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The evolution model of electricity market on the stable development in China and its dynamic analysis

Author

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  • Zhang, Wenbin
  • Tian, Lixin
  • Wang, Minggang
  • Zhen, Zaili
  • Fang, Guochang

Abstract

According to the causal relationships between electricity consumption, electricity supply and electricity price, the complex interrelationships among all the factors in electricity market are clarified, and a net structure that every element can conduct mutually are founded. Furthermore, a novel dynamic system model of the electricity supply-consumption-price is proposed. By means of Lyapunov exponents and bifurcation diagrams, the dynamic behaviors of the system are analyzed, and an electricity market attractor is achieved. Then the model is applied to China's electricity market. With the statistical data of China between 1997 and 2012, the parameters for establishing a China-specific dynamic system of electricity market are identified by artificial neural network of machine learning. Owing to the actual system in China's electricity market is unstable, four efficient single regulatory strategies have been found by means of bifurcation diagrams. And comparative scenario analysis is employed based on the influences to electricity market and electricity balance index under all kinds of regulatory strength and regulatory strategies. The results show that the current actual situation in China's electricity market should be considered, and excessive regulation should be avoided. At the same time, it is found that the integrated regulatory strategies are better than the single regulatory strategies, and the optimal regulatory strategy of the integrated regulatory strategies is strategy-2,3,4. That is, to reform the regulation of China's current electricity market, the best way is to deepen market-oriented reforms in the electricity generation side, implementing peak-valley TOU price and adopting appropriate administrative measures.

