Robust Optimal Dispatch of Power Systems with Wind Farm

• Autorzy: Zhang Jinhua , Gu Bo , Meng Hang , Fu Chentao , Zhu Xueling
• Języki publikacji: EN

Abstrakty

EN

With the rapid development of new energy power generation, large-scale wind power generation has been integrated into power grids. However, the fluctuation and discontinuity of wind power pose challenges to the safe and reliable operation of power systems. Therefore, constructing a reasonable dispatching method to manage the uncertainty of wind power outputhas become an important topic and this study was structured with this precise aim in mind. An ellipsoidal robust set of wind power outputs was initially constructed in accordance with the predicted value and predicted error of wind power. Second, a power system optimization dispatch model of automatic generation control (AGC) was established on the basis of the robust set. This model aimed to minimize the cost of power generation and maximize the use of wind power according to the following constraint conditions: power system power balance, upper andlower limit of wind and thermal power unit outputs, climbing power, and spinning reserve. Finally, the internal point method was employed to solve the example. Results show that, on the premise of safe operation, the total operating cost of the robust optimization dispatch method is decreased by 8.64% compared with that of the traditional dispatch method, and economic efficiency is improved. Robust optimal dispatch factors in the uncertainty of wind power output meaning the load shedding scenario seldom occurs, thereby enhancing operational reliability. This study can be used to improve the reliability and economics of power system operation and provide a basis for optimizing dispatch in power systems.

Słowa kluczowe

EN

wind power; uncertainty; prediction error; robust optimization; economics

PL

energia wiatrowa; niepewność; błąd predykcji; optymalizacja odporna; ekonomia

• Wydawca: Institute of Heat Engineering, Warsaw University of Technology
• Czasopismo: Journal of Power Technologies
• Rocznik: 2020
• Tom: Vol. 100, nr 2
• Strony: 92--101
• Opis fizyczny: Bibliogr. 34 poz., rys., tab., wykr.

Twórcy

autor

Zhang Jinhua

• North China University of Water Resources and Electric Power, Henan Province, 450046, China

autor

Gu Bo

• North China University of Water Resources and Electric Power, Henan Province, 450046, China

autor

Meng Hang

• North China Electric Power University, Beijing, 102206, China

autor

Fu Chentao

• North China University of Water Resources and Electric Power, Henan Province, 450046, China

autor

Zhu Xueling

• North China University of Water Resources and Electric Power, Henan Province, 450046, China

