World Environmental and Water Resources Congress 2018
Day-Ahead Electricity Market Clearing Price Forecasting: A Case in Yunnan
Publication: World Environmental and Water Resources Congress 2018: Watershed Management, Irrigation and Drainage, and Water Resources Planning and Management
ABSTRACT
The newly-reformed Yunnan electricity market has been operating successfully for three years with more and more abundant trade varieties and perfect trading mechanism. The vitality of the market is increasing year by year. Market participants are gradually exploring reasonable bidding strategies, and the accurate electricity price forecasting is a joint demand for all the market members. With comprehensive considerations of the social economic background, the structure of the power system, the market development state, and other factors in Yunnan, this paper analyzes various factors and rules that have effects on the electricity price in day-ahead market. Furthermore, a successful application of an auto regressive moving average (ARMA) model to electricity price forecasting (EPF) is presented. The case study based on real data of day-ahead market in Yunnan shows that the proposed model could reflect the characteristics of electricity price and the results prove this model’s effectivity.
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ACKNOWLEDGMENTS
This research was supported by “National Natural Science Foundation of China” (No. 91547201) and “The Fundamental Research Funds for the Central Universities” (Grant No. DUT16QY14).
REFERENCES
Chen, F., Liu, B., Cheng, C., & Mirchi, A. (2017). Simulation and Regulation of Market Operation in Hydro-Dominated Environment: The Yunnan Case. Water, 9(8), 623.
Szkuta, B. R., Sanabria, L. A., & Dillon, T. S. (1999). Electricity price short-term forecasting using artificial neural networks. Power Systems IEEE Transactions on, 14(3), 851–857.
Mandal, P., Senjyu, T., Urasaki, N., Funabashi, T., & Srivastava, A. K. (2007). A novel approach to forecast electricity price for pjm using neural network and similar days method. IEEE Transactions on Power Systems, 22(4), 2058–2065.
Cheng, C., Luo, B., Miao, S., & Wu, X. (2016). Mid-term electricity market clearing price forecasting with sparse data: a case in newly-reformed yunnan electricity market. Energies, 9(10), 804.
Miao, S., Luo, B., Shen, J., & Cheng, C. (2017). Optimal Operation of Cascade Hydropower Plants in Deregulated Electricity Market: A Case Study of Lancang River in China. World Environmental and Water Resources Congress (pp. 453–463).
Cheng, C., Yan, L., Mirchi, A., & Madani, K. (2016). China’s booming hydropower: systems modeling challenges and opportunities. Journal of Water Resources Planning & Management, 143(1), 02516002.
Contreras, J., Espinola, R., Nogales, F. J., & Conejo, A. J. (2003). Arima models to predict next-day electricity prices. IEEE Transactions on Power Systems, 18(3), 1014–1020.
Box, G. E. P., & Jenkins, G. M. (2010). Time series analysis: forecasting and control. Technometrics, 31(4), 238–242.
Yang, H. T., & Huang, C. M. (1998). A new short-term load forecasting approach using self-organizing fuzzy armax models. IEEE Transactions on Power Systems, 13(1), 217–225.
Nogales, F. J., Contreras, J., Conejo, A. J., & Espinola, R. (2007). Forecasting next-day electricity prices by time series models. IEEE Power Engineering Review, 22(3), 58–58.
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Published In
World Environmental and Water Resources Congress 2018: Watershed Management, Irrigation and Drainage, and Water Resources Planning and Management
Pages: 141 - 151
Editor: Sri Kamojjala, Las Vegas Valley Water District
ISBN (Online): 978-0-7844-8140-0
Copyright
© 2018 American Society of Civil Engineers.
History
Published online: May 31, 2018
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