Suggested Citation

  • Zhang, Wenbin & Tian, Lixin & Wang, Minggang & Zhen, Zaili & Fang, Guochang, 2016. "The evolution model of electricity market on the stable development in China and its dynamic analysis," Energy, Elsevier, vol. 114(C), pages 344-359.
    • Handle: RePEc:eee:energy:v:114:y:2016:i:c:p:344-359
    DOI: 10.1016/j.energy.2016.08.015
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    as
    1. Lai, T.M. & To, W.M. & Lo, W.C. & Choy, Y.S., 2008. "Modeling of electricity consumption in the Asian gaming and tourism center—Macao SAR, People's Republic of China," Energy, Elsevier, vol. 33(5), pages 679-688.
    2. Sun, Mei & Wang, Xiaofang & Chen, Ying & Tian, Lixin, 2011. "Energy resources demand-supply system analysis and empirical research based on non-linear approach," Energy, Elsevier, vol. 36(9), pages 5460-5465.
    3. Amarawickrama, Himanshu A. & Hunt, Lester C., 2008. "Electricity demand for Sri Lanka: A time series analysis," Energy, Elsevier, vol. 33(5), pages 724-739.
    4. Abdel-Aal, R.E. & Al-Garni, A.Z. & Al-Nassar, Y.N., 1997. "Modelling and forecasting monthly electric energy consumption in eastern Saudi Arabia using abductive networks," Energy, Elsevier, vol. 22(9), pages 911-921.
    5. Sari, Ramazan & Soytas, Ugur, 2004. "Disaggregate energy consumption, employment and income in Turkey," Energy Economics, Elsevier, vol. 26(3), pages 335-344, May.
    6. Tian, Lixin & Jin, Rulei, 2012. "Theoretical exploration of carbon emissions dynamic evolutionary system and evolutionary scenario analysis," Energy, Elsevier, vol. 40(1), pages 376-386.
    7. Alagoz, B. Baykant & Kaygusuz, Asim & Akcin, Murat & Alagoz, Serkan, 2013. "A closed-loop energy price controlling method for real-time energy balancing in a smart grid energy market," Energy, Elsevier, vol. 59(C), pages 95-104.
    8. Deb, Rajat & Albert, Richard & Hsue, Lie-Long & Brown, Nicholas, 2000. "How to Incorporate Volatility and Risk in Electricity Price Forecasting," The Electricity Journal, Elsevier, vol. 13(4), pages 65-75, May.
    9. Egelioglu, F. & Mohamad, A.A. & Guven, H., 2001. "Economic variables and electricity consumption in Northern Cyprus," Energy, Elsevier, vol. 26(4), pages 355-362.
    10. He, Kaijian & Yu, Lean & Tang, Ling, 2015. "Electricity price forecasting with a BED (Bivariate EMD Denoising) methodology," Energy, Elsevier, vol. 91(C), pages 601-609.
    11. Ghasemi, A. & Shayeghi, H. & Moradzadeh, M. & Nooshyar, M., 2016. "A novel hybrid algorithm for electricity price and load forecasting in smart grids with demand-side management," Applied Energy, Elsevier, vol. 177(C), pages 40-59.
    12. Fang, Guochang & Tian, Lixin & Sun, Mei & Fu, Min, 2012. "Analysis and application of a novel three-dimensional energy-saving and emission-reduction dynamic evolution system," Energy, Elsevier, vol. 40(1), pages 291-299.
    13. Ranjan, Manish & Jain, V.K., 1999. "Modelling of electrical energy consumption in Delhi," Energy, Elsevier, vol. 24(4), pages 351-361.
    14. Mohamed, Zaid & Bodger, Pat, 2005. "Forecasting electricity consumption in New Zealand using economic and demographic variables," Energy, Elsevier, vol. 30(10), pages 1833-1843.
    15. Liu, Heping & Shi, Jing, 2013. "Applying ARMA–GARCH approaches to forecasting short-term electricity prices," Energy Economics, Elsevier, vol. 37(C), pages 152-166.
    16. Yee Yan, Yuk, 1998. "Climate and residential electricity consumption in Hong Kong," Energy, Elsevier, vol. 23(1), pages 17-20.
    17. Lin, Boqiang & Liu, Xia, 2013. "Electricity tariff reform and rebound effect of residential electricity consumption in China," Energy, Elsevier, vol. 59(C), pages 240-247.
    18. Yu, Mengmeng & Hong, Seung Ho, 2016. "Supply–demand balancing for power management in smart grid: A Stackelberg game approach," Applied Energy, Elsevier, vol. 164(C), pages 702-710.
    19. Ventosa, Mariano & Baillo, Alvaro & Ramos, Andres & Rivier, Michel, 2005. "Electricity market modeling trends," Energy Policy, Elsevier, vol. 33(7), pages 897-913, May.
    20. Pappas, S.Sp. & Ekonomou, L. & Karamousantas, D.Ch. & Chatzarakis, G.E. & Katsikas, S.K. & Liatsis, P., 2008. "Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models," Energy, Elsevier, vol. 33(9), pages 1353-1360.
    21. Saab, Samer & Badr, Elie & Nasr, George, 2001. "Univariate modeling and forecasting of energy consumption: the case of electricity in Lebanon," Energy, Elsevier, vol. 26(1), pages 1-14.
    22. Pao, Hsiao-Tien, 2009. "Forecast of electricity consumption and economic growth in Taiwan by state space modeling," Energy, Elsevier, vol. 34(11), pages 1779-1791.
    23. Abdel-Aal, R.E. & Al-Garni, A.Z., 1997. "Forecasting monthly electric energy consumption in eastern Saudi Arabia using univariate time-series analysis," Energy, Elsevier, vol. 22(11), pages 1059-1069.
    24. Kwon, Sanguk & Cho, Seong-Hoon & Roberts, Roland K. & Kim, Hyun Jae & Park, Kihyun & Edward Yu, T., 2016. "Effects of electricity-price policy on electricity demand and manufacturing output," Energy, Elsevier, vol. 102(C), pages 324-334.
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    4. Singh, Nitin & Mohanty, Soumya Ranjan & Dev Shukla, Rishabh, 2017. "Short term electricity price forecast based on environmentally adapted generalized neuron," Energy, Elsevier, vol. 125(C), pages 127-139.

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