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Environmental Science and Pollution Research

, Volume 25, Issue 18, pp 17540–17552 | Cite as

A system dynamics model of China’s electric power structure adjustment with constraints of PM10 emission reduction

  • Xiaopeng Guo
  • Dongfang Ren
  • Xiaodan Guo
Research Article
  • 80 Downloads

Abstract

Recently, Chinese state environmental protection administration has brought out several PM10 reduction policies to control the coal consumption strictly and promote the adjustment of power structure. Under this new policy environment, a suitable analysis method is required to simulate the upcoming major shift of China’s electric power structure. Firstly, a complete system dynamics model is built to simulate China’s evolution path of power structure with constraints of PM10 reduction considering both technical and economical factors. Secondly, scenario analyses are conducted under different clean-power capacity growth rates to seek applicable policy guidance for PM10 reduction. The results suggest the following conclusions. (1) The proportion of thermal power installed capacity will decrease to 67% in 2018 with a dropping speed, and there will be an accelerated decline in 2023–2032. (2) The system dynamics model can effectively simulate the implementation of the policy, for example, the proportion of coal consumption in the forecast model is 63.3% (the accuracy rate is 95.2%), below policy target 65% in 2017. (3) China should promote clean power generation such as nuclear power to meet PM10 reduction target.

Keywords

The adjustment of electric power structure PM10 System dynamics Air-pollution reduction policy 

Notes

Acknowledgements

Project supported by the National Social Science Fund of China (17BGL136).

