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GHG-mitigation oriented and coal-consumption constrained inexact robust model for regional energy structure adjustment – A case study for Jiangsu Province, China

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  • Ji, Ling
  • Zhang, Bei-Bei
  • Huang, Guo-He
  • Xie, Yu-Lei
  • Niu, Dong-Xiao

Abstract

Resources and environmental crises are involved in energy management system, confronting a larger amount of coal consumption and GHG emission during structure adjustment plan making in China. In this study, a GHG-mitigation oriented and coal-consumption constrained inexact robust energy system management model is developed for adjusting regional power structure and analyzing the effects of different policy instruments on the performance of power system. The proposed model is a hybrid methodology of interval two-stage stochastic programming and stochastic robust programming. It can handle uncertainties presented as discrete intervals and probability distributions, and reflect the tradeoff between system costs and the tractability under different policy scenarios. The proposed model was applied to a case study of Jiangsu Province, a developed region within the total coal consumption control pilot area of China. The results revealed that ambitious carbon emission and coal consumption reduction targets would promote the utilization and development of renewable energy. Stricter carbon emission caps were shown to be more efficient than coal consumption cap policies for encouraging investment in renewable energy generation, especially wind power. If over 15% carbon emission reduction target was carried out, the recent coal consumption control policy would have little impact on the electricity system development.

Suggested Citation

  • Ji, Ling & Zhang, Bei-Bei & Huang, Guo-He & Xie, Yu-Lei & Niu, Dong-Xiao, 2018. "GHG-mitigation oriented and coal-consumption constrained inexact robust model for regional energy structure adjustment – A case study for Jiangsu Province, China," Renewable Energy, Elsevier, vol. 123(C), pages 549-562.
    • Handle: RePEc:eee:renene:v:123:y:2018:i:c:p:549-562
    DOI: 10.1016/j.renene.2018.02.059
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    2. Menglu Li & Wei Wang & Gejirifu De & Xionghua Ji & Zhongfu Tan, 2018. "Forecasting Carbon Emissions Related to Energy Consumption in Beijing-Tianjin-Hebei Region Based on Grey Prediction Theory and Extreme Learning Machine Optimized by Support Vector Machine Algorithm," Energies, MDPI, vol. 11(9), pages 1-15, September.
    3. Ji, Ling & Zhang, Beibei & Huang, Guohe & Wang, Peng, 2020. "A novel multi-stage fuzzy stochastic programming for electricity system structure optimization and planning with energy-water nexus - A case study of Tianjin, China," Energy, Elsevier, vol. 190(C).
    4. Xu, Xiao & Hu, Weihao & Cao, Di & Huang, Qi & Liu, Zhou & Liu, Wen & Chen, Zhe & Blaabjerg, Frede, 2020. "Scheduling of wind-battery hybrid system in the electricity market using distributionally robust optimization," Renewable Energy, Elsevier, vol. 156(C), pages 47-56.

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