A day-ahead sleeved power purchase agreement model for estimating the profit of wind farms in the Indian energy market

被引:7
作者
Das, Priti [1 ]
Malakar, Tanmoy [1 ]
机构
[1] Natl Inst Technol, Dept Elect Engn, Silchar, India
关键词
energy market; imbalance pricing; power purchase agreement; wind farms;
D O I
10.1002/2050-7038.12821
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The ever-increasing demand for green energy sources encourages wind farms to participate in the energy market through long-term contracts. This paper discusses the existing frameworks of the wind energy market and presents the strategies to integrate wind energy into the grid through the valuable power purchase agreement (PPA). Critical observations reveal that the opportunities of the short-term energy market remain unexplored for wind farms till date. To promote more investments and to enhance the operating economy, the wind energy sector must adapt to the sporadic nature of generation in the day-ahead energy market. The selection of accurate PPA for wind farms is a potent area of research and has encouraged the authors to explore this area vividly. In view of this, the main objective of the article is to develop a day-ahead sleeved PPA model and to evaluate its effectiveness in the Indian energy market. To correlate the proposed model with practicality aspects, seasonal wind speed scenarios are forecasted using the sARIMA model for a practical wind farm. The performance of the employed sARIMA model is evaluated through proper comparative assessment with other models. Thereafter to validate the importance of the developed PPA, a detailed comparative analysis is carried out with the existing long-term PPA. Further, the potentiality of the proposed sleeved PPA is compared with its other counterparts. Result analysis confirms the usefulness of the proposed PPA model in maximizing the economy of the renewable energy market entities.
引用
收藏
页数:30
相关论文
共 63 条
[1]   Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting [J].
Aasim ;
Singh, S. N. ;
Mohapatra, Abheejeet .
RENEWABLE ENERGY, 2019, 136 :758-768
[2]   Coordinated operation of electric vehicle charging and wind power generation as a virtual power plant: A multi-stage risk constrained approach [J].
Abbasi, Mohammad Hossein ;
Taki, Mehrdad ;
Rajabi, Amin ;
Li, Li ;
Zhang, Jiangfeng .
APPLIED ENERGY, 2019, 239 :1294-1307
[3]  
Abdusamad, 2018, WIND ENERGY RELIABIL, P734
[4]   Smart-grid investments, regulation and organization [J].
Agrell, Per J. ;
Bogetoft, Peter ;
Mikkers, Misja .
ENERGY POLICY, 2013, 52 :656-666
[5]   Large penetration of wind and dispersed generation into Danish power grid [J].
Akhmatov, Vladislav ;
Knudsen, Hans .
ELECTRIC POWER SYSTEMS RESEARCH, 2007, 77 (09) :1228-1238
[6]  
ALGHUSSAIN L, 2018, INT C PHOT SCI TECHN, P1
[7]   Hybrid multiscale wind speed forecasting based on variational mode decomposition [J].
Ali, Mumtaz ;
Khan, Asif ;
Rehman, Naveed Ur .
INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS, 2018, 28 (01)
[8]   Evaluating risk-constrained bidding strategies in adjustment spot markets for wind power producers [J].
Angeles Moreno, M. ;
Bueno, Miriam ;
Usaola, Julio .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2012, 43 (01) :703-711
[9]  
[Anonymous], 2019, SIKAR WEATHER FORECA
[10]  
[Anonymous], 2020, NATL GRID FREQUENCY