References

  1. An QX, Wen Y, Xiong BB, Yang M, Chen XH (2016) Allocation of carbon dioxide emission permits with minimum cost for Chinese province in big data environment. J Clean Prod 113(15):1125–1135Google Scholar
  2. Betulozer EG, Selahattin I (2013) The scenario analysis on CO2 emission mitigation potential in the Turkish electricity sector: 2006–2030. Energy 49(1):395–403Google Scholar
  3. Cai W, Wang C, Zhang Y, Chen J (2007) Scenario analysis on CO2 emissions reduction potential in China’s electricity sector. Energy Policy 35(12):6356–6445Google Scholar
  4. Claudio C, Enrico P, Marialuisa V (2010) A non-linear analysis to detect the origin of PM10 concentrations in Northern Italy. Sci Total Environ 409(1):182–191Google Scholar
  5. Du LL, Li XZ et al (2018) System dynamic modeling of urban carbon emissions based on the regional National Economy and Social Development Plan: a case study of Shanghai city. J Clean Prod 172:1501–1513Google Scholar
  6. Duro JA, Padilla E (2014) International inequalities in per capita CO2 emission: a decomposition methodology by Kaya factors. Energy Econ 28(2):170–187Google Scholar
  7. Fang G, Tian L, Fu M (2017) The effect of energy construction adjustment on the dynamical evolution of energy-saving and emission-reduction system in China. Appl Energy 196:180–189Google Scholar
  8. Feng Y, Chen S, Zhang L (2013) System dynamics modeling for urban energy consumption and CO2 emissions: a case study of Beijing, China. Ecol Model 252(SI):44–52Google Scholar
  9. Grcia E, Mohanty A, Lin W, Cherry S (2013) Dynamic analysis of hybrid energy systems under flexible operation and variable renewable generation—part II: dynamic cost analysis. Energy 52(1):17–26Google Scholar
  10. Guo X, Wei YN (2016) Will the steam coal price rebound under the new economy normalcy in China? Energies 9(9):751Google Scholar
  11. Guo X, Guo X, Yuan J (2015) Impact analysis of air pollutant emission policies on thermal coal supply chain enterprises in China. Sustainability 7(1):75–95Google Scholar
  12. John RS (1997) A U.K wide episode of elevated particle (PM10) concentration in March 1996. Atmos Environ 31(15):2381–2383Google Scholar
  13. John RS (2002) The use of receptor modelling and emission inventory data to explain the downward trend in UK PM10 concentrations. Atmos Environ 36(25):4089–4101Google Scholar
  14. John RS, Emma L, Beth C (2001) Receptor modelling of PM10 concentrations at a United Kingdom national network monitoring site in Central London. Atmos Environ 35(2):297–304Google Scholar
  15. Li L, Sun Z (2013) Dynamic energy control for energy efficiency improvement of sustainable manufacturing systems using Markov decision process. Cybern Syst 43(5):1195–1205Google Scholar
  16. Li F, Dong S, Li Z, Li Y, Li S, Wang Y (2012) The improvement of CO2 emission reduction policies based on system dynamics method in traditional industrial region with large CO2 emission. Energy Policy 51(12):683–695Google Scholar
  17. Li W, Xiaoyu L, Guanzhong S (2014) An inexact two-stage dynamic stochastic model for regional electricity and heat supply management with pollutants mitigation control. Environ Syst Res 3:18Google Scholar
  18. Li K, Li JL, Wang WG, Tong S, Liggio J, Ge M (2017) Evaluating the effectiveness of joint emission control policies on the reduction of ambient VOCs: implications from observation during the 2014 APEC summit in suburban Beijing. Atmos Environ 164:117–127Google Scholar
  19. Liu L, Zong H, Zhao E, Chen C, Wang J (2014) Can China realize its carbon emission reduction goal in 2020: from the perspective of thermal power development. Appl Energy 124(1):199–212Google Scholar
  20. Mathews AJ, Tan H (2013) The transformation of the electric power sector in China. Energy Policy 52(1):170–180Google Scholar
  21. Nastaran A, Abbas S (2013) A system dynamics model for analyzing energy consumption and CO2 emission in Iranian cement industry under various production and export scenarios. Energy Policy 58(7):75–89Google Scholar
  22. Peggy M, Kenneth BK (2014) Modelling tools to evaluate China’s future energy system—a review of the Chinese perspective. Energy 69(5):132–143Google Scholar
  23. Relvas H, Miranda AI, Carnevale C, Maffeis G, Turrini E, Volta M (2017a) Optimal air quality policies and health: a multi-objective nonlinear approach. Environ Sci Pollut Res 24(15):13687–13699Google Scholar
  24. Helder Relvas, Ana Isabel Miranda, Claudio Carnevale, Giuseppe Maffeis, Enrico Turrini, Marialuisa Volta (2017b) Optimal air quality policies and health: a multi-objective nonlinear approach. Environ Sci Pollut Res 24(15): 13687–13699Google Scholar
  25. Salman A, Tahar RB (2013) Using system dynamics to evaluate renewable electricity development in Malaysia. Renew Electr Dev 43(1):24–39Google Scholar
  26. Saysel AK, Hekimogiu M (2013) Exploring the options for carbon dioxide mitigation in Turkish electric power industry: system dynamics approach. Energy Policy 60(9):675–686Google Scholar
  27. Sheikhifini A, Moghhadam MP, Sheikh-Ei-Eslami MK (2014) A dynamic model for distributed energy resource expansion planning considering multi-resource support schemes. Electr Power Energy Syst 60(9):357–366Google Scholar
  28. Song SK, Shon ZH (2014) Current and future emission estimates of exhaust gases and particles from shipping at the largest port in Korea. Environ Sci Pollut Res 21(10):6612–6622Google Scholar
  29. Song X, Zhang X , Long Y, Guo Y(2017) Study on the evolution mechanism and development forecasting of China’s power supply structure clean development. Sustainability 9(2):213Google Scholar
  30. Sui ZF, Zhang YS, Peng Y, Norris P (2016) Fine particulate matter emission and size distribution characteristics in an ultra-low emission power plant. Fuel 185:863–871Google Scholar
  31. Tan Z, Song Y, Zhang H, Shi Q, Xu J (2014) Joint optimization model of generation side and user side based on energy-saving policy. Electr Power Energy Syst 57(5):135–140Google Scholar
  32. Wang K, Wang C, Lu X, Chen J (2007) Scenario analysis on CO2 emissions reduction potential in China’s iron and steel industry. Energy Policy 35(4):2320–2335Google Scholar
  33. Wen Z, Li H (2014) Analysis of potential energy conservation and CO2 emissions reduction in China’s non-ferrous metals industry from a technology perspective. Int J Greenhouse Gas Control 28(9):45–56Google Scholar
  34. Xie YL, Xia DH, Ji L, Zhou WN, Huang GH (2017) An inexact cost-risk balanced model for regional energy structure adjustment management and resources environmental effect analysis-a case study of Shandong province, China. Energy 126:374–391Google Scholar
  35. Yan QY, Qin C, Nie MJ, Yang L (2018) Forecasting the electricity demand and market shares in retail electricity market based on system dynamics and Markov chain. Math Probl Eng 2018:1–11Google Scholar
  36. Yao Q, Li SQ, Xu HW, Zhuo JK, Song Q (2010) Reprint of: Studies on formation and control of combustion particulate matter in China: a review. Energy 35(11):4480–4493Google Scholar
  37. Zhang J, Liu B (2008) Energy saving and emission reduction and power coal washing. Coal Eng 3:21–22Google Scholar
  38. Zhang P, Du W, Jiao S, He L (2014) A decomposition model to analyze effect of SO2 emission density of China. J Cent South Univ 21(2):701–708Google Scholar
  39. Zhang YZ, Zhao XG, Zuo Y, Ren LZ, Wang L (2017) The development of the renewable energy power industry under feed-in tariff and renewable portfolio standard: a case study of China’s photovoltaic power industry. Sustainability 9(4):2071–1050Google Scholar
  40. Zhao Y, Wang SX, Nielsen CP, Li XH (2010) Establishment of a database of emission factors for atmospheric pollutants from Chinese coal-fired power plants. Atmos Environ 44(12):1515–1523Google Scholar
  41. Zhao Y, Nielsen CP, Lei Y, McElroy MB, Hao J (2011) Quantifying the uncertainties of a bottom-up emission inventory of anthropogenic atmospheric pollutants in China. Atmos Chem Phys 11(5):2295–2308Google Scholar
  42. Zhao X, Ma Q, Yang R (2013) Factors influencing CO2 emissions in China’s power industry: co-integration analysis. Energy Policy 57(6):89–98Google Scholar
  43. Zheng M, Zhang K, Dong J (2013) Overall review of China’s wind power industry: status quo, existing problems and perspective for future development. Renew Sust Energ Rev 24(8):379–386Google Scholar
  44. Zhou X, Guan XL, Zhang M, Zhang Y (2017) Allocation and simulation study of carbon emission quotas among China’s provinces in 2020. Environ Sci Pollut Res 24(6):7088–7113Google Scholar
  45. Zhu H, Huang G (2013) Dynamic stochastic fractional programming for sustainable management of electric power systems. Electr Power Energy Syst 53(12):553–563Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Economics and ManagementNorth China Electric Power UniversityBeijingChina
  2. 2.Beijing Key Laboratory of New Energy and Low-Carbon DevelopmentNorth China Electric Power UniversityBeijingChina
  3. 3.School of EnvironmentRenmin University of ChinaBeijingChina

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