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Effect of power-to-gas technology in energy hub optimal operation and gas network congestion reduction

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  • Salehi, Javad
  • Namvar, Amin
  • Gazijahani, Farhad Samadi
  • Shafie-khah, Miadreza
  • Catalão, João P.S.

Abstract

Natural gas will play a key role in the transition to a lower-carbon economy, constituting a natural alternative to coal and acting as a backup resource to the intermittent nature of renewable generation. These energy carriers can be structurally linked together by Power-to-X technologies because of their interaction to increase energy efficiency. For this purpose, this paper proposes an innovative model to optimally manage the electricity and natural gas grids in a cost-efficient manner. In this model, an energy hub has water, electricity, and gas oil as inputs, supplying electric and thermal loads. Besides, the energy hub uses the Power-to-gas (P2G) technology to produce natural gas, selling it to a gas network to reduce the congestion in gas pipelines and the energy hub owner's costs. A demand response program has been also applied in this model to shift the loads from on-peak times to off-peak ones. Various technologies such as energy storage and distributed generation have been used in the modeling to reach the goals targeted by operators. Furthermore, a scenario generation method has been applied to model the uncertainty of wind turbine output. The proposed problem has been finally formulated as mixed-integer linear programming that has been solved under GAMS software by using CPLEX solver to reach the global optimality. The results obtained from simulations demonstrate that the proposed model can significantly reduce the operation cost, while properly alleviating gas network congestion.

Suggested Citation

  • Salehi, Javad & Namvar, Amin & Gazijahani, Farhad Samadi & Shafie-khah, Miadreza & Catalão, João P.S., 2022. "Effect of power-to-gas technology in energy hub optimal operation and gas network congestion reduction," Energy, Elsevier, vol. 240(C).
    • Handle: RePEc:eee:energy:v:240:y:2022:i:c:s036054422103084x
    DOI: 10.1016/j.energy.2021.122835
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    5. Innocent Kamwa & Leila Bagherzadeh & Atieh Delavari, 2023. "Integrated Demand Response Programs in Energy Hubs: A Review of Applications, Classifications, Models and Future Directions," Energies, MDPI, vol. 16(11), pages 1-21, May.
    6. Shahbazbegian, Vahid & Shafie-khah, Miadreza & Laaksonen, Hannu & Strbac, Goran & Ameli, Hossein, 2023. "Resilience-oriented operation of microgrids in the presence of power-to-hydrogen systems," Applied Energy, Elsevier, vol. 348(C).
    7. Akbari, Ehsan & Mousavi Shabestari, Seyed Farzin & Pirouzi, Sasan & Jadidoleslam, Morteza, 2023. "Network flexibility regulation by renewable energy hubs using flexibility pricing-based energy management," Renewable Energy, Elsevier, vol. 206(C), pages 295-308.
    8. Chen, Minghao & Sun, Yi & Xie, Zhiyuan & Lin, Nvgui & Wu, Peng, 2023. "An efficient and privacy-preserving algorithm for multiple energy hubs scheduling with federated and matching deep reinforcement learning," Energy, Elsevier, vol. 284(C).
    9. Fan, Linyuan & Ji, Dandan & Lin, Geng & Lin, Peng & Liu, Lixi, 2023. "Information gap-based multi-objective optimization of a virtual energy hub plant considering a developed demand response model," Energy, Elsevier, vol. 276(C).
    10. He, Shuaijia & Gao, Hongjun & Chen, Zhe & Liu, Junyong & Zhao, Liang & Wu, Gang & Xu, Song, 2022. "Low-carbon distribution system planning considering flexible support of zero-carbon energy station," Energy, Elsevier, vol. 244(PB).
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