Multi-Stage Statistical Approach to Wind Power Forecast Errors Evaluation: A Southern Sulawesi Case Study

Dhany Harmeidy Barus, Rinaldy Dalimi

Abstract


Wind Power Plant (WPP) is part of renewable energy sources, with rapid expansion worldwide. It has the advantages of clean and green energy, but its uncertainty leads to an additional grid integration cost. The uncertainty of wind power output is much dependent on the accuracy of the wind power forecast (WPF) result. Since there is no perfect wind power forecast, understanding the current system's forecast accuracy characteristics is essential in expecting typical errors faced in the future. This paper proposed a new algorithm of the statistical approach method to evaluate characteristics of wind power forecast errors (WPFE) from an observed power system with high-penetration WPP. This method combined the approach of scatter diagram, statistical distribution, standard error performance, and score weighting in a multi-stage algorithm. It consists of serial and parallel processes to check the consistency of the results. In this study, a comprehensive analysis was made of various scenarios based on location and timescale. This proposed algorithm has been successfully tested on statistical data of Sidrap WPP and Jeneponto WPP in the Southern Sulawesi power system. The result showed that the scenario with the aggregation of both WPPs in hour-ahead timescale has the most accurate and consistent performance among all scenarios. It demonstrated specific characteristics of WPFE in the observed power system that can be used as an essential starting point in conducting future wind integration expansion studies.

Keywords


Statistical approach; multi-stage; wind power forecast errors.

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References


Y. Hao and C. Tian, “A novel two-stage forecasting model based on error factor and ensemble method for multi-step wind power forecasting,†Applied Energy, vol. 238, pp. 368–383, Mar. 2019.

Y. Wang, Q. Hu, D. Srinivasan, and Z. Wang, "Wind power curve modeling and wind power forecasting with inconsistent data," IEEE Transactions on Sustainable Energy, vol. 10, no. 1, pp. 16-25, Jan. 2019.

Y. Kim and J. Hur, “An ensemble forecasting model of wind power outputs based on improved statistical approaches,†Energies, vol. 13, pp. 1071-1086, Mar. 2020.

J. Yan, H. Zhang, Y. Liu, S. Han, L. Li, and Z. Lu, “Forecasting the high penetration of wind power on multiple scales using multi-to-multi mapping,†IEEE Transactions on Power Systems, vol. 33, pp. 3276-3284, May 2018.

M. B. Ozkan and P. Karagoz, “Data mining-based upscaling approach for regional wind power forecasting: regional statistical hybrid wind power forecast technique (regional SHWIP),†IEEE Access, vol. 7, pp. 171790-171800, 2019.

V. Hoolohan, A. S. Tomlin, and T. Cockerill, “Improved near surface wind speed predictions using Gaussian process regression combined with numerical weather predictions and observed meteorological data,†Renewable Energy, vol. 126, pp. 1043–1054, Oct. 2018.

L. Zhang, Y. Dong and J. Wang, “Wind speed forecasting using a two-stage forecasting system with an error correcting and nonlinear ensemble strategy,†IEEE Access, vol. 7, pp. 176000-176023, 2019.

L. Han, M. Li, X. Wang, and Y. Cheng, “Real-time wind power forecast error estimation based on eigenvalue extraction by dictionary learning,†Chinese Journal of Electronics, vol. 28, pp. 349-356, Mar. 2019.

T. Ji, D. Hong, J. Zheng, Q. Wu and X. Yang, “Wind power forecast with error feedback and its economic benefit in power system dispatch,†IET Generation, Transmission & Distribution, vol. 12, pp. 5730-5738, Nov. 2018.

L. Han, C. E. Romero, X. Wang, and L. Shi, "Economic dispatch considering the wind power forecast error," IET Generation, Transmission & Distribution, vol. 12, no. 12, pp. 2861-2870, Jul. 2018.

L. Han, R. Zhang, X. Wang, and Y. Dong, “Multi-time scale rolling economic dispatch for wind/storage power system based on forecast error feature extraction,†Energies, vol. 11, pp. 2124-2140, Aug. 2018.

X. Yu, X. Dong, S. Pang, L. Zhou, and H. Zang, “Energy storage sizing optimization and sensitivity analysis based on wind power forecast error compensation,†Energies, vol. 12, pp. 4755-4771, Des. 2019.

A. Banik, C. Behera, T. V. Sarathkumar and A. K. Goswami, "Uncertain wind power forecasting using LSTM-based prediction interval," IET Renewable Power Generation, vol. 14, no. 14, pp. 2657-2667, Oct. 2020.

C. Ning and F. You, “Data-Driven Adaptive Robust Unit Commitment Under Wind Power Uncertainty: A Bayesian Nonparametric Approach,†IEEE Transactions on Power Systems, vol. 34, no. 3, pp. 2409–2418, May 2019.

