Wind power bidding strategy by harmonizing neural network and genetic algorithm

References

• Amjady, N., A. A. Rashidi, and H. Zareipour. 2013. Stochastic security‐constrained joint market clearing for energy and reserves auctions considering uncertainties of wind power producers and unreliable equipment. International Transactions on Electrical Energy Systems 23 (4):451–72. doi:10.1002/etep.670.
   Web of Science ®Google Scholar
• Angarita, J. L., J. Usaola, and J. Martínez-Crespo. 2009. Combined hydro-wind generation bids in a pool-based electricity market. Electric Power Systems Research 79 (7):1038–46. doi:10.1016/j.epsr.2009.01.002.
   Web of Science ®Google Scholar
• Anzanello, M. J., and F. S. Fogliatto. 2011. Learning curve models and applications: Literature review and research directions. International Journal of Industrial Ergonomics 41 (5):573–83. doi:10.1016/j.ergon.2011.05.001.
   Web of Science ®Google Scholar
• Bathurst, G. N., Weatherill, J., & Strbac, G. (2002). Trading wind generation in short term energy markets. IEEE Transactions on Power Systems 17(3):782–789. doi:10.1109/TPWRS.2002.800950.
   Web of Science ®Google Scholar
• Botterud, A., J. Wang, R. J. Bessa, H. Keko, and V. Miranda Risk management and optimal bidding for a wind power producer. Proceedings of the IEEE PES General Meeting, Providence, RI, USA, July 2010, 8–10.
   Google Scholar
• Chang, G. W., H. J. Lu, P. K. Wang, Y. R. Chang, and Y. D. Lee. 2017. Gaussian mixture model‐based neural network for short‐term wind power forecast. International Transactions on Electrical Energy Systems 27 (6):e2320. doi:10.1002/etep.2320.
   Web of Science ®Google Scholar
• Dai, T., and W. Qiao. 2013. Trading wind power in a competitive electricity market using stochastic programing and game theory. IEEE Transactions on Sustainable Energy 4 (3):805–15. doi:10.1109/TSTE.2013.2251917.
   Web of Science ®Google Scholar
• De, G., Z. Tan, M. Li, L. Huang, and X. Song. 2018. Two-Stage Stochastic Optimization for the Strategic Bidding of a Generation Company Considering Wind Power Uncertainty. Energies 11 (12):3527. doi:10.3390/en11123527.
   Web of Science ®Google Scholar
• Electric Power Statistics Information System, Available online: http://epsis.kpx.or.kr/epsisnew/selectEkmaRegUpsGrid.do?menuId=050804 ( accessed on 9 March 2020)
   Google Scholar
• Fang, X., and M. Cui. 2020. Analytical Model of Day-ahead and Real-time Price Correlation in Strategic Wind Power Offering. Journal of Modern Power Systems and Clean Energy 8 (5):1024–27. doi:10.35833/MPCE.2019.000598.
   Web of Science ®Google Scholar
• Federal Energy Regulatory Commission (FERC). 2007. Preventing Undue Discrimination and Preference in Transmission Service. Docket Nos . 12265–531 RM05–17-001, 002 and RM05–25-001, 002; Order No. 890-A.
   Google Scholar
• Federal Energy Regulatory Commission (FREC). Settlement Intervals and Shortage Pricing in Markets Operated by Regional Transmission Organizations and Independent System Operators. Docket No. RM15–24000; Order No. 825. 2016.
   Google Scholar
• Ha, C., H. Seok, and C. Ok. 2018. Evaluation of forecasting methods in aggregate production planning: A Cumulative Absolute Forecast Error (CAFE). Computers & Industrial Engineering 118:329–39. doi:10.1016/j.cie.2018.03.003.
   Web of Science ®Google Scholar
• Islam, B. U. 2011. Comparison of conventional and modern load forecasting techniques based on artificial intelligence and expert systems. International Journal of Computer Science Issues (IJCSI) 8 (5):504.
   Google Scholar
• Jiang, Y., M. Chen, and S. You. 2017. A unified trading model based on robust optimization for day-ahead and real-time markets with wind power integration. Energies 10 (4):554. doi:10.3390/en10040554.
   Web of Science ®Google Scholar
• Kuo, C., and A. Reitsch. 1995. Neural networks vs. conventional methods of forecasting. The Journal of Business Forecasting 14 (4):17.
