Explicit degradation modelling in optimal lead–acid battery use for photovoltaic systems

Explicit degradation modelling in optimal lead–acid battery use for photovoltaic systems

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Generation, Transmission & Distribution — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Lead–acid battery is a storage technology that is widely used in photovoltaic (PV) systems. Battery charging and discharging profiles have a direct impact on the battery degradation and battery loss of life. This study presents a new 2-model iterative approach for explicit modelling of battery degradation in the optimal operation of PV systems. The proposed approach consists of two models: namely, economic model and degradation model which are solved iteratively to reach the optimal solution. The economic model is a linear programming optimisation problem that calculates the optimal hourly battery use profile based on an assumed value of the battery degradation cost. The degradation model, in turn, gives the battery degradation cost based on the battery use profile, temperature and battery characteristics. The models are solved iteratively to finally reach to the optimal battery use considering battery degradation. The proposed approach has been applied to a 4 kWp PV system and the performance of the proposed approach were evaluated. Applicability of the proposed approach in determining the optimal storage size and the economic battery life were also shown. Advantages and the capability of the proposed approach in considering PV generation and irradiation variations were also evaluated through seasonality analysis.


    1. 1)
    2. 2)
    3. 3)
    4. 4)
    5. 5)
    6. 6)
      • 6. Glavin, M., Hurley, W.G.: ‘Battery management system for solar energy applications’. 41st Int. Universities Power Engineering Conf. UPEC 06, 2006, vol. 1, pp. 7983.
    7. 7)
    8. 8)
      • 8. Available at, accessed November 2013.
    9. 9)
    10. 10)
      • 10. Geth, F., Tant, J., Six, D., et al: ‘Techno-economical and life expectancy modeling of battery energy storage systems’. Proc. 21st Int. Conf. on Electricity Distribution (CIRED), Frankfurt, 2011.
    11. 11)
    12. 12)
    13. 13)
    14. 14)
    15. 15)
      • 15. Bindner, H., Cronin, T., Lundsager, P., et al: ‘Lifetime modelling of lead–acid batteries’ (Risø National Laboratory, R-1515, Denmark, April 2005).
    16. 16)
      • 16. CRES: ‘Results and analysis of simulated cycling tests on batteries’. Center for Renewable Energy Systems Report for a Benchmarking Project, February2004.
    17. 17)
    18. 18)
      • 18. Borenstein, S.: ‘Time-varying retail electricity prices: theory and practice’, Electr. Deregulation: Choices Chall., 2005, pp. 111130.
    19. 19)
    20. 20)
      • 20. Ekren, O., Ekren, B.Y.: ‘Size optimization of a solar-wind hybrid energy system using two simulation based optimization techniques’, in Carriveau, R. (ed.): ‘Fundamental and advanced topics in wind power’ (INTECH Open Access Publisher, 2011).

Related content

This is a required field
Please enter a valid email address