Z-number-based negotiation model for determining two-part transmission tariffs of cross-regional transmission projects

Bo Zou; Ying Zhou; Jiahua Hu; Fushuan Wen; Zhao-Yang Dong; Yu Zheng; Rui Zhang

Z-number-based negotiation model for determining two-part transmission tariffs of cross-regional transmission projects

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Author(s): Bo Zou ¹ ; Ying Zhou ² ; Jiahua Hu ¹ ; Fushuan Wen ^{1, 3} ; Zhao-Yang Dong ⁴ ; Yu Zheng ⁴ ; Rui Zhang ⁴
- Affiliations: 1: School of Electrical Engineering, Zhejiang University, Hangzhou 310027, People's Republic of China ;
  2: Zhejiang Huayun Electric Power Engineering Design Consulting Co. Ltd, Hangzhou 310014, People's Republic of China ;
  3: Department of Electrical and Electronic Engineering, Universiti Teknologi Brunei, Bandar Seri Begawan BE1410, Brunei ;
  4: Electric Power Research Institute, China Southern Power Gird, Guangzhou 510080, People's Republic of China
Source: Volume 11, Issue 7, 11 May 2017, p. 1829 – 1838
DOI: 10.1049/iet-gtd.2016.1742 , Print ISSN 1751-8687, Online ISSN 1751-8695

Received 04/11/2016, Accepted 27/01/2017, Revised 24/12/2016, Published 06/02/2017

For cross-regional transmission projects, the two-part transmission pricing mechanism is suggested so as to promote the sustainable development of cross-regional electricity trading. In the two-part transmission pricing mechanism, appropriately determining the capacity charging ratio (CCR) is an important issue not well solved. Given this background, a Z-number-based risk-minimised negotiation model is developed for a transmission company and a power purchaser to achieve an agreeable CCR under incomplete information. The uncertainty distribution of the future annual electricity transmission quantity is first estimated by the Z-number-based multiple Z-valuations; and then, the benefit and risk loss measured by the well-established conditional value at risk (CVaR) are analysed for the participating two parties. Subsequently, the negotiation model where each negotiator is to minimise its risk loss under a given lowest acceptable benefit constraint and the estimations of the opponent's risk tolerance and negotiation strategy is presented to determine the optimal offer. Finally, the ± 500 kV Xiluodu−Guangdong direct current (DC) transmission project in the southern region of China is employed to demonstrate the basic characteristics of the proposed model.

