EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 139 (2017) MATEC Web Conf., 139 (2017) 00103 References
Open Access
Issue
MATEC Web Conf.
Volume 139, 2017
2017 3rd International Conference on Mechanical, Electronic and Information Technology Engineering (ICMITE 2017)
Article Number 00103
Number of page(s) 6
DOI https://doi.org/10.1051/matecconf/201713900103
Published online 05 December 2017
  1. Gans J, King S, Stonecash R, et al. Principles of economics[M]. Cengage Learning, 2011. [Google Scholar]
  2. Wen F, David A K. Bidding strategies in electricity markets[J]. Automation of Electric Power Systems, 2000, 24(14): 1-6. [Google Scholar]
  3. Wang Shuai. A review of bidding strategies for generation companies[J]. Moderm Economic Information, 2009 (23): 107-110. [Google Scholar]
  4. Wang Xifan, Wang Xiuli, Chen Haoyong. Power market theory[M]. Xi’an Jiaotong University Press, 2003. [Google Scholar]
  5. Li Tao, Jiang Chuanwen, Hou Zhijian. A cost offer model on the generation side of power market[J]. Information Journal Hydroelectric Energy, 2001, 19(3): 62-63. [Google Scholar]
  6. Zhang Shaohua, Li Yuzeng. Probabilistic forecasting of short-run marginal cost of power generation under electricity market[J]. Proceedings of the CSEE, 2000, 20(10): 19-22. [Google Scholar]
  7. Nogales F J, Contreras J, Conejo A J, et al. Forecasting next-day electricity prices by time series models[J]. IEEE Transactions on power systems, 2002, 17(2): 342-348. [Google Scholar]
  8. Huang Rixing, Kang Chongqing, Xia Qing. System marginal price forecasting in electricity market[J]. Automation of Electric Power Systems, 2000, 24(22): 9-12. [Google Scholar]
  9. Li Caihua, Wang Zhiwei. Short-term marginal price forecasting with hybrid model[J]. Automation of Electric Power Systems, 2000, 24(22): 9-12. [Google Scholar]
  10. Liu Zhengguo, Gao Fuying. Forecasting market-clearing price in Zhejiang generation market using neural network[J]. Automation of Electric Power Systems, 2002, 26(9): 49-52. [Google Scholar]
  11. Yang Li, Qiu Jiaju, Jiang Daozhuo. BPN based day-ahead unconstrained market clearing price forecasting model[J]. Automation of Electric Power Systems, 2001, 25(19): 11-14. [Google Scholar]
  12. Song chao. Application of wavelet analysis and neural network in electricity price forecasting[D]. Zhejiang University, 2002. [Google Scholar]
  13. Conejo A J, Nogales F J, Arroyo J M. Price-taker bidding strategy under price uncertainty[J]. IEEE Transactions on Power Systems, 2002, 17(4): 1081-1088. [CrossRef] [Google Scholar]
  14. Ni E, Luh P B, Rourke S. Optimal integrated generation bidding and scheduling with risk management under a deregulated power market[J]. IEEE Transactions on Power Systems, 2004, 19(1): 600-609. [Google Scholar]
  15. Bialek W, Callan C G, Strong S P. Field theories for learning probability distributions[J]. Physical review letters, 1996, 77(23): 4693. [Google Scholar]
  16. Zhang D, Wang Y, Luh P B. Optimization based bidding strategies in the deregulated market[C]//Power Industry Computer Applications, 1999. PICA’99. Proceedings of the 21st 1999 IEEE International Conference. IEEE, 1999: 63-68. [Google Scholar]
  17. Song H, Liu C C, Lawarree J. Decision making of an electricity supplier’s bid in a spot market[C]//Power Engineering Society Summer Meeting, 1999. IEEE. IEEE, 1999, 2: 692-696. [CrossRef] [Google Scholar]
  18. David A K. Competitive bidding in electricity supply[C]//IEE Proceedings C-Generation, Transmission and Distribution. IET, 1993, 140(5): 421-426. [CrossRef] [Google Scholar]
  19. Wen F, David A K. Optimal bidding strategies and modeling of imperfect information among competitive generators[J]. IEEE transactions on power systems, 2001, 16(1): 15-21. [Google Scholar]
  20. Wen F, David A K. Coordination of bidding strategies in day-ahead energy and spinning reserve markets[J]. International Journal of Electrical Power & Energy Systems, 2002, 24(4): 251-261. [CrossRef] [Google Scholar]
  21. Ma Li, Wen Fushuan. A preliminary investigation on bidding strategies employing step-wise bidding rules[J]. Automation of Electric Power Systems, 2002, 26(9): 16-19. [Google Scholar]
