DOI: https://doi.org/10.15407/publishing2020.57.035

THE INFLUENCE OF THE WIND SPEED PREDICTION ERROR ON THE SIZE OF THE STORAGE CONTROLLED OPERATION ZONE IN THE SYSTEM WITH THE WIND GENERATOR

K.S. Klen*, M.K. Yaremenko**, V.Ya. Zhuykov***
National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute,
pr. Peremohy, 37, Kyiv, 03056, Ukraine,
e-mail: This e-mail address is being protected from spambots. You need JavaScript enabled to view it
* ORCID ID : http://orcid.org/0000-0002-6674-8332
** ORCID ID : http://orcid.org/0000-0001-8782-1642
*** ORCID ID : http://orcid.org/0000-0002-3338-2426

The article analyzes the influence of wind speed prediction error on the size of the controlled operation zone of the storage. The equation for calculating the power at the output of the wind generator according to the known values of wind speed is given. It is shown that when the wind speed prediction error reaches a value of 20%, the controlled operation zone of the storage disappears. The necessity of comparing prediction methods with different data discreteness to ensure the minimum possible prediction error and determining the influence of data discreteness on the error is substantiated. The equations of the "predictor-corrector" scheme for the Adams, Heming, and Milne methods are given. Newton's second interpolation formula for interpolation/extrapolation is given at the end of the data table. The average relative error of MARE was used to assess the accuracy of the prediction. It is shown that the prediction error is smaller when using data with less discreteness. It is shown that when using the Adams method with a prediction horizon of up to 30 min, within ± 34% of the average energy value, the drive can be controlled or discharged in a controlled manner. References 13, figures 2, tables 3.
Key words: renewable energy sources, wind generator, prediction, martingale theory, Adams method, Newton’s interpolation polynomial.

1. Zhuikov V. Y., Lukianenko L. M., Mykolaiets D. A., Osypenko K. S., Steliuk A. O., Tereshchenko T. O. & Yamnenko Y. S. Improving the efficiency of systems with renewable energy sources: a monograph. Kyiv: Kafedra. 2018. 368 p. (Ukr)
2. Rozvytok vidnovlyuvanyx dzherel energiyi v Ukrayini. (Development of renewable energy sources in Ukraine), 2017. URL: http://energymagazine.com.ua/wpcontent/uploads/2017/03/Rozvitok-VDE-v-Ukrai-ni.pdf. (Accessed: 07.02.2019).
3. Zhuikov V. Y. & Osypenko K. S. Heisenberg uncertainty principle in estimating the level of energy generated by renewable sources. Tekhnichna Elektrodynamika. 2017. No 1. Pp. 10–16. (Ukr) DOI: https://doi.org/10.15407/techned2017.01.010
4. Turing A. On Computable Numbers, with an Application to the Entscheidungsproblem. Proceedings of the London Mathematical Society. London, Mathematical Society, 1937. Vol. 42. Pp. 230–265. DOI: https://doi.org/10.1112/plms/s2-42.1.230
5. Zhuikov, V. Y., & Osypenko, K. S. The influence of the statistical nature of the parameters of the system elements on the charge level of the drive. Tekhnichna Elektrodynamika. 2019. No 1. Pp. 16–20. (Ukr) DOI: https://doi.org/10.15407/techned2019.01.016
6. Krivcov V., Olejnikov A., and A. Y. Inexhaustible energy. Book. 1. Wind power generators. Kharkiv: Natsyonalnyi Aerokosmycheskyi Unyversytet Kharkovskyi Avyatsyonnyi Ynstytut, 2003. 400 p. (Rus)
7. MegaWatt Technology. Wind turbine SV-3.1. (n.d.). Retrieved from https://megawatt-technology.all.biz/vtrogenerator-sv-3-1-g17708723 (Accessed: 07.02.2019).
8. Belhydromet. (n.d.). Retrieved from http://pogoda.by/ (Accessed: 07.02.2019).
9. NOAA National Centers For Environmental Information (n.d.). URL:https://www.ncdc.noaa.gov/crn/sensors.htm?stationId=1801#wind.
10. Kalinin, V., Nabatov, K., Shuvalov, A., & Kobelev, A. On the possibilities of using alternative energy sources. Vestnik TGTU. 2003. V. 9. No 3. Pp. 450–456. (Rus)
11. Bakhvalov N.S., Zhydkov N.P., Kobelkov G.M. Numerical methods. Moscow: Binom. 2004. 634 p. (Rus)
12. Süli E. & Mayers D. F. Introduction to numerical methods. Cambridge University Press; 1st Edition, 2003. DOI: https://doi.org/10.1017/CBO9780511801181
13. Ghofrani M., Alolayan M. Time Series and Renewable Energy Forecasting. 2018. DOI: https://doi.org/10.5772/intechopen.70845

Received 28.02.2020  

PDF