W. Xie, P. Zhang, R. Chen, and Z. Zhou, “A Nonparametric Bayesian Framework for Short-Term Wind Power Probabilistic Forecast,â€, vol. 34, no. 1, pp. 371–379, Jan. 2019.

E. Oh and H. Wang, "Reinforcement-learning-based energy storage system operation strategies to manage wind power forecast uncertainty," IEEE Access, vol. 8, pp. 20965-20976, 2020.

J. Y. Shin, C. Jeong, and J. H. Heo, “A novel statistical method to temporally downscale wind speed Weibull distribution using scaling property,†Energies, vol. 11, pp. 633-647, 2018.

M. B. H. Kumar, S. Balasubramaniyan, P. Sanjeevikumar, and J. B. H. Nielsen, “Wind energy potential assessment by Weibull parameter estimation using multiverse optimization method: A case study of Tirumala region in India,†Energies, vol. 12, pp. 2158-2172, 2019.

C. Wang et al., "Optimal sizing of energy storage considering the spatial-temporal correlation of wind power forecast errors," IET Renewable Power Generation, vol. 13, no. 4, pp. 530-538, Mar. 2019.

F. Ge, Y. Ju, Z. Qi, and Y. Lin, "Parameter estimation of a Gaussian mixture model for wind power forecast error by Riemann L-BFGS optimization," IEEE Access, vol. 6, pp. 38892-38899, 2018.

J. J. Miettinen and H. Holttinen, “Characteristics of day-ahead wind power forecast errors in Nordic countries and beneï¬ts of Aggregation,†Wind Energy, vol. 20, pp. 959-972, 2017.

J. Miettinen and H. Holtinnen, “Impacts of wind power forecast errors on the real-time balancing need: a Nordic case study,†IET Renewable Power Generation, vol. 13, pp. 227-233, 2019.

L. M Putranto, Sarjiya, and A. N. Widiastuti, “Optimizing the preventive and corrective control scheme in integrated variable renewable energy generation,†International Journal on Advanced Science, Engineering and Information Technology, vol. 8, no. 5, pp. 2031-2038, 2018.

A. A. Kadhem, N. I. A. Wahab, I. Aris, J. Jasni, A. N. Abdalla and Y. Matsukawa, “Reliability assessment of generating systems with wind power penetration via BPSO,†International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 4, 2017.

M. A. M. Idris, M. R. Hao, and M. A. Ahmad, “A data driven approach to wind plant control using moth-flame optimization (MFO) algorithm,†International Journal on Advanced Science, Engineering and Information Technology, vol. 9, no.1, pp. 18-23, 2019.

E. Spiliotis, F. Petropoulos, K. Nikolopoulos, “The impact of imperfect weather forecasts on wind power forecasting performance: evidence from two wind farms in Greece,†Energies, vol. 13, pp. 1880-1895, 2020.

S. Sun, J. Fu, and A. Li, “A compound wind power forecasting strategy based on clustering, two-stage decomposition, parameter optimization, and optimal combination of multiple machine learning approaches,†Energies, vol. 12, pp 3586-3599, Sep. 2019.

D. H. Barus and R. Dalimi, "The integration of wind power plants to southern Sulawesi power system," in Proc. ICECCE’20, 2020, paper 290. p. 643.

“LODSSPS 2019 data sheet,†SSLDC, Makassar, Indonesia.

M. B Wilk and R. Gnanadesikan, “Probability plotting methods for the analysis of data,†Biometrika Trust, vol. 55, no. 1, pp. 1–17, Mar. 1968.

M. G. Bulmer, Principles of Statistics, 3rd ed., K. M. Earnshaw, Ed. New York, USA: Dover Publication, 1979.

P. H. Westfall, “Kurtosis as peakedness, 1905–2014. R.I.P.,†The American Statistician, vol. 68, no. 3, pp. 191–195, Jul. 2014.

H. Madsen, P. Pinson, and G. Kariniotakis, “Standardizing the performance evaluation of short-term wind power prediction models,†Wind Engineering, vol. 29, no. 6, pp. 475-489, Dec. 2005.

M. Vahidzadeh, C. D. Markfort, “An induction curve model for prediction of power output of wind turbines in complex conditions,†Energies, vol. 13, no. 4, pp. 891-906, 2020.

L. Lledo, V. Torralba, A Soret, J. Ramon, and F. J. Doblas-Reyes, “Seasonal forecasts of wind power generation,†Renewable Energy, vol. 143, pp. 91-100, Dec. 2019.

H. Arslan, H. Baltaci, B. O. Akkoyunlu, S. Karanfil, and M. Tayanc, “Wind speed variability and wind power potential over Turkey: Case studies for Çanakkale and İstanbul,†Renewable Energy, vol. 145, pp. 1020–1032, Jan. 2020.




DOI: http://dx.doi.org/10.18517/ijaseit.11.2.12385

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