Estimating lower probability bound of power system's capability to fully accommodate variable wind generation

Bin Liu; Bingxu Zhai; Mengchen Liu; Feng Liu; Haibo Lan

Estimating lower probability bound of power system's capability to fully accommodate variable wind generation

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Author(s): Bin Liu^{1, 2} ; Bingxu Zhai² ; Mengchen Liu³ ; Feng Liu⁴ ; Haibo Lan²
- Affiliations: 1: School of Electrical Engineering and Telecommunications, The University of New South Wales , Sydney 2052 , Australia ;
  2: Jibei Electric Power Dispatching and Control Center, State Grid Corporation of China , Beijing 100053 , People's Republic of China ;
  3: Department of Grid Connection , Goldwind Australia Pty Ltd , Sydney 2000 , Australia ;
  4: Department of Electrical Engineering , Tsinghua University , Beijing 100084 , People's Republic of China
Source: Volume 2019, Issue 16, March 2019, p. 1011 – 1015
DOI: 10.1049/joe.2018.8603 , Online ISSN 2051-3305

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)

Received 24/08/2018, Accepted 19/09/2018, Published 23/10/2018

As the penetration of wind generation increases, the uncertainty it brings has imposed great challenges to power system operation. To cope with the challenges, tremendous research work has been conducted, among which two aspects are of most importance, that is, making immune operation strategies and accessing the power system's capability to accommodate the variable energy. Driven and inspired by the latter problem, this paper will discuss the power system's capability to accommodate variable wind generation in a probability sense. Wind generation, along with its uncertainty is illustrated by a polyhedron, which contains prediction, risk, and uncertainty information. Then, a three-level optimisation problem is presented to estimate the lower probability bound of power system's capability to fully accommodate wind generation. After reformulating the inner max-min problem, or feasibility check problem, into its equivalent mixed-integer linear program (MILP) form, the bisection algorithm is presented to solve this challenging problem. Modified IEEE systems are adopted to show the effectiveness of the proposed method.