Bibliografia

• 1. Lina, A., and B., W. (2018) A review of the state-of-the-art in windenergy reliability analysis.Renewable& Sustainable Energy Reviews, 81 (1), 1643-1651.
• 2. Yusheng, X., L, X., X, F., and al., et (2014) A review on impacts of wind power uncertainties on power systems. Proceedings of the Csee, 34 (29), 5029-5040.
• 3. Yoshida, K., S, N., S, T., and al., et (2017) A dispatch operation method of wind farm to deal with ramp events and evaluation of required battery capacity. IEEE Transactions on Power & Energy, 137 (10), 687-696.
• 4. Murage, M. W., and Anderson, C. L. (2014) Contribution of pumped hydro storage to integration of wind power in kenya: An optimal control approach. Renewable Energy, 63, 698-707.
• 5. Billinton, R., and Huang, D. (2011) Incorporating wind power in generating capacity reliability evaluation using different models. IEEE Transactions on Power Systems, 26 (4), 2509-2517.
• 6. Chun-Lung, C. (2008) Optimal wind-thermal generating unit commitment. IEEE Transactions on Energy Conversion, 23 (1), 273-280.
• 7. Moyano, C.F., and Lopes, J. A. P. (2009) An optimization approach for wind turbine commitment and dispatch in a wind park. Electric Power Systems Research, 79 (1), 71-79.
• 8. Wenmeng, Z., Mingbo, L., Jianquan, Z., and al, et (2015) Abileve decomposition and coordination economic dispatch method for power plants network considering stochastic wind generation. Power System Technology, 39 (7), 1847-1854.
• 9. Jianfang, T., Yashan, M., Qiaozhu, Z., and al, et (2015) Security constrained unit commitment with wind power based on evaluation of wind power penetration capacity. Power System Technology, 39 (9), 2398-2403.
• 10. Jun, X., Lu W, X. F. U., and al, et (2016) Reactive power planning with consideration of windpower probability distribution uncertainty for distribution network. Electric Power Automation Equipment, 36 (6), 40-47.
• 11. Yida, Y., L, W., Q, Y., and al, et (2017) Optimal method of improving wind power accommodationby nonparametric conditional probabilistic forecasting. Power System Technology, 41 (5), 1443-1450.
• 12. Xin Yi, Z., Q, Y.J., and J, Z.X. (2017) Dynamic economic dispatch incorporating multiple wind farms based on fft simplied chance constrained programming. Journal of Zhejiang University, 51 (5), 976-983.
• 13. Alismail, F., Xiong, P., and Singh, C. (2018) Optimal wind farm allocation in multiarea power systems using distributionally robust optimization approach. IEEE Transactions on Power Systems, 33(1), 536-544.
• 14. Fan, L., K, W., U, W.W., and al, et (2017) A study of multitime scale robust schedule and dispatch methodology. Power System Technology, 41 (5), 1576-1582.
• 15. Chen, H., Xuan, P., Wang, Y., Tan, K., and Jin, X. (2016) Key technologies for integration of multitype renewable energy sources-research on multi-timeframe robust scheduling/ dispatch. IEEE Transactions on Smart Grid, 7 (1), 471-480.
• 16. Cobos, N. G., Arroyo, J. M., Alguacil, N., and Street, A. (2019) Robust energy and reserve scheduling under wind uncertainty considering fast acting generators. IEEE Transactions on Sustainable Energy,10 (4), 2142-2151.
• 17. Methaprayoon, K., Lee, W. J., Yingvivatanapong,C., and Liao, J. (2007) An integration of ANN wind power estimation into UC considering the forecasting uncertainty. IEEE Systems Technical Conference on Industrial and Commercial Power, 43.
• 18. Doherty, R., and OMalley, M. (2005) A new approach to quantify reserve demand in systems with signicant installed wind capacity. IEEE Transactionson Power Systems, 20 (2), 587-595.
• 19. Attarha, A., Amjady, N., Dehghan, S., and Vatani, B. (2018) Adaptive robust self-scheduling for a wind producer with compressed air energy storage. IEEE Transactions on Sustainable Energy, 9 (4),1659-1671.
• 20. Holttinen, H., Milligan, M., Kirby, B., Acker, T., Neimane, V., and Molinski, T. (2008) Using standard deviation as a measure of increased operational reserve requirement for wind power. Wind Engineering, 32 (4), 355-377.
• 21. Black, M., and Strbac, G. (2007) Value of bulkenergy storage for managing wind power uctuations. IEEE Transactions on Energy Conversion, 22 (1),197-205.
• 22. Ortega-Vazquez, M. A., and Kirschen, D. S. (2009) Estimating the spinning reserve requirements in systems with signicant wind power generation penetration. IEEE Transactions on Power Systems, 24 (1),114-124.
• 23. LI, R., H, Z., J, L., and al, et (2016) A robust and economic dispatch methodology for interconnected power system by considering the uncertainty of wind power. Modern Electric Power, (4), 15-22.
• 24. Yu, S. H. U. I., J, L., H, G., and al, et (2018) Two-stage distributed robust cooperative dispatch for integrated electricity and natural gas energy systems considering uncertainty of wind power. Automation of Electric Power Systems, 42 (13), 43-50+75.
• 25. Wang, B., N, T., S, Z., and al, et (2017) Stochastic & adjustable robust hybrid dispatch model of power system considering demand response’s participation in large-scale wind power consumption. Proceedings of the Csee, 37 (21), 6339-6346.
• 26. Sun, J., B, L., F, L., and al, et (2014) Modeling and evaluation of uncertainty set considering wind power prediction error correlation. Automation of Electric Power Systems, 38 (18), 27-32 +98.
• 27. Ilak, P., Rajšl, I., Đaković, J., and Delimar, M. (2018) Duality based risk mitigation method for construction of joint hydro-wind coordination short-runmarginal cost curves.Energies,11(5), 1254.
• 28. WU, W., K, W., LI, G., and al, et (2017) Modeling ellipsoidal uncertainty set considering conditional correlation of wind power generation. Proceedings of the Csee, 37(9), 2500-2506.
• 29. Grant, M., Boyd, S., and Ye, Y. (2007) Disciplined convex programming. Nonconvex Optimization & Its Applications, Kluwer Academic Publishers.
• 30. Li, Z., Wu, W., and Zhang, B. (2014) A robust interval economic dispatch method accommodating large-scale wind power generation. part one: Dispatch scheme and mathematical model. Automation of Electric Power Systems, 38 (20), 33-39.
• 31. Behera, S., Sahoo, S., and Pati, B.B. (2015) A review on optimization algorithms and application towind energy integration to grid. Renewable and Sustainable Energy Reviews, 48, 214-227.
• 32. Qianwen, Z., X, W., T, Y., and al, et (2017) A robust dispatch method for power grid with wind farms. Power System Technology, 41 (5), 1451-1459.
• 33. Yan, J. (2014) Uncertainty of Wind Power Forecasting and Power System Economic Dispatch.
• 34. Chen, J., Wu, W., Zhang, B., and al, et (2014) A robust interval wind power dispatch method considering the trade off between security and economy. Proceedings of the CSEE, 34 (7), 1033-1040.

Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).