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Low-carbon power dispatch with wind power based on carbon trading mechanism

Author

Listed:
  • Jin, Jingliang
  • Zhou, Peng
  • Li, Chenyu
  • Guo, Xiaojun
  • Zhang, Mingming

Abstract

Low-carbon power dispatching with wind power has been attracting increasing attention recently, whereas it also encounters some difficulties under carbon trading mechanism, such as the resistance to carbon emission reduction, the fairness of carbon trading, and the disturbance of wind power uncertainty. For harmonizing these contradictions, this paper presents a stochastic dynamic economic dispatch model focusing on wind power uncertainty and carbon emission rights simultaneously, which tries to minimize total electrical energy costs under uncertainty, and to meet the specific requirement of carbon emission reductions. Moreover, several traditional allocation methods for initial carbon emission rights emphasizing the equality are introduced, and the uncertainty characterizations of wind power are discussed through the scenario-generation technique. The simulation results eventually demonstrate that the operating cost for non-cooperative mode cannot be optimized as the ratio of carbon emission reductions rises; carbon trading mechanism is beneficial for lower cost under the pressure of carbon emission reduction; carbon price ranges that suit both carbon trading parties could be deduced. Synthesizing wind power uncertainty and carbon emission rights, this study is helpful for allocating the load demand of wind power integrated system to each generating unit more scientifically and reasonably based on carbon trading mechanism.

Suggested Citation

  • Jin, Jingliang & Zhou, Peng & Li, Chenyu & Guo, Xiaojun & Zhang, Mingming, 2019. "Low-carbon power dispatch with wind power based on carbon trading mechanism," Energy, Elsevier, vol. 170(C), pages 250-260.
    • Handle: RePEc:eee:energy:v:170:y:2019:i:c:p:250-260
    DOI: 10.1016/j.energy.2018.12.126
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    8. Tan, Qinliang & Ding, Yihong & Zheng, Jin & Dai, Mei & Zhang, Yimei, 2021. "The effects of carbon emissions trading and renewable portfolio standards on the integrated wind–photovoltaic–thermal power-dispatching system: Real case studies in China," Energy, Elsevier, vol. 222(C).
    9. Meng, Fanyi & Bai, Yang & Jin, Jingliang, 2021. "An advanced real-time dispatching strategy for a distributed energy system based on the reinforcement learning algorithm," Renewable Energy, Elsevier, vol. 178(C), pages 13-24.
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