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Probabilistic sizing of a low-carbon emission power system considering HVDC transmission and microgrid clusters

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  • Li, Bei
  • Li, Jiangchen

Abstract

Nowadays, large numbers of renewable energy generators have been installed world-widely to reduce carbon dioxide emissions. Hydrogen-based storage system has the largest energy density, which is with a great potential suitable to address the intermittence of these renewable energy outputs. In addition, the HVDC (high voltage direct current) transmission is often deployed to transmit the remote located abundant renewable energy resources to terminal consumers due to its lower losses. However, building a low-carbon emission power system through hydrogen-based microgrid clusters and the HVDC transmission still lacks investigations thus is an essential problem. In this paper, a probabilistic sizing methodology considering the uncertainties is developed. First, both the hydrogen-based storage system model and the HVDC model are analytically modeled. Second, a mixed integer programming optimal strategy is deployed to operate the hydrogen-based microgrid. Third, operation model of the renewable energy power station-HVDC transmission-IEEE 30 nodes network-microgrid clusters are presented. Last, the best sizing value for each component is obtained by a genetic algorithm. In addition, the uncertainties of the data profiles are modeled using scenario method. Within different scenarios, the probability density function of each sizing component is calculated. The results demonstrate that the sizing values based on the probabilistic method can reduce the adverse impacts of the uncertainties on the utility grids. Specifically, the mean value of the utility grid operation cost is reduced by 0.005 %-0.08 %.

Suggested Citation

  • Li, Bei & Li, Jiangchen, 2021. "Probabilistic sizing of a low-carbon emission power system considering HVDC transmission and microgrid clusters," Applied Energy, Elsevier, vol. 304(C).
    • Handle: RePEc:eee:appene:v:304:y:2021:i:c:s0306261921011004
    DOI: 10.1016/j.apenergy.2021.117760
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    3. Fei Feng & Xin Du & Qiang Si & Hao Cai, 2022. "Hybrid Game Optimization of Microgrid Cluster (MC) Based on Service Provider (SP) and Tiered Carbon Price," Energies, MDPI, vol. 15(14), pages 1-22, July.
    4. Chen, Xiaoyuan & Jiang, Shan & Chen, Yu & Zou, Zhice & Shen, Boyang & Lei, Yi & Zhang, Donghui & Zhang, Mingshun & Gou, Huayu, 2022. "Energy-saving superconducting power delivery from renewable energy source to a 100-MW-class data center," Applied Energy, Elsevier, vol. 310(C).
    5. Wu, Xiong & Cao, Binrui & Liu, Bingwen & Zhang, Ziyu & Wang, Xiuli, 2023. "Capacity planning of carbon-free microgrid with hydrogen storage considering robust short-term off-grid operation," Renewable Energy, Elsevier, vol. 202(C), pages 242-254.

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