Open Access
Issue |
E3S Web Conf.
Volume 472, 2024
International Conference on Renewable Energy, Green Computing and Sustainable Development (ICREGCSD 2023)
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Article Number | 01010 | |
Number of page(s) | 13 | |
Section | Smart and Energy Efficient Systems | |
DOI | https://doi.org/10.1051/e3sconf/202447201010 | |
Published online | 05 January 2024 |
- Global wind energy council (2023) [Google Scholar]
- Ministry of New and Renewable energy-Government of India (2023). https://mnre.gov.in. [Google Scholar]
- T. Dai and W. Qiao, “Optimal bidding strategy of a strategic wind power producer in the short-term market,” IEEE Transactions on Sus- trainable Energy, Vol. 6, No. 3, 707–719, (2015). [CrossRef] [Google Scholar]
- L. Baringo and A. J. Conejo, “Offering strategy of wind-power producer: a multi-stage risk-constrained approach,” IEEE Transactions on Power Systems, vol. 31, No. 2, 1420–1429, (2016). [CrossRef] [Google Scholar]
- Patil, Ganesh Sampatrao, Anwar Mulla, Subhojit Dawn, and Taha Selim Ustun. “Profit Maximization with Imbalance Cost Improvement by Solar PV-Battery Hybrid System in Deregulated Power Market.” Energies 15, No. 14, 5290, (2022). [CrossRef] [Google Scholar]
- Shamsi, Mahdieh, and Paul Cuffe. “A prediction market trading strategy to hedge financial risks of wind power producers in electricity markets.” IEEE Transactions on Power Systems 36, no. 5, 4513–4523, (2021). [CrossRef] [Google Scholar]
- Mahmoudi, N., Saha, T.K., Eghbal, M.: “Wind power offering strategy in day-ahead markets: employing demand response in a two-stage plan”, IEEE Trans. Power Syst, 30 (4), 1888–1896, (2015). [CrossRef] [Google Scholar]
- Kazempour, S.J., Zareipour, H.: “Equilibria in an oligopolistic market with wind power production”, IEEE Trans. Power Syst, 29 (2), pp. 686–697, (2014). [CrossRef] [Google Scholar]
- Afshari Igder, Mosayeb, Taher Niknam, and Mohammad-Hassan Khooban. “Bidding strategies of the joint wind, hydro, and pumped- storage in generation company using novel improved clonal selection optimization algorithm.” IET Science, Measurement & Technology 11, no. 8, 991–1001, (2017). [CrossRef] [Google Scholar]
- Ding H., Pinson P., Hu Z., Song Y. “Integrated bidding and operating strategies for windstorage systems”. IEEE Trans Sustain Energy 7 (1), 163–172, (2016). [CrossRef] [Google Scholar]
- Ding Z., Sarikprueck P., Lee W.-J., “Medium-term operation for an industrial customer considering demand-side management and risk management”. IEEE Trans Ind Appl 52 (2), 1127–1135, (2016). [Google Scholar]
- Wu H., Shahidehpour M., Alabdulwahab A., Abusorrah A., “A game theoretic approach to risk-based optimal bidding strategies for electric vehicle aggregators in electricity markets with variable wind energy resources”. IEEE Trans Sustain Energy 7 (1), 374–385, (2016). [CrossRef] [Google Scholar]
- Wu J., Zhang B., Wang K., Shao J., Yao J., Zeng D., Ge T. “Optimal economic dispatch model based on risk management for wind- integrated power system”. IET Gen Transm Distrib 9 (15), 2152–2158, (2015). [CrossRef] [Google Scholar]
- Song M., Amelin M., “Purchase bidding strategy for a retailer with flexible demands in day-ahead electricity market”. IEEE Trans Power System 32 (3), 1839–1850, (2017). [CrossRef] [Google Scholar]
- Tian M.W., Yan S.R., Tian X.X., Nojavan S., Jermsittiparsert K., “Risk and profit-based bidding and offering strategies for pumped hydro storage in the energy market”. J Clean Prod, 256, 1–10, (2020). [Google Scholar]
- Zhang, Qiwei, and Fangxing Li. “From systematic risk to systemic risk: Analysis over day-ahead market operation under high renewable penetration by CoVaR and marginal CoVaR.” IEEE Transactions on Sustainable Energy 12, no. 2, 761–771, (2020). [Google Scholar]
