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Stochastic optimal scheduling of electricity–hydrogen enriched compressed natural gas urban integrated energy system

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  • Ye, Jianan
  • Xie, Min
  • Zhang, Shiping
  • Huang, Ying
  • Liu, Mingbo
  • Wang, Qiong

Abstract

Hydrogen blended gas turbines (HBGTs) are pivotal for the carbon emission reduction (CER) of urban integrated energy systems (UIESs). However, the traditional rough modeling performed in the existing research on optimal scheduling is not suitable for the implementation of HBGTs. To reflect the influence of hydrogen blending on the operation performance of HBGTs, a novel HBGT model is developed in this study based on the multidisciplinary integration modeling methodology with chemical reaction kinetics and system thermodynamics. This model can describe carbon emission and co-combustion power generation properties of HBGT. When the tracking of hydrogen-enriched compressed natural gas (HCNG) composition is considered, the model can be further applied to the stochastic optimal scheduling of electricity–HCNG UIESs with internal and external uncertainties to solve the carbon emission and renewable energy source (RES) consumption problems of UIESs. The simulation results demonstrate that the proposed HBGT modeling method has low-carbon economy and offers accurate operation performance. Furthermore, HBGT power generation can reduce the carbon emissions of an electricity–HCNG UIES by 22.62%. Meanwhile, the economy and wind power consumption are improved by more than 0.71% and 6.53%, respectively. The proposed optimal scheduling model considering multiple uncertainties is more reliable with the improvement of confidence level, which is an important guide for obtaining the optimal hydrogen blending ratio and promoting the CER of UIES.

Suggested Citation

  • Ye, Jianan & Xie, Min & Zhang, Shiping & Huang, Ying & Liu, Mingbo & Wang, Qiong, 2023. "Stochastic optimal scheduling of electricity–hydrogen enriched compressed natural gas urban integrated energy system," Renewable Energy, Elsevier, vol. 211(C), pages 1024-1044.
    • Handle: RePEc:eee:renene:v:211:y:2023:i:c:p:1024-1044
    DOI: 10.1016/j.renene.2023.05.020
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    References listed on IDEAS

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