Welcome to Francis Academic Press

Academic Journal of Humanities & Social Sciences, 2020, 3(2); doi: 10.25236/AJHSS.2020.030214.

Optimital Decision-making Model of Mixed Ownership Reform in Power Grid Enterprise for Investing Hrbird Energy System Project

Author(s)

Xiaoxu Fu*, Caixia Tan, Jiang Wang, Jinghan Zhou, Zhongfu Tan

Corresponding Author:
Xiaoxu Fu
Affiliation(s)

School of Economics and Management, North China Electric Power University, Beijing 102206, China;

*Correspondenc: 183758841@qq.com

Abstract

The randomness of wind generation is one of the main factors restricting gird connection of wind generation. The involvements of energy storage systems and resources used for demand response in the process of optimization for wind power are useful means to enhance its regulation capacity. Considering the uncertainty of wind generation in day-ahead plans, this paper proposes a coordinated scheduling optimization model for Wind-ES hybrid systems with demand response via electric vehicles. The model can be used to apply energy storage systems and electric vehicles simultaneously to both peak shaving/valley filling and wind generation plan tracking to achieve the coordination between the on-grid revenue and penalty cost of the hybrid system, so as to develop the optimal strategy for maximum benefits. The wind power is modeled by using scenario analysis method, and the mixed integer programming problem of this paper is solved via CPLEX software. The case study results show that the coordinated scheduling optimization model can not only earn additional revenue for electric vehicle owners, but also effectively improve the economy of wind power grid connection, which provides an important reference for scheduling the demand response resources of electric vehicles to consume wind generation.

Keywords

V2G; Wind-ES hybrid systems; scenario reduction; uncertainty; mixed integer programming

Cite This Paper

Xiaoxu Fu, Caixia Tan, Jiang Wang, Jinghan Zhou, Zhongfu Tan. Optimital Decision-making Model of Mixed Ownership Reform in Power Grid Enterprise for Investing Hrbird Energy System Project. Academic Journal of Humanities & Social Sciences (2020) Vol. 3, Issue 2: 124-138. https://doi.org/10.25236/AJHSS.2020.030214.

References

[1] Wind power grid connected operation of NEA in 2017 [EB / OL]. [2018-1-26.] 

[2] Wu Xiong, Wang Xiuli, Li Jun, et al. Joint scheduling model and solution of wind energy storage hybrid system [J]. Chinese Journal of electrical engineering, 2013,33 (13): 10-17. 

[3] Dukpa A, Duggal I, Venkatesh B, et al. Optimal participation and risk mitigation of wind generators in an electricity market[J]. IET Renewable Power Generation, 2010, 4(2): 165-175.

[4] Garcia-Gonzalez J, De l M R M R, Santos L M, et al. Stochastic joint optimization of wind generation and pumped-storage units in an electricity market[J]. IEEE Transactions on Power Systems, 2008, 23(2): 460-468.

[5] Huang Yang, Hu Wei, min Yong, Luo Weihua, Wang Zhiming, Ge Weichun. Multi objective coordinated scheduling of wind storage joint system considering day ahead planning [J]. Chinese Journal of electrical engineering, 2014,34 (28): 4743-4751. 

[6] Hu Zechun, Xia Rui, Wu Linlin, Liu Hui. Optimization strategy of wind storage combined operation considering energy storage participating in frequency modulation [J]. Grid technology, 2016,40 (08): 2251-2257. 

[7] Lu Qiuyu, Hu Wei, min Yong, Wang Zhiming, Luo Weihua, Cheng Tao. Multi mode coordination and optimization strategy of wind storage system considering time dependence [J]. Power system automation, 2015,39 (02): 6-12. 

[8] You Yi, Liu Dong, Zhong Qing, et al. Multi objective optimization of energy storage system in active distribution network [J]. Power system automation, 2014, 38 (18): 46-52. 

[9] He Chuan, Liu Tianqi, Hu Xiaotong, Li Xi, Li Xingyuan. Optimal control of wind power climbing based on ultra short term wind power prediction and hybrid energy storage [J]. Grid technology, 2017,41 (03): 782-790. 

[10] Song Yihang, Tan Zhongfu, Li Huanhuan, et al. Joint optimization model of generation side, storage side and demand side to promote wind power consumption [J]. Grid technology, 2014, 38 (3): 610-615.

[11] Ju Liwei, Qin Chao, Wu Hongliang, et al. Stochastic optimization scheduling model of wind power consumption considering multi type demand response [J]. Grid technology, 2015, 39 (7): 1839-1846. 

[12] Xu Hui, Jiao Yang, Pu Lei, Tan Zhongfu, et al. Stochastic scheduling optimization model of integrated virtual power plant with wind and gas storage considering uncertainty and demand response [J]. 2017, 41 (11): 3590-3597. 

[13] Ju Liwei, Li Huanhuan, Chen Zhihong, et al. Comparative analysis model of wind power electric vehicle multiple grid connection modes based on two-step adaptive algorithm [J]. Grid technology, 2014, 38 (6): 1492-1498.

[14] Hou Jianchao, Hu Qunfeng, Tan Zhongfu. Multi objective optimization model of wind power electric vehicle coordinated dispatching considering demand response [J]. Electric power automation equipment, 2016, 36 (7): 22-27. 

[15] Pan DIF, Liu Hui, Li Yanfei. Improved algorithm for short-term multi-step prediction of wind speed in wind farms [J]. Chinese Journal of electrical engineering, 2008, 28 (26): 87-91. 

[16] Deng Yong. Research on optimal scheduling of power system units with wind farms [D]. Chongqing University, 2014. 

[17] Yan Zhengang. Study on micro grid economic dispatch considering the randomness of micro sources [D]. Southwest Jiaotong University, 2017. 

[18] Matevosyan J, Soder L. Minimization of imbalance cost trading wind power on the short-term power market[J]. IEEE Transactions on Power Systems, 2006, 21(3):1396-1404.

[19] Dai Yuanhang, Chen Lei, min Yong, Xu Fei, Hou Kaiyuan, Zhou Ying. Optimal operation of wind farm and cogeneration with heat storage [J] Chinese Journal of electrical engineering, 2017,37 (12): 3470-3479 + 3675. 

[20] Xie Yingzhao, Lu Jiping. Multi objective unit combination optimization model and solution of wind storage hybrid system [J]. Electric power automation equipment, 2015, 35 (3): 18-26. 

[21] Matevosyan, Julija, Söder, Lennart. Minimization of imbalance cost trading wind power on the short-term power market[J]. IEEE Transactions on Power Systems, 2006, 21(3):1396-1404.

[22] Holger Heitsch, Werner Römisch. Scenario Reduction Algorithms in Stochastic Programming[J].Computational Optimization and Applications, 2003, 24(2-3):187-206.

[23] Shi Quansheng, Ping zongfei, Chen Minjun. Price linkage model considering electric vehicle access [J]. Electric power automation equipment, 2014,34 (11): 34-40.