References

1. 1)
  - 5. Ummels, B.C., Gibescu, M., Pelgrum, E., et al: ‘Impacts of wind power on thermal generation unit commitment and dispatch’, IEEE Trans. Energy Convers., 2007, 22, (1), pp. 44–51.
2. 2)
  - 22. Wei, W., Liu, F., Mei, S.: ‘Dispatchable region of the Variable wind generation’, IEEE Trans. Power Syst., 2015, 30, (5), pp. 2755–2765.
3. 3)
  - 18. Bertsimas, D., Litvinov, E., Sun, X.A., et al: ‘Adaptive robust optimization for the security constrained unit commitment problem’, IEEE Trans. Power Syst., 2013, 28, (1), pp. 52–63.
4. 4)
  - 16. Wang, Q., Guan, Y., Wang, J.: ‘A chance-constrained two-stage stochastic program for unit commitment with uncertain wind power output’, IEEE Trans. Power Syst., 2012, 27, (1), pp. 206–215.
5. 5)
  - 14. Wu, L., Shahidehpour, M., Li, T.: ‘Stochastic security-constrained unit commitment’, IEEE Trans. Power Syst., 2007, 22, (2), pp. 800–811.
6. 6)
  - 13. Wei, W., Liu, F., Mei, S., et al: ‘Robust energy and reserve dispatch under variable renewable generation’, IEEE Trans. Smart Grid, 2014, 6, (1), pp. 369–380.
7. 7)
  - 11. Jiang, R., Wang, J., Guan, Y.: ‘Robust unit commitment with wind power and pumped storage hydro’, IEEE Trans. Power Syst., 2012, 27, (2), pp. 800–810.
8. 8)
  - 20. Jiang, R., Wang, J., Zhang, M., et al: ‘Two-stage minimax regret robust unit commitment’, IEEE Trans. Power Syst., 2013, 28, (3), pp. 2271–2282.
9. 9)
  - 30. Dudurych, I.M., O'Sullivan, J., Rogers, A., et al: ‘Tools for handling high amounts of wind generation in national control centre in Ireland’. Proc. IEEE PES General Meeting Conf., San Diego, USA, 2012, pp. 1–8.
10. 10)
  - 24. Bludszuweit, H., Antonio Dominguez-Navarro, J., Llombart, A.: ‘Statistical analysis of wind power prediction error’, IEEE Trans. Power Syst., 2008, 23, (3), pp. 983–991.
11. 11)
  - 21. Martin-Martinez, S., Gomez-Lazaro, E., Molina-Garcia, A., et al: ‘Impact of wind power curtailments on the spanish power system operation’. Proc. IEEE PES General Meeting Conf. and Exposition, National Harbor, USA, 2014, pp. 1–5.
12. 12)
  - 25. Hodge, B., Milligan, M.: ‘Wind power forecasting error distributions over multiple timescales’. Proc. IEEE PES General Meeting Conf., Detroit, USA, 2011, pp. 1–8.
13. 13)
  - 15. Constantinescu, E.M., Zavala, V.M., Rocklin, M., et al: ‘A computational framework for uncertainty quantification and stochastic optimization in unit commitment with wind power generation’, IEEE Trans. Power Syst., 2011, 26, (1), pp. 431–441.
14. 14)
  - 29. Lofberg, J.: ‘Yalmip: a toolbox for modeling and optimization in matlab’. Proc. IEEE Int. Symp. on Computer Aided Control Systems Design, Taipei, 2004, pp. 284–289.
15. 15)
  - 6. Daneshi, H., Srivastava, A.K.: ‘Security-constrained unit commitment with wind generation and compressed air energy storage’, IET Gener. Transm. Distrib., 2012, 6, (2), pp. 167–175.
16. 16)
  - 10. Pappala, V.S., Erlich, I., Rohrig, K., et al: ‘A stochastic model for the optimal operation of a wind-thermal power system’, IEEE Trans. Power Syst., 2009, 24, (2), pp. 940–950.
17. 17)
  - 12. Guan, Y., Wang, J.: ‘Uncertainty sets for robust unit commitment’, IEEE Trans. Power Syst., 2014, 29, (3), pp. 1439–1440.
18. 18)
  - 9. Wang, J., Shahidehpour, M., Li, Z.: ‘Security-constrained unit commitment with volatile wind power generation’, IEEE Trans. Power Syst., 2008, 23, (3), pp. 1319–1327.
19. 19)
  - 7. Jabr, R.A., Karaki, S., Korbane, J.A.: ‘Robust multi-period OPF With storage and renewables’, IEEE Trans. Power Syst., 2014, 5, (30), pp. 2790–2799.
20. 20)
  - 3. Wu, L., Shahidehpour, M., Li, Z.: ‘Comparison of scenario-based and interval optimization approaches to stochastic SCUC’, IEEE Trans. Power Syst., 2012, 27, (2), pp. 913–921.
21. 21)
  - 2. Morales, J.M., Conejo, A.J., Madsen, H., et al: ‘Integrating renewables in electricity markets: operational problems’ (Springer, Berlin, 2014).
22. 22)
  - 26. Lu, N., Diao, R., Hafen, R.P., et al: ‘A comparison of prediction error generators for modeling wind and load uncertainty’. IEEE PES General Meeting, Vancouver, Canada, 2013, pp. 1–5.
23. 23)
  - 23. Wang, C., Liu, F., Wang, J., et al: ‘Risk-Based admissibility assessment of wind generation integrated into a bulk power system generation integrated into a bulk power system’, IEEE Trans. Sust. Energy, 2016, 7, (1), pp. 325–336.
24. 24)
  - 4. Restrepo, J.F., Galiana, F.D.: ‘Assessing the yearly impact of wind power through a new hybrid deterministic/stochastic unit commitment’, IEEE Trans. Power Syst., 2011, 26, (1), pp. 401–410.
25. 25)
  - 19. Wang, Y., Xia, Q., Kang, C.: ‘Unit commitment with volatile node injections by using interval optimization’, IEEE Trans. Power Syst., 2011, 26, (3), pp. 1705–1713.
26. 26)
  - 28. GUROBI User's Manual, GUROBI., 2016, release 6.5.
27. 27)
  - 17. Zheng, Q.P., Wang, J., Liu, A.L.: ‘Stochastic optimization for unit commitment: A review’, IEEE Trans. Power Syst., 2014, 30, (4), pp. 1439–1440.
28. 28)
  - 8. Opathella, C., Venkatesh, B.: ‘Managing uncertainty of wind energy With wind generators cooperative’, IEEE Trans. Power Syst., 2013, 28, (3), pp. 2918–2928.
29. 29)
  - 1. Handschin, E., Petroianu, A.: ‘Energy management systems: operation and control of electric energy transmission systems’ (Springer Press, Berlin, 1991).
30. 30)
  - 27. CPLEX User's Manual, IBM, Armonk, New York, 2015, release 12.6.1.

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Estimating lower probability bound of power system's capability to fully accommodate variable wind generation

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