- Chen Shujuan, Qin Jiang, Yuqing He, Ruanming Huang, Jiayong Li, Can Li, and Jing Liao. “A BP neural network-based hierarchical investment risk evaluation method considering the uncertainty and coupling for the power grid.” IEEE Access 8, 110279–110289, (2020). [CrossRef] [Google Scholar]
- Yang H., Zhang S., Qiu J., Lai M., Dong Z., “CVaR-constrained optimal bidding of electric vehicle aggregators in day-ahead and real-time markets”. IEEE Trans Industr Inf 13 (5), 2555–2565, (2017). [CrossRef] [Google Scholar]
- Ahmadian S., Vahidi B., Jahanipour J., Hoseinian S.H., Rastegar H., “Price restricted optimal bidding model using derated sensitivity factors by considering risk concept”. IET Gener Transm Distrib 10 (2), 310–324, (2016). [CrossRef] [Google Scholar]
- Mazzi N., Kazempour J., Pinson P., “Price-taker offering strategy in electricity pay-as-bid markets”. IEEE Trans Power Syst 33 (2), 2175–2183, (2018). [CrossRef] [Google Scholar]
- Panda R., Tiwari P.K., “Economic risk based bidding strategy for profit maximization of wind integrated day-ahead and real-time double auctioned competitive power markets”. IET Gener Transm Distrib 13 (2), 209–218, (2019). [CrossRef] [Google Scholar]
- Xue, Yusheng, Chen Yu, Kang Li, Fushuan Wen, Yi Ding, Qiuwei Wu, and Guangya Yang. “Adaptive ultra-short-term wind power prediction based on risk assessment.” CSEE Journal of Power and Energy Systems 2, no. 3, 59–64, (2016). [CrossRef] [Google Scholar]
- Xu, Zhiwei, Zechun Hu, Yonghua Song, and Jianhui Wang. “Risk- averse optimal bidding strategy for demand-side resource aggregators in day-ahead electricity markets under uncertainty.” IEEE Transactions on Smart Grid 8, no. 1, 96–105, (2015). [Google Scholar]
- Panda, Rajesh, and Prashant Kumar Tiwari. “An economic risk based optimal bidding strategy for various market players considering optimal wind placements in day-ahead and real-time competitive power market”. International Journal of System Assurance Engineering and Management 13, No. 1, 347–362, (2022). [Google Scholar]
- Baringo, Luis, and Antonio J. Conejo. “Offering strategy of wind- power producer: A multi-stage risk-constrained approach.” IEEE Transactions on Power Systems 31, no. 2, 1420–1429, (2015). [Google Scholar]
- Foti, Magda, and Manolis Vavalis. “Blockchain based uniform price double auctions for energy markets.” Applied Energy 254, 113604, (2019). [CrossRef] [Google Scholar]
- Zhang, Qiwei, and Fangxing Li. “Financial resilience and financial reliability for systemic risk assessment of electricity markets with high- penetration renewables.” IEEE Transactions on Power Systems 37, no. 3, 2312–2321, (2021). [Google Scholar]
- Wang, X., Wang, X., Huang, M., et al.: ‘A double auction method for resource management and bidding strategy on grid resources. Int. Conf Genetic and Evolutionary Computing, Shenzhen, China, 422–425, (2010) [Google Scholar]
- Zimmerman, R.D., Murillo-Sanchez, C.E., Gan, D.: ‘MATPOWER: a MATLAB power system simulation package’, 2006. Available at http://pserc.cornell.edu/matpower, accessed (2020). [Google Scholar]
- Wu, Hongyu, Mohammad Shahidehpour, Ahmed Alabdulwahab, and Abdullah Abusorrah. “A game theoretic approach to risk-based optimal bidding strategies for electric vehicle aggregators in electricity markets with variable wind energy resources.” IEEE Transactions on Sustainable Energy 7, no. 1, 374–385, (2016). [CrossRef] [Google Scholar]
- Dawn, Subhojit, Prashant Kumar Tiwari, Arup Kumar Goswami, and Rajesh Panda. “An approach for system risk assessment and mitigation by optimal operation of wind farm and FACTS devices in a centralized competitive power market.” IEEE Transactions on Sustainable Energy 10, No. 3, 1054–1065, (2018). [Google Scholar]
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