References

1. 1)
  - 30. Ren, Y.L., Zhang, X.H., Huang, Q.H.: ‘Comparatively research of methods of electricity pricing at transitional period in China’, J. Chongqing Univ., 2002, 25, (9), pp. 134–138.
2. 2)
  - 31. Lowrance, W.W.: ‘Of acceptable risk’ (William Kaufmann, Los Altos, CA, 1976).
3. 3)
  - 5. Coase, R.H.: ‘The marginal cost controversy’, Economica, 1946, 13, (51), pp. 169–182.
4. 4)
  - 10. Khatib, S.E., Galiana, F.D.: ‘Negotiating bilateral contracts in electricity markets’, IEEE Trans. Power Syst., 2007, 22, (2), pp. 553–562.
5. 5)
  - 14. Yager, R.R.: ‘On Z-valuations using Zadeh's Z-numbers’, Int. J. Intell. Syst., 2012, 27, (3), pp. 259–278.
6. 6)
  - 12. Soroudi, A., Amraee, T.: ‘Decision making under uncertainty in energy systems: state of the art’, Renew. Sust. Energy Rev., 2013, 28, pp. 376–384.
7. 7)
  - 8. Osborne, M.J., Rubinstein, A.: ‘Bargaining and markets’ (San Diego: Academic Press, 1990).
8. 8)
  - 21. Yu, N.P., Somani, A., Tesfatsion, L.: ‘Financial risk management in restructured wholesale power markets: Concepts and tools’. Proc. IEEE Power and Energy Society General Meeting, Minneapolis, MN, July 2010, pp. 1–8.
9. 9)
  - 6. Shirmohammadi, D., Filho, V., Gorenstin, B., et al: ‘Some fundamental technical concepts about cost based transmission pricing’, IEEE Trans. Power Syst., 1996, 11, (2), pp. 1002–1008.
10. 10)
  - 4. Olmos, L., P.-Arriaga, I.J.: ‘A comprehensive approach for computation and implementation of efficient electricity transmission network charges’, Energy Policy, 2009, 37, (12), pp. 5285–5295.
11. 11)
  - 28. Yager, R.R.: ‘On ordered weighted averaging aggregation operators in multicriteria decision making’, IEEE Trans. Syst. Man Cybern., 1988, 18, (1), pp. 183–190.
12. 12)
  - 27. Yager, R.R.: ‘Quantifier guided aggregation using OWA operators’, Int. J. Intell. Syst., 1996, 11, (1), pp. 49–73.
13. 13)
  - 9. David, A.K., Wen, F.S.: ‘Bilateral transaction bargaining between independent utilities under incomplete information’, IEE Proc. Gener. Transm. Distrib., 2001, 148, (5), pp. 448–454.
14. 14)
  - 24. Kharrati, S., Kazemi, M., Ehsan, M.: ‘Equilibria in the competitive retail electricity market considering uncertainty and risk management’, Energy, 2016, 106, pp. 315–328.
15. 15)
  - 15. Azadeh, A., Kokabi, R., Saberi, M., et al: ‘Trust prediction using Z-numbers and artificial neural networks’. Proc. IEEE Int. Conf. Fuzzy Syst., Beijing, China, July 2014, pp. 522–528.
16. 16)
  - 1. Zeng, M., Peng, L.L., Fan, Q.N., et al: ‘Trans-regional electricity transmission in China: status, issues and strategies’, Renew. Sust. Energy Rev., 2016, 66, pp. 572–583.
17. 17)
  - 25. Stoilov, D., Stoilov, L.: ‘Improving inter-transmission compensation in EU’, Energy policy, 2013, 62, pp. 282–291.
18. 18)
  - 2. Aguado, J.A.V., Quintana, H., Madrigal, M., et al: ‘Coordinated spot market for congestion management of inter-regional electricity markets’, IEEE Trans. Power Syst., 2004, 19, (1), pp. 180–187.
19. 19)
  - 18. Loomes, G., Sugden, R.: ‘Regret theory: an alternative theory of rational choice under uncertainty’, Econom. J., 1982, 92, (368), pp. 805–824.
20. 20)
  - 20. Rockafellar, R.T., Uryasev, S.: ‘Optimization of conditional value-at-risk’, J. Risk, 2000, 2, (3), pp. 21–41.
21. 21)
  - 3. He, Y.X., Zhu, M.Z., Xiong, W., et al: ‘Electricity transmission tariffs for large-scale wind power consumption in western Gansu province, China’, Renew. Sust. Energy Rev., 2012, 16, (7), pp. 4543–4550.
22. 22)
  - 19. Jorion, P.: ‘Value at risk’ (McGraw-Hill, New York, 1997).
23. 23)
  - 17. Bjorgan, R., Liu, C.C., Lawarree, J.: ‘Financial risk management in a competitive electricity market’, IEEE Trans. Power Syst., 1999, 14, (4), pp. 1285–1291.
24. 24)
  - 13. Zadeh, L.A.: ‘A note on Z-numbers’, Inf. Sci., 2011, 181, (14), pp. 2923–2932.
25. 25)
  - 29. Casella, G., Berger, R.L.: ‘Statistical inference’ (Duxbury, Pacific Grove, CA, 2002, p. 102).
26. 26)
  - 23. Yang, J.J., He, Y., Zou, B., et al: ‘A CVaR-based coal inventory optimization model for coal-fired power plants in electricity market environment’, Autom. Electr. Power Syst., 2014, 38, (4), pp. 51–59.
27. 27)
  - 7. Gu, H.Y., Wen, F.S., Zou, B., et al: ‘Two-part transmission pricing for west-to-east power transmission in the southern region of China’, Proc. CSU-EPSA, 2014, 26, (4), pp. 1–8.
28. 28)
  - 11. Yu, N.P., Tesfatsion, L., Liu, C.C.: ‘Financial bilateral contract negotiation in wholesale electricity markets using Nash bargaining theory’, IEEE Trans. Power Syst., 2012, 27, (1), pp. 251–267.
29. 29)
  - 26. Zadeh, L.A.: ‘Probability measures of fuzzy events’, J. Math. Anal. Appl., 1968, 23, (2), pp. 421–427.
30. 30)
  - 22. Larimi, S.M.M., Haghifam, M.R., Moradkhani, A.: ‘Risk-based reconfiguration of active electric distribution networks’, IEE Proc. Gener. Transm. Distrib., 2016, 10, (4), pp. 1006–1015.
31. 31)
  - 16. Yaakob, A.M., Serguieva, A., Gegov, A.: ‘FN-TOPSIS: fuzzy networks for ranking traded equities’, IEEE Trans. Fuzzy Syst., 2016, to be published doi: 10.1109/TFUZZ.2016.2555999.

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Z-number-based negotiation model for determining two-part transmission tariffs of cross-regional transmission projects

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