   Google Scholar
• Lei, M., L. Shiyan, J. Chuanwen, L. Hongling, and Z. Yan. 2009. A review on the forecasting of wind speed and generated power. Renewable and Sustainable Energy Reviews 13 (4):915–20. doi:10.1016/j.rser.2008.02.002.
   Web of Science ®Google Scholar
• Li, G., J. Shi, and X. Qu. 2011. Modeling methods for GenCo bidding strategy optimization in the liberalized electricity spot market–A state-of-the-art review. Energy 36 (8):4686–700. doi:10.1016/j.energy.2011.06.015.
   Web of Science ®Google Scholar
• Matevosyan, J., and L. Soder. 2006. Minimization of imbalance cost trading wind power on the short-term power market. IEEE Transactions on Power Systems 21 (3):1396–404. doi:10.1109/TPWRS.2006.879276.
   Web of Science ®Google Scholar
• Morales, J. M., A. J. Conejo, and J. Perez-Ruiz. 2010. Short-term trading for a wind power producer. IEEE Transactions on Power Systems 25 (1):554–64. doi:10.1109/TPWRS.2009.2036810.
   Web of Science ®Google Scholar
• Pandžić, H., J. M. Morales, A. J. Conejo, and I. Kuzle. 2013. Offering model for a virtual power plant based on stochastic programming. Applied Energy 105:282–92. doi:10.1016/j.apenergy.2012.12.077.
   Web of Science ®Google Scholar
• Piotrowski, P., D. Baczyński, M. Kopyt, K. Szafranek, P. Helt, and T. Gulczyński. 2019. Analysis of forecasted meteorological data (NWP) for efficient spatial forecasting of wind power generation. Electric Power Systems Research 175:105891. doi:10.1016/j.epsr.2019.105891.
   Web of Science ®Google Scholar
• Rahimiyan, M., J. M. Morales, and A. J. Conejo. 2011. Evaluating alternative offering strategies for wind producers in a pool. Applied Energy 88 (12):4918–26. doi:10.1016/j.apenergy.2011.06.038.
   Web of Science ®Google Scholar
• Rayati, M., H. Goodarzi, and A. Ranjbar. 2019. Optimal bidding strategy of coordinated wind power and gas turbine units in real‐time market using conditional value at risk. International Transactions on Electrical Energy Systems 29 (1):e2645. doi:10.1002/etep.2645.
   Web of Science ®Google Scholar
• Sharma, K. C., R. Bhakar, and H. P. Tiwari. 2014. Strategic bidding for wind power producers in electricity markets. Energy Conversion and Management 86:259–67. doi:10.1016/j.enconman.2014.05.002.
   Web of Science ®Google Scholar
• Sharma, S. K., and S. Ghosh. 2016. Short-term wind speed forecasting: Application of linear and non-linear time series models. International Journal of Green Energy 13 (14):1490–500. doi:10.1080/15435075.2016.1212200.
   Web of Science ®Google Scholar
• Shi, J., X. Qu, and S. Zeng. 2011. Short-term wind power generation forecasting: Direct versus indirect ARIMA-based approaches. International Journal of Green Energy 8 (1):100–12. doi:10.1080/15435075.2011.546755.
   Web of Science ®Google Scholar
• The European Wind Energy Association (EWEA). Balancing responsibility and costs of wind power plants. 2015.
   Google Scholar
• Vahidinasab, V., and S. Jadid. 2010. Normal boundary intersection method for suppliers’ strategic bidding in electricity markets: An environmental/economic approach. Energy Conversion and Management 51 (6):1111–19. doi:10.1016/j.enconman.2009.12.019.
   Web of Science ®Google Scholar
• Vom Scheidt, F., H. Medinová, N. Ludwig, B. Richter, P. Staudt, and C. Weinhardt. 2020. Data analytics in the electricity sector–A quantitative and qualitative literature review. Energy and AI 1:100009. doi:10.1016/j.egyai.2020.100009.
   Google Scholar
• XX, 2000 https://www.koenergy.kr/kosep/en/main.do
   Google Scholar
• Xy, 2000 https://www.mathworks.com/help/matlab/referencelist.html?type=function
   Google Scholar
• Zhang, H., F. Gao, J. Wu, K. Liu, and X. Liu. 2012. Optimal bidding strategies for wind power producers in the day-ahead electricity market. Energies 5 (11):4804–23. doi:10.3390/en5114804.
   Web of Science ®Google Scholar