Robust Optimal Dispatch of Power Systems with Wind Farm
Abstract
With the rapid development of new energy power generation, large-scale wind power generation has been integrated intopower grids. However, the fluctuation and discontinuity of wind power have introduced challenges to the safe and reliableoperation of power systems. Therefore, constructing a reasonable dispatching method considering the uncertainty of windpower output has become an important topic. This study aims to establish a reasonable power system dispatching optimizationmethod considering the uncertainty of wind power output. Hence, an ellipsoidal robust set of wind power outputwas initially constructed in accordance with the predicted value and predicted error of wind power. Second, a power systemoptimization dispatch model of automatic generation control (AGC) was established on the basis of the robust set. This modelaimed to minimize the cost of power generation and maximize the use of wind power according to the following constraintconditions: power system power balance, upper and lower limit of wind and thermal power unit outputs, climbing power, andspinning reserve. Finally, the internal point method was employed to solve the example. Results show that on the premiseof safe operation, the total operating cost of the robust optimization dispatch method is decreased by 8.64% compared withthat of the traditional dispatch method, and the economic efficiency is improved. The robust optimal dispatch considers theuncertainty of wind power output, and load shedding scene seldom occurs, thereby enhancing the operational reliability. Thisstudy can improve the reliability and economics of power system operation and provide a basis for optimization dispatch ofpower systems.References
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correlation of wind power generation. Proceedings of
the Csee, 37 (9), 2500{2506.
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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 to
wind energy integration to grid. Renewable and Sus-
tainable 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 tradeo between security and economy.
Proceedings of the CSEE, 34 (7), 1033{1040.
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 dierent 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 Re-
search, 79 (1), 71{79.
8.Wenmeng, Z., Mingbo, L., Jianquan, Z., and al, et
(2015) Abileve decomposition and coordination economic
dispatch method for powe 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 wind
power 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 accommodation
by nonparametric conditional probabilistic forecasting.
Power System Technology, 41 (5), 1443{1450.
12.XinYi, Z., Q, Y.J., and J, Z.X. (2017) Dynamic
economic dispatch incorporating multiple wind
farms based on t 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 multi-area 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 multi-time 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 Transactions
on 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 compressedair 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 bulk
energy 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) Twostage
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. Pro-
ceedings 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., Rajsl, I., akovi´c, J., and Delimar, M.
(2018) Duality based risk mitigation method for construction
of joint hydro-wind coordination short-run
marginal 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 to
wind energy integration to grid. Renewable and Sus-
tainable 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 tradeo between security and economy.
Proceedings of the CSEE, 34 (7), 1033{1040.
Published
2020-04-23
How to Cite
ZHANG, Jinhua et al.
Robust Optimal Dispatch of Power Systems with Wind Farm.
Journal of Power Technologies, [S.l.], v. 100, n. 2, p. 92-101, apr. 2020.
ISSN 2083-4195.
Available at: <https://papers.itc.pw.edu.pl/index.php/JPT/article/view/1649>. Date accessed: 21 nov. 2024.
Issue
Section
Energy Engineering and Technology
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