RETRACTED: Risk-constrained stochastic power procurement of storage-based large electricity consumer (Retracted Article)

被引:31
作者
Cao, Yan [1 ]
Wang, Qiangfeng [1 ]
Fan, Qingming [1 ]
Nojavan, Sayyad [2 ]
Jermsittiparsert, Kittisak [3 ]
机构
[1] Xian Technol Univ, Sch Mechatron Engn, Xian 710021, Peoples R China
[2] Univ Bonab, Dept Elect Engn, Bonab, Iran
[3] Chulalongkorn Univ, Social Res Inst, Bangkok 10330, Thailand
关键词
Large electricity consumer; Risk-measuring; Energy trading and business; Energy storage management; Stochastic programming; Downside risk constraints; DEMAND RESPONSE; RENEWABLE ENERGY; OPERATION; SYSTEM; PERFORMANCE; STRATEGY; MODEL;
D O I
10.1016/j.est.2019.101183
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
Large electricity consumers can be either a large industrial consumer or a coalition of small electricity consumers. Large consumers (LCs) confront with various uncertainties due to the use of various power resources in the power procurement process, such as renewable resources, self-generation units, forward contracts, and pool market. These uncertainties can be lead to many financial risks for LCs. In this paper, the stochastic power procurement problem of large consumers is solved, and the new risk-measurement method is used to analyze the large consumer risks in power procurement process. The mentioned risk-measurement method is called downside risk constraints (DRC) method, which is used to model the financial risk imposed from uncertain parameters along with the stochastic problems. According to obtained results, it can be concluded that DRC method is a non-equilibrium method, which is applied clearly as a constraint to the optimization problem. In addition by using the DRC, LC can experience lower-risk strategy in the power procurement problem. Also, using DRC can make the total cost of large consumer independent of scenarios, which led to the lower-risk experiencing by the large consumer. Finally, results are expressed that lower-risk cost in DRC is less than the cost of the worst scenario in stochastic programming.
引用
收藏
页数:10
相关论文
共 31 条
[1]   Modeling and prioritizing demand response programs in power markets [J].
Aalami, H. A. ;
Moghaddam, M. Parsa ;
Yousefi, G. R. .
ELECTRIC POWER SYSTEMS RESEARCH, 2010, 80 (04) :426-435
[2]   RETRACTED: Energy storage system and demand response program effects on stochastic energy procurement of large consumers considering renewable generation (Retracted article. See vol. 14, pg. 6040, 2020) [J].
Aalami, Habib Allah ;
Nojavan, Sayyad .
IET GENERATION TRANSMISSION & DISTRIBUTION, 2016, 10 (01) :107-114
[3]   Optimal operation scheduling of wind power integrated with compressed air energy storage (CAES) [J].
Abbaspour, M. ;
Satkin, M. ;
Mohammadi-Ivatloo, B. ;
Lotfi, F. Hoseinzadeh ;
Noorollahi, Y. .
RENEWABLE ENERGY, 2013, 51 :53-59
[4]   Optimal offering and bidding strategies of renewable energy based large consumer using a novel hybrid robust-stochastic approach [J].
Abedinia, Oveis ;
Zareinejad, Mohsen ;
Doranehgard, Mohammad Hossein ;
Fathi, Gholamreza ;
Ghadimi, Noradin .
JOURNAL OF CLEANER PRODUCTION, 2019, 215 :878-889
[5]   A summary of demand response in electricity markets [J].
Albadi, M. H. ;
El-Saadany, E. F. .
ELECTRIC POWER SYSTEMS RESEARCH, 2008, 78 (11) :1989-1996
[6]  
[Anonymous], 2012, COMPLEMENTARITY MODE
[7]   Self-sufficient renewable energy supply in urban areas: Application to the city of Seville [J].
Arcos-Vargas, Angel ;
Gomez-Exposito, Antonio ;
Gutierrez-Garcia, Francisco .
SUSTAINABLE CITIES AND SOCIETY, 2019, 46
[8]   RETRACTED: Risk-assessment of photovoltaic-wind-battery-grid based large industrial consumer using information gap decision theory (Retracted article. See vol. 217, pg. 399, 2021) [J].
Bagal, Hamid Asadi ;
Soltanabad, Yashar Nonni ;
Dadjuo, Milad ;
Wakil, Karzan ;
Ghadimi, Noradin .
SOLAR ENERGY, 2018, 169 :343-352
[9]  
Barroso L.A., 2006, DECISION MAKING UNCE
[10]   Residential demand response program: Predictive analytics, virtual storage model and its optimization [J].
Basnet, Saurav M. S. ;
Aburub, Haneen ;
Jewell, Ward .
JOURNAL OF ENERGY STORAGE, 2019, 23 :183-194