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A multi-objective mixed integer linear programming model for integrated electricity-gas network expansion planning considering the impact of photovoltaic generation

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  • Yamchi, Hamid Bakhshi
  • Safari, Amin
  • Guerrero, Josep M.

Abstract

This paper presents a new expansion planning model of integrated electricity and gas networks with the high penetration of renewable energy resources, using a multi-objective mixed integer linear programming approach. The proposed method concludes the generation and transmission expansion planning of electricity network and natural gas network expansion planning. Since the outage of natural gas-fired units due to gas network faults is possible, in this paper, integrated electricity and gas network expansion planning is studied by applying N−1 contingency to these systems. Photovoltaic power plants are used for generation expansion planning to reduce emission and greenhouse gases. Reduced disjunctive model is applied to decrease the calculation time of applying N−1 criterion to power systems. A scenario-based stochastic method is exploited to study the correlated uncertainty of the photovoltaic generation and electrical loads. The proposed model is applied to two case studies, including modified Garver’s system & 5-nodegas network and IEEE-RTS system & Belgian gas network. The numerical results show that using photovoltaic generation in the expansion planning problem while considering contingency in both electrical and gas networks decreases costs by 9.08% and 7.08%, and reduces emission by 38.71% and 34.62% in the first and second systems, respectively.

Suggested Citation

  • Yamchi, Hamid Bakhshi & Safari, Amin & Guerrero, Josep M., 2021. "A multi-objective mixed integer linear programming model for integrated electricity-gas network expansion planning considering the impact of photovoltaic generation," Energy, Elsevier, vol. 222(C).
    • Handle: RePEc:eee:energy:v:222:y:2021:i:c:s0360544221001821
    DOI: 10.1016/j.energy.2021.119933
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    7. Finke, Jonas & Bertsch, Valentin, 2022. "Implementing a highly adaptable method for the multi-objective optimisation of energy systems," MPRA Paper 115504, University Library of Munich, Germany.
    8. 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).
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