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Analysis of emission taxes levying on regional electric power structure adjustment with an inexact optimization model - A case study of Zibo, China

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  • Guan, Panbo
  • Huang, Guohe
  • Wu, Chuanbao
  • Wang, Linrui
  • Li, Chaoci
  • Wang, Yuanyi

Abstract

In this study, an inexact two-stage chance-constrained programming (ITSCCP) model was provided for multiple electrical power system supply and demand management in Zibo City under uncertainties. Three scenarios about the electric power structure adjustment, renewable power generation, and the emission taxes were designed. Methods of two-stage stochastic programming (TSP) and inexact chance-constrained programming (ICCP) were incorporated into the developed model to tackle uncertainties in terms of various cost coefficients, decision maker's risk attitude which was described by interval values and probability distributions. Moreover, under the objective of cost minimize, the electrical power generation planning for each terms under different feasibility degrees (violating constraints or available resources situations) can be obtained. The results indicated that higher probability of violating system constraints would increase risk of system, but lower the total cost; the proportion of optimized thermal power generation and imported electricity would decrease, which could promote the energy conservation and emissions reduction in some degree. At the same time, the model results are valuable for decision-makers to tackle the uncertainty of the power generation schemes within a complicated energy system and make a desired compromise between the satisfaction degree of the economic benefits and feasibility degree of constraints.

Suggested Citation

  • Guan, Panbo & Huang, Guohe & Wu, Chuanbao & Wang, Linrui & Li, Chaoci & Wang, Yuanyi, 2019. "Analysis of emission taxes levying on regional electric power structure adjustment with an inexact optimization model - A case study of Zibo, China," Energy Economics, Elsevier, vol. 84(C).
    • Handle: RePEc:eee:eneeco:v:84:y:2019:i:c:s014098831930266x
    DOI: 10.1016/j.eneco.2019.104485
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    Cited by:

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