Two-stage robust transaction optimization model and benefit allocation strategy for new energy power stations with shared energy storage considering green certificate and virtual energy storage mode

被引:15
作者
Ju, Liwei [1 ,2 ]
Bai, Xiping [1 ,2 ]
Li, Gen [3 ]
Gan, Wei [4 ]
Qi, Xin [1 ,2 ]
Ye, Fan [5 ]
机构
[1] North China Elect Power Univ, Sch Econ & Management, Changping 102206, Beijing, Peoples R China
[2] North China Elect Power Univ, Beijing Key Lab New Energy & Low Carbon Dev, Beijing 102206, Peoples R China
[3] Tech Univ Denmark DTU, Dept Engn Technol & Didact, DK-2750 Ballerup, Denmark
[4] Cardiff Univ, Sch Engn, Cardiff CF24 3AA, Wales
[5] State Grid Jiangxi Extra High Voltage Co, Nanchang 330096, Peoples R China
基金
中国国家自然科学基金; 北京市自然科学基金; 中国博士后科学基金;
关键词
Variable renewable energy; Shared energy storage; Two-stage robust optimization; Benefit allocation;
D O I
10.1016/j.apenergy.2024.122996
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
In the context of the large-scale participation of renewable energy in market trading, this paper designs a cooperation mode of new energy power stations (NEPSs) and shared energy storage (SES) to participate in the power-green certificate market, which divides SES into physical energy storage and virtual energy storage. Secondly, combining the advantages of scenario generation and robust optimization (RO), a two-stage RO model with improved uncertainty interval is proposed to determine the optimal trading strategy. Then, to better align the distribution results of cooperative benefits with the actual contributions of NEPSs and SES, an entropy weight modified Shapley value benefit allocation strategy is constructed. Finally, the new energy base in Qinghai Province, China is chosen for simulation. The results show: (1) Adding energy storage and using two-stage RO are able to effectively improve the ability of NEPSs to resist uncertainty, which increases the revenue of the alliance by 22.8%. (2) The application of SES has better economic benefits than each member equipped with energy storage separately. Compared with the latter, the deviation penalty cost of the former is reduced by 66.4%, and the revenue is increased by 3.4%. (3) The proposed entropy weight modified Shapley value method embodies the important auxiliary role of SES more obviously. Based on this method, the overall satisfaction of the alliance increases by 12.6%. Generally speaking, the optimization model and benefit allocation strategy proposed in this paper can provide guidance for NEPSs and SES participating in power trading, and promote the low -carbon transformation of the power sector.
引用
收藏
页数:23
相关论文
共 47 条
[1]   Crude oil time series prediction model based on LSTM network with chaotic Henry gas solubility optimization [J].
Altan, Aytac ;
Karasu, Seckin .
ENERGY, 2022, 242
[2]   A new hybrid model for wind speed forecasting combining long short-term memory neural network, decomposition methods and grey wolf optimizer [J].
Altan, Aytac ;
Karasu, Seckin ;
Zio, Enrico .
APPLIED SOFT COMPUTING, 2021, 100
[3]  
Andrey C, 2019, Energy Econ, V84
[4]  
[Anonymous], 2022, Studies from Aalto University update current data on renewable energy (optimization of photovoltaic and wind generation systems for autonomous microgrids with pev-parking lots), P686
[5]  
[Anonymous], 2024, National data
[6]   Two-stage robust planning-operation co-optimization of energy hub considering precise energy storage economic model [J].
Chen, Cong ;
Sun, Hongbin ;
Shen, Xinwei ;
Guo, Ye ;
Guo, Qinglai ;
Xia, Tian .
APPLIED ENERGY, 2019, 252
[7]  
Chenfei L, 2022, Sino-Glob Energy, V27, P86
[8]  
Dfcfw, 2024, About us
[9]  
Dogan K, 2022, Energy Econ, V106
[10]   An improved Shapley value-based pro fi t allocation method for CHP-VPP [J].
Fang, Fang ;
Yu, Songyuan ;
Liu, Mingxi .
ENERGY, 2020, 213