2001.
16. http://www.houseofbatteries.com/pdf/NP38-12
17. R. Belfkira, C. Nichita, P. Reghem, G. Barakat, “Modeling and optimal sizing of hybrid energy system”, International Power Electronics and Motion Control Conference (EPE-PEMC), September 1-3, Poznan, Poland, 2008.
18. C.R. Akli, X. Roboam, B. Sareni, A. Jeunesse, “Energy management and sizing of a hybrid locomotive”, 12th International Conference on Power Electronics and Applications (EPE'2007), Aalborg, Denmark, 2007
19. R. Belfkira, G. Barakat, C. Nichita, “Sizing optimization of a stand-alone hybrid power supply unit: wind/PV system with battery storage”, International Review of Electrical Engineering (IREE), Vol. 3, No. 5, October 2008.