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A multi-objective optimization framework for integrated electricity and natural gas networks considering smart homes in downward under uncertainties

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  • Safaie, Amir Abbas
  • Alizadeh Bidgoli, Mohsen
  • Javadi, Saeid

Abstract

This paper presents a multi-objective optimization framework for day-ahead scheduling of integrated electricity and natural gas networks in the presence of five sets of smart homes. The study system includes a 69-bus electricity distribution network and a 14-node natural gas network, equipped with gas turbines, wind turbines, photovoltaic (PV) panels, electrical energy storage (EES) systems and power-to-gas (P2G) technologies. The scheduling problem is modeled as a two-objective optimization problem and its objectives include minimizing the operation cost and CO2 emissions. In order to model the two-objective optimization problem, the epsilon-constraint method has been adopted. Finally, the proposed model has been solved in the form of 3 case studies by CPLEX solver in general algebraic modeling system (GAMS) software. The simulation results demonstrate that the two-objective modeling of the scheduling problem leads to a 2.87% reduction in CO2 emissions despite a 0.75% increase in operating costs. The results also illustrate that a 21.93% increase in the customer's comfort index leads to a 41% increase in annual operating costs. Finally, the results substantiate that the installation of P2G technologies along with wind turbines prevents wind power curtailment in some hours.

Suggested Citation

  • Safaie, Amir Abbas & Alizadeh Bidgoli, Mohsen & Javadi, Saeid, 2022. "A multi-objective optimization framework for integrated electricity and natural gas networks considering smart homes in downward under uncertainties," Energy, Elsevier, vol. 239(PC).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pc:s0360544221024622
    DOI: 10.1016/j.energy.2021.122214
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    Cited by:

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    2. Zhang, Houwang & Wu, Qiuwei & Chen, Jian & Lu, Lina & Zhang, Jiangfeng & Zhang, Shuyi, 2023. "Multiple stage stochastic planning of integrated electricity and gas system based on distributed approximate dynamic programming," Energy, Elsevier, vol. 270(C).
    3. Mansouri, S.A. & Ahmarinejad, A. & Nematbakhsh, E. & Javadi, M.S. & Esmaeel Nezhad, A. & Catalão, J.P.S., 2022. "A sustainable framework for multi-microgrids energy management in automated distribution network by considering smart homes and high penetration of renewable energy resources," Energy, Elsevier, vol. 245(C).
    4. 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).
    5. Hong, Bingyuan & Qiao, Dan & Li, Yichen & Sun, Xiaoqing & Yang, Baolong & Li, Li & Gong, Jing & Wen, Kai, 2023. "Supply-demand balance of natural gas pipeline network integrating hydraulic and thermal characteristics, energy conservation and carbon reduction," Energy, Elsevier, vol. 283(C).
    6. Khojasteh, Meysam & Faria, Pedro & Lezama, Fernando & Vale, Zita, 2022. "Optimal strategy of electricity and natural gas aggregators in the energy and balance markets," Energy, Elsevier, vol. 257(C).
    7. Rezaee Jordehi, Ahmad & Mansouri, Seyed Amir & Tostado-Véliz, Marcos & Sirjani, Reza & Safaraliev, Murodbek & Nasir, Mohammad, 2024. "A three-level model for integration of hydrogen refuelling stations in interconnected power-gas networks considering vehicle-to-infrastructure (V2I) technology," Energy, Elsevier, vol. 308(C).

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