  22. Widjaja M, Sugianto L F, Morrison R E. Fuzzy model of generator bidding system in competitive electricity markets[C]//Fuzzy Systems, 2001. The 10th IEEE International Conference on. IEEE, 2001, 3: 1396-1399. [Google Scholar]
  23. Lamont J W, Rajan S. Strategic bidding in an energy brokerage[J]. IEEE Transactions on Power Systems, 1997, 12(4): 1729-1733. [CrossRef] [Google Scholar]
  24. Yang Li, Wen Fushuan. A possibility theory based approach for building optimal bidding strategies in electricity markets[J]. Automation of Electric Power Systems, 2002, 26(23): 12-17. [Google Scholar]
  25. Wen F, David A K. Strategic bidding in reserve market[C]//Advances in Power System Control, Operation and Management, 2000. APSCOM-00. 2000 International Conference on. IET, 2000, 1: 80-85. [Google Scholar]
  26. Wen F, David A K. Optimal bidding strategies for competitive generators and large consumers[J]. International Journal of Electrical Power & Energy Systems, 2001, 23(1): 37-43. [CrossRef] [Google Scholar]
  27. Hao S. A study of basic bidding strategy in clearing pricing auctions[C]//Power Industry Computer Applications, 1999. PICA’99. Proceedings of the 21st 1999 IEEE International Conference. IEEE, 1999: 55-60. [Google Scholar]
  28. Diao Qinhua, Lin Jikeng, NI Yixin, et al. Game theory and its applications in power markets[J]. Automation of Electric Power Systems, 2001, 25(1): 19-23. [Google Scholar]
  29. Wu Zhiyong, Kang Chongqing, Xia Qing, et al. Strategic bidding with application of game theory[J]. Automation of Electric Power Systems, 2002, 26(9): 7-11. [Google Scholar]
  30. Contreras J, Candiles O, de la Fuente J I, et al. A cobweb bidding model for competitive electricity markets[J]. IEEE Transactions on Power Systems, 2002, 17(1): 148-153. [CrossRef] [Google Scholar]
  31. Song Yiqun, Ni Yixin, Hou Zhijian, et al. A novel model of Gencos strategic behaviors based on conjectured supply function equilibrium[J]. Automation of Electric Power Systems, 2003, 27(13): 15-18. [Google Scholar]
  32. Chattopadhyay D. Multicommodity spatial Cournot model for generator bidding analysis[J]. IEEE Transactions on power systems, 2004, 19(1): 267-275. [CrossRef] [Google Scholar]
  33. Xian W, Yuzeng L, Shaohua Z. Oligopolistic equilibrium analysis for electricity markets: a nonlinear complementarity approach[J]. IEEE Transactions on Power Systems, 2004, 19(3): 1348-1355. [Google Scholar]
  34. Xie Shiyu. Evolutionary game theory under bounded rationality[J]. Journal of Shanghai University of Finance and Economics, 2001, 3(5): 3-9. [Google Scholar]
  35. Gao Jie, Sheng Zhaohan. Elementary groping for evolutionary game theory and its application in electricity market[J]. Automation of Electric Power Systems, 2003, 27(18): 18-21. [Google Scholar]
  36. Fujii Y, Okamura T, Inagaki K, et al. Basic analysis of the pricing processes in modeled electricity markets with multi-agent simulation[C]//Electric Utility Deregulation, Restructuring and Power Technologies, 2004. (DRPT 2004). Proceedings of the 2004 IEEE International Conference on. IEEE, 2004, 1: 257-261. [Google Scholar]
  37. Xiong G, Hashiyama T, Okuma S. An electricity supplier bidding strategy through Q-Learning[C]//Power Engineering Society Summer Meeting, 2002 IEEE. IEEE, 2002, 3: 1516-1521. [Google Scholar]
  38. Wang Yeping, Yang Yan, Jing Zhaoxia, et al. Application of intelligent Agent-based simulation to electricity market[J]. Journal of South China University of Technology(Natural Science Edition), 2010, 38(3). [Google Scholar]
  39. Chen Zhixu, Zhang Lizi, Shu Jun, et al. Bidding strategy research for price-taker in electricity market[J]. Electric Power, 2007, 40(6): 1-5. [Google Scholar]
  40. He Li. Research on Bidding Problems for Hydropower Plant in Electricity Market[D]. Huazhong University of Science & Technology, 2007 [Google Scholar]
  41. Shi Xiaojun. Bidding Strategy Research for Hydropower Plant in Electric Power Market[D]. Xi’an University of Technology, 2005. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.