Abstract
As the development of wind power, especially the planning and construction of large-scale wind power base, doing research on acceptance capacity for wind power and auxiliary services recovering mechanism of wind power integration. Improved auxiliary services recovering mechanism plays a very important role in improving grid acceptance, reducing abandoned wind power and improving economic efficiency of wind power. An alternative approach is to compensate those conventional units providing support services for wind power units. Ancillary services caused by wind power integration include peak shaving, frequency modulation, automatic generation control (AGC), reserve, reactive power regulation and black startup. In this paper, we consider peak shaving, the peak shaving costs caused by wind power integration is quantified and the applicability of cooperative method on peak shaving costs apportion. A numerical example is accepted to analyze the energy consumption increments of thermal power units. Also, we use Shapley allocation method to determine the peak shaving costs recovering for each unit caused by wind power integration. The results tend to be a reference for peak shaving costs allocation for conventional units after wind power integration.
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References
Yi, L., Zhu, M., Wei, L.: A computing method for peak load regulation ability of northwest china power grid connected with large-scale wind farms. Power Syst. Technol. 34(2), 129–132 (2010)
Simopoulos, D.N., Kavatza, S.D., Vournas, C.D.: An enhanced peak shaving method for short term hydrothermal scheduling. Energy Convers. Manag. 48, 3018–3024 (2007)
Zoumas, C.E., Bakirtzis, A.G., Theocharis, J.B., Petridis, V.: A genetic algorithm solution approach to the hydrothermal coordination problem. IEEE Trans. Power Syst. 19(2), 1356–1364 (2004)
Xie, J., Bai, X., Gan, D.: Evaluation and incentive mechanism of peaking capability of hydroelectric/thermo electric generators. J Zhejiang Univ. (Eng. Sci.) 43(11), 2079–2084 (2009)
Xie, S., Cheng, C., Cai, H.: The improved approach of thermal power peaking. Autom. Electr. Power Syst. 30(1), 89–93 (2006)
Gao, X., Peng, J., Luo, A.: Anucleolus solution in disconnected local areas for fixed transmission cost allocation of multi-area network. Relay 35(23), 39–42 (2007)
Zhou, X., Du, S.: A novel nucleolus theory based allocation method of power losses in bilateral electricity markets. Proc. CSEE 25(1), 60–65 (2005)
Xie, J., Zhang, X., Wu, F., Fu, R.: Peaking cost allocation using cooperative game theory and engineering concept. Power Syst. Prot. Control 40(11), 16–23 (2012)
Doorman, G.L.: Capacity subscription: solving the peak demand challenge in electricity markets. IEEE Trans. Power Syst. 20(1), 239–245 (2005)
Zhang, W., Wang, X., Wu, X., Yao, L.: An analysis model of power system with large-scale wind power and transaction mode of direct power purchase by large consumers involved in system scheduling. Proc. CSEE 35(12), 2927–2935 (2015)
Wang, J., Wang, N., Ma, Y., et al.: Study on cost allocation mechanism of wind power intergration. Mod. Electr. Power 27(4), 35–39 (2010)
He, Y., Hu, J., Yan, Z., et al.: Compensation mechanism for ancillary service cost of grid-integration of large-scale wind farms. Power Syst. Technol. 37(12), 3552–3557 (2013)
Liu, B., Chen, L., Wang, Y., et al.: Allocating reserve cost for hedging against wind generation uncertainty: a coalitional-game-theoretic approach. Control Theory Appl. 33(4), 437–445 (2016)
Mei, S., Wang, Y., Liu, F.: A game theory based planning model and analysis for hybrid power system with wind generators-photovoltaic panels-storage batteries. Autom. Electr. Power Syst. 35(20), 13–19 (2011)
Wang, Y., Mei, S., Liu, F.: Imputation schemes for the cooperative game in the hybrid power system planning. J. Syst. Sci. Math. Sci. 32(4), 418–428 (2012)
Mei, S.W., Wang, Y.Y., Liu, F.: Game approaches for hybrid power system planning. IEEE Trans. Sustain. Energy 3(3), 506–517 (2012)
Karngelos, E., Bouffard, F.: A cooperative game theory approach to wind power generation imbalance cost allocation. http://www.pscc-central.org/uploads/tx_ethpublications/fp42_01.pdf. 2011/2013
Zhang, H., Yin, Y., Shen, H.: Peak-load regulating adequacy evaluation associated with large-scale wind power peak-load regulating adequacy evaluation associated with large-scale wind power integration. Proc. CSEE 31(22), 26–31 (2011)
Cai, A.: Research on Ancillary Service for Generation Companies in Northwest Grid [D]. North China Electric Power University, Beijing (2012)
Xiao, C., Wang, N., Ding, K.: System power regulation scheme for jiuquan wind power base. Proc. CSEE 30(10), 1–7 (2010)
Lv, X., Liu, G., Huang, Z.: HUANG Zi-yuan. “The adjusting discharge peak methods and the exiting questions’. Power Syst. Eng. 23(9), 37–40 (2007)
Liu, H.: The Crowding-Out Effect Caused by Wind Power Increase and the Method to Evaluate the Benefit of Wind Power. [D]. Northeast Dianli University, Jilin (2012)
Zhao, X., Wang, M., Zhao, Y., Wu, Q.: A model of compensation mechanism on peak-regulating ancillary services based on capacity variance across thermal power units. Autom. Electr. Power Syst. 37(4), 57–61 (2013)
He, G.: A System of Thermal Unit Start-Up (Shut-Down) Cost Based on.NET Technology. [D]. Huazhong University of Science and Technology, Wuhan (2006)
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Dong, J., Xue, G., Li, R. et al. Research on peak shaving costs and allocation of wind power integration using scalable computing method. Cluster Comput 20, 391–400 (2017). https://doi.org/10.1007/s10586-016-0718-y
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DOI: https://doi.org/10.1007/s10586-016-0718-y