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A game theory-based coordination and optimization control methodology for a wind power-generation hybrid energy storage system. (English) Zbl 1458.93012

Summary: The installation of an energy storage system to smooth the fluctuations of wind power output at a certain wind farm can improve the electric quality of wind power connected to the grid. In order to reduce the capacity of the energy storage system and the loss of the battery and make full use of the advantages of the super-capacitor, a game theory-based coordination and optimization control methodology for a wind power-generation and storage system (WPGSS) is presented in this paper. Aiming to maximize the WPGSS’s overall profit, the methodology, taking the smoothing effect of the active power, the cost of the hybrid energy storage system (HESS), and the earnings of wind power connected to grid into consideration, builds a coordination and optimization control model based on the ensemble empirical mode decomposition (EEMD) algorithm combined with game theory. In the model, the low-pass filtering signal obtained by the EEMD is used to smooth the fluctuations of wind power output, and the band-pass filtering signal and high-pass filtering signal obtained by the EEMD are used to achieve energy distribution among the HESS. Cooperative game theory is introduced to determine the filter order of the EEMD according to the state of charge (SOC) of the HESS and to achieve the coordination and optimization control of the WPGSS taking the maximization of the WPGSS’s overall profit as the game’s goal constraint conditions. The genetic algorithm (GA) and particle swarm optimization (PSO) are adopted to solve the model’s optimal solution, and the simulation tests were realized to verify the effectiveness of the proposed method, which can provide a theoretical basis for the coordination and optimization control of the WPGSS.

MSC:

93A14 Decentralized systems
91A12 Cooperative games
91A80 Applications of game theory
49N90 Applications of optimal control and differential games
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[1] 1Lajouad, R., A. E. Magri, A. E. Fadili, F. Z. Chaoui, and F. Giri, “Adaptive nonlinear control of wind energy conversion system involving induction generator,” Asian J. Control, Vol. 17, No. 4, pp. 1365-1376 (2015). · Zbl 1338.93190
[2] 2Olaofe, Z. O., and K. A. Folly, “Wind energy analysis based on turbine and developed site power curves: A case‐study of Darling City,” Renew. Energy, Vol. 53, No. 9, pp. 306-318 (2013).
[3] 3Ibrahim, H., A. IIinca, and J. Perron, “Energy storage systems‐characteristics and comparisons,” Renew. Sust. Energ. Rev., Vol. 12, No. 5, pp. 1221-1250 (2008).
[4] 4Kasseris, E., Z. Samaras, and D. Zafeiris, “Optimization of a wind‐power fuel‐cell hybrid system in an autonomous electrical network environment,” Renew. Energy, Vol. 32, No. 1, pp. 57-79 (2007).
[5] 5Zolfaghari, S., G. H. Riahy, and M. Abedi, “A new method to adequate assessment of wind farms’ power output,” Energy Conv. Manag., Vol. 103, pp. 585-604 (2015).
[6] 6Ge, B., M. Wang, D. Bi, C. B. Rogers, et al., “Energy storage system‐based power control for grid‐connected wind power farm,” Int. J. Electr. Power Energy Syst., Vol. 44, No. 1, pp. 115-122 (2013).
[7] 7Fallahi, F., M. Nick, G. H. Riahy, S. H. Hosseinian, and A. Doroudi, “The value of energy storage in optimal non‐firm wind capacity connection to power systems,” Renew. Energy, Vol. 64, pp. 34-42 (2014).
[8] 8Xie, S. X., and Q. L. Wang, “Application of fuzzy energy storage in a distributed generation system,” East China Electric Power, Vol. 39, No. 7, pp. 1179-1182 (2011).
[9] 9Yan, G., X. Zhu, J. H. Li, G. Mu, W. H. Luo, and K. Yang, “Control strategy design for hybrid energy storage systems with built‐in service life calculation,” Autom. Electric Power Syst., Vol. 37, No. 1, pp. 110-114 (2013).
[10] 10Feng, H. X., J. Liang, F. Zhang, C. F. Wang, and B. H. Sun, “Optimized energy storage capacity of wind farms concerning dispatching plan and economical operation,” Autom. Electric Power Syst., Vol. 37, No. 1, pp. 90-95 (2013).
[11] 11Dhillon, J., A. Kumar, and S. K. Singal, “A stochastic approach for the operation of a wind and pumped storage plant under a deregulated environment,” Int. J. Green Energy, Vol. 13, No. 1, pp. 55-62 (2016).
[12] 12Gelazanskas, L., A. Baranauskas, and K. Gamage, “Hybrid wind power balance control strategy using thermal power, hydro power and flow batteries,” Int. J. Electr. Power Energy Syst., Vol. 74, pp. 310-321 (2016).
[13] 13Li, C., X. Yang, M. Zhang, H. Wang, et al., “Optimal configuration scheme for hybrid energy storage system of super‐capacitors and batteries based on cost analysis,” Autom. Electric Power Syst., Vol. 37, No. 18, pp. 20-24 (2013).
[14] 14Suliang, M., M. H. Meng, and X. Jiang, “Capacity configuration of the hybrid energy storage system based on bloch spherical quantum genetic algorithm,” Proc. CSEE, Vol. 03, pp. 592-599 (2015).
[15] 15Li, W. and G. Joos, “A power electronic interface for a battery super capacitor hybrid energy storage system for wind applications,” IEEE Conf. Power Electron. Specialists, Rhodes, Greece, pp. 1762-1768 (2008).
[16] 16Yuan, Y., C. Sun, and M. Li, “Determination of optimal super capacitor‐lead‐acid battery energy storage capacity for smoothing wind power using empirical mode decomposition and neural network,” Elect. Power Syst. Res., Vol. 127, pp. 323-331 (2015).
[17] 17Myerson, B., Game Theory: Analysis of Conflict, Harvard University Press, Cambridge and London (1991). · Zbl 0729.90092
[18] 18Vigdorovich, I. I., “The Reynolds analogy and a new formulation of the temperature‐defect law for a turbulent boundary layer on a plate,” Dokl. Phys., Vol. 61, No. 2, pp. 64-9 (2016).
[19] 19Zhang, K., C. Mao, and J. Xie, “Determination of characteristic parameters of batteries energy storage system for wind farm,” IET Renew. Power Gener., Vol. 8, No. 1, pp. 22-32 (2014).
[20] 20Hilbert‐Huang transform. Available at https://en. wikipedia.org/wiki/Hilbert‐Huang_transform.
[21] 21Branzei, R., D. Dimitrov, and S. Tijs, Models in Cooperative Game Theory, Springer, Heidelberg, Berlin, (2005). · Zbl 1079.91005
[22] 22Han, X., Z. K. Zhao, J. L. Li, and T. M. Ji, “Economic evaluation for wind power generation-hybrid energy storage system based on game theory,” Int. J. Energy Res., Vol. 41, No. 1, pp. 49-62 (2017).
[23] 23Petkovic, D., S. Shamshirband, A. Kamsin, M. Lee, O. Anicic, and V. Nikolic, “Survey of the most influential parameters on the wind farm net present value (NPV) by adaptive neuro‐fuzzy approach,” Renew. Sust. Energ. Rev., Vol. 57, pp. 1270-1278 (2016).
[24] 24Herbert, G. M. “Factorial sampling plans for preliminary computational experiments,” Technometrics, Vol. 33, No. 2, pp. 161-174 (1991).
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