Statistical analysis and dimensioning of a wind farm energy storage system

Open access


The growth in renewable power generation and more strict local regulations regarding power quality indices will make it necessary to use energy storage systems with renewable power plants in the near future. The capacity of storage systems can be determined using different methods most of which can be divided into either deterministic or stochastic. Deterministic methods are often complicated with numerous parameters and complex models for long term prediction often incorporating meteorological data. Stochastic methods use statistics for ESS (Energy Storage System) sizing, which is somewhat intuitive for dealing with the random element of wind speed variation. The proposed method in this paper performs stabilization of output power at one minute intervals to reduce the negative influence of the wind farm on the power grid in order to meet local regulations. This paper shows the process of sizing the ESS for two selected wind farms, based on their levels of variation in generated power and also, for each, how the negative influences on the power grid in the form of voltage variation and a shortterm flicker factor are decreased.

[1] Shokrzadeh S., Jozani M. J., Molinski T., A statistical algorithm for predicting the energy storage capacity for baseload wind power generation in the future electric grids, Energy (2015).

[2] Power grid codes of transmission network. Terms of use, operation and planning of network development. Version 2.1 (in Polish), The consolidated text of the Charter of renovation CK / 1/2012 approved by the President of URE No. DPK-4320-2 (16) / 2010 - 2013 / LK (2013).

[3] PN-EN 50160: 2002 Voltage characteristics of electricity supplied by public distribution networks

[4] Luo Y., Shi L., Tu G., Optimal sizing and control strategy of isolated grid with wind power and energy storage system, Energy Conversion and Management, vol. 80, pp. 407-415 (2014).

[5] Zhang Y., Tang X., Qi Z., Liu Z., The Ragone plots guided sizing of hybrid storage system for taming the wind power, Electrical Power and Energy Systems, vol. 65, pp. 246-253 (2015).

[6] Paatero J.V., Lund P.D., Effect of Energy Storage on Variations in Wind Power, Wind Energy, vol. 8, no. 4, pp. 421-441 (2005).

[7] Schneidera M., Bielb K., Pfallera S., Schaedea H., Glockb C.H., Optimal sizing of electrical energy storage systems using inventory models, Energy Procedia, vol. 73, pp. 48-58 (2015).

[8] Jaworsky C., Turitsyn K., Backhaus S., The Effect of Forecasting Accuracy on the Sizing of Energy Storage, ASME 2014 Dynamic Systems and Control Conference (2014).

[9] Haessig P., Multon B., Ahmed H.B., Lascaud S., Bondon P., Energy storage sizing for wind power: impact of the autocorrelation of day-ahead forecast errors, HAL (2013).

[10] Korpaas M., Holen A.T., Hildrum R., Operation and sizing of energy storage for wind power plants in a market system, Electrical Power & Energy Systems, vol. 25, issue 8, pp. 599-606 (2003).

[11] Lubośny Z., Wind farms in the power system, monograph, WNT Publishing House, Warsaw (2013).

[12] William E.J., Gupta V., Huff M., Linda O., Govar J., A Comparative Study of Lithium Poly-Carbon Monoflouride (Li/CFx) and Lithium Iron Phosphate (LiFePO4) Battery Chemistries for State of Charge Indicator Design, Contract M67854-08-C 6530 (2009).

[13] Gualous H., Alcicek G., Diab Y., Hammar A., Venet P., Adams K., Marumo C., Lithium Ion capacitor characterization and modelling, ESSCAP’ (2008).

[14] Abbey C., Joos G., Supercapacitor energy storage for wind energy applications, IEEE Transactions on Industry Applications, vol. 43, no. 3, pp. 769-776 (2007).

[15] Jayalakshmi M., Balasubramanian K., Simple capacitors to supercapacitors-an overview, Int. J. Electrochem. Sci., col. 3, no. 11, pp. 1196-1217 (2008).

[16] Díaz-González F., Sumper A., Gomis-Bellmunt O., Villafáfila-Robles R., A review of energy storage technologies for wind power applications, Renewable and Sustainable Energy Reviews, vol. 16, no. 4, pp. 2154-2171 (2012).

Archives of Electrical Engineering

The Journal of Polish Academy of Sciences

Journal Information

CiteScore 2016: 0.71

SCImago Journal Rank (SJR) 2016: 0.238
Source Normalized Impact per Paper (SNIP) 2016: 0.535


All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 192 190 15
PDF Downloads 103 100 8