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Balancing low-carbon power dispatching strategy for wind power integrated system

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  • Jin, Jingliang
  • Zhou, Peng
  • Zhang, Mingming
  • Yu, Xianyu
  • Din, Hao

Abstract

With the introduction of carbon trading in wind power integrated system, decision-makers may face more contradictions, such as economy and environment, objectivity and subjectivity, certainty and uncertainty. In order to balance these contradictions, this paper presents an economic emission dispatch model focusing on carbon price and wind power uncertainty simultaneously. Specifically factors for eliminating adverse effects of wind power uncertainty in both economic and environmental respects are particularly considered, decision-makers’ subjective investment attitudes towards wind power development could be quantitatively depicted according to carbon price. The simulation results eventually demonstrate that increasing the penalty coefficient core reflects decision-makers’ more radical investment attitudes, while improving the reserve coefficient core embodies more conservative investment attitudes; increasing carbon price will generally encourage decision-makers to explore more wind power, and to restrict thermal power; the more radical attitudes decision-makers hold, scheduling outputs the more sensitive with carbon price, and the faster wind power advance. Taking into full account carbon price and wind power uncertainty, the proposed model is beneficial to exploring a more balanced low-carbon power dispatching strategy for wind power integrated system.

Suggested Citation

  • Jin, Jingliang & Zhou, Peng & Zhang, Mingming & Yu, Xianyu & Din, Hao, 2018. "Balancing low-carbon power dispatching strategy for wind power integrated system," Energy, Elsevier, vol. 149(C), pages 914-924.
  • Handle: RePEc:eee:energy:v:149:y:2018:i:c:p:914-924
    DOI: 10.1016/j.energy.2018.02.103
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    7. Mou, Dunguo & He, Xiaoping, 2019. "Developing large-scale energy storage to alleviate a low-carbon energy bubble," Energy Policy, Elsevier, vol. 132(C), pages 62-74.
    8. 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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