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Considering the Tiered Low-Carbon Optimal Dispatching of Multi-Integrated Energy Microgrid with P2G-CCS

Author

Listed:
  • Zixuan Liu

    (Water Conservancy Project and Civil Engineering College, Tibet Agriculture and Animal Husbandry University, Linzhi 860000, China
    Research Center of Civil, Hydraulic and Power Engineering of Tibet, Tibet Agriculture and Animal Husbandry University, Linzhi 860000, China)

  • Yao Gao

    (Beijing Shougang Automation Information Technology Co., Ltd., Beijing 100043, China)

  • Tingyu Li

    (State Grid Heilongjiang Power Company Limited State Grid Daqing Power Supply Company, Daqing 163000, China)

  • Ruijin Zhu

    (Research Center of Civil, Hydraulic and Power Engineering of Tibet, Tibet Agriculture and Animal Husbandry University, Linzhi 860000, China
    Electric Engineering College, Tibet Agriculture and Animal Husbandry University, Linzhi 860000, China)

  • Dewen Kong

    (Water Conservancy Project and Civil Engineering College, Tibet Agriculture and Animal Husbandry University, Linzhi 860000, China
    Research Center of Civil, Hydraulic and Power Engineering of Tibet, Tibet Agriculture and Animal Husbandry University, Linzhi 860000, China)

  • Hao Guo

    (Water Conservancy Project and Civil Engineering College, Tibet Agriculture and Animal Husbandry University, Linzhi 860000, China
    Research Center of Civil, Hydraulic and Power Engineering of Tibet, Tibet Agriculture and Animal Husbandry University, Linzhi 860000, China)

Abstract

The paper addresses the overlooked interaction between power-to-gas (P2G) devices and carbon capture and storage (CCS) equipment, along with the stepwise carbon trading mechanism in the context of current multi-park integrated energy microgrids (IEMGs). Additionally, it covers the economic and coordinated low-carbon operation issues in multi-park IEMGs under the carbon trading system. It proposes a multi-park IEMG low-carbon operation strategy based on the synchronous Alternating Direction Method of Multipliers (ADMM) algorithm. The algorithm first enables the distribution of cost relationships among multi-park IEMGs. Then, using a method that combines a CCS device with a P2G unit in line with the tiered carbon trading scheme, it expands on the model of single IEMGs managing thermal, electrical, and refrigeration energy. Finally, the comparison of simulation cases proves that the proposed strategy significantly reduces the external energy dependence while keeping the total cost of the users unchanged, and the cost of interaction with the external grid is reduced by 56.64%, the gas cost is reduced by 27.78%, and the carbon emission cost is reduced by 29.54% by joining the stepped carbon trading mechanism.

Suggested Citation

  • Zixuan Liu & Yao Gao & Tingyu Li & Ruijin Zhu & Dewen Kong & Hao Guo, 2024. "Considering the Tiered Low-Carbon Optimal Dispatching of Multi-Integrated Energy Microgrid with P2G-CCS," Energies, MDPI, vol. 17(14), pages 1-18, July.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:14:p:3414-:d:1433271
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    References listed on IDEAS

    as
    1. Ju, Liwei & Yin, Zhe & Lu, Xiaolong & Yang, Shenbo & Li, Peng & Rao, Rao & Tan, Zhongfu, 2022. "A Tri-dimensional Equilibrium-based stochastic optimal dispatching model for a novel virtual power plant incorporating carbon Capture, Power-to-Gas and electric vehicle aggregator," Applied Energy, Elsevier, vol. 324(C).
    2. Najafi, Arsalan & Pourakbari-Kasmaei, Mahdi & Jasinski, Michal & Lehtonen, Matti & Leonowicz, Zbigniew, 2021. "A hybrid decentralized stochastic-robust model for optimal coordination of electric vehicle aggregator and energy hub entities," Applied Energy, Elsevier, vol. 304(C).
    3. Yousaf Raza, Muhammad & Lin, Boqiang, 2022. "Natural gas consumption, energy efficiency and low carbon transition in Pakistan," Energy, Elsevier, vol. 240(C).
    4. Liang, Tao & Chai, Lulu & Tan, Jianxin & Jing, Yanwei & Lv, Liangnian, 2024. "Dynamic optimization of an integrated energy system with carbon capture and power-to-gas interconnection: A deep reinforcement learning-based scheduling strategy," Applied Energy, Elsevier, vol. 367(C).
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