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Co-optimization of resilient gas and electricity networks; a novel possibilistic chance-constrained programming approach

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  • Shabazbegian, Vahid
  • Ameli, Hossein
  • Ameli, Mohammad Taghi
  • Strbac, Goran
  • Qadrdan, Meysam

Abstract

Gas-fired power plants are commonly employed to deal with the intermittency of renewable energy resources due to their flexible characteristics. Therefore, the intermittency in the power system transmits to the gas system through the gas-fired power plants, which makes the operation of these systems even more interdependent. This study proposes a novel possibilistic model for the integrated operation of gas and power systems in the presence of electric vehicles and demand response. The model takes into account uncertainty in demand prediction and output power of wind farms, which is based on possibility and necessity theories in fuzzy logic through modeling uncertain parameters by Gaussian membership function. Moreover, a contingency analysis algorithm based on maximin optimization is developed to enhance the resiliency in the integrated operation of these systems by finding the worst-case scenario for the outage of components. The proposed model is implemented on a Belgium gas network and IEEE 24-bus electricity network. It is demonstrated that the possibilistic model allows the gas network to respond to the demand variations by providing a sufficient level of linepack within the pipelines. As a result, gas-fired power plants are supposed to commit more efficiently to cope with the intermittency of wind farms, which reduce the wind curtailment by 26%. Furthermore, it is quantified that through applying the contingency analysis algorithm in presence of demand response and electrical vehicles, the costs of operation and load shedding is reduced up to 17% and 83%, respectively.

Suggested Citation

  • Shabazbegian, Vahid & Ameli, Hossein & Ameli, Mohammad Taghi & Strbac, Goran & Qadrdan, Meysam, 2021. "Co-optimization of resilient gas and electricity networks; a novel possibilistic chance-constrained programming approach," Applied Energy, Elsevier, vol. 284(C).
    • Handle: RePEc:eee:appene:v:284:y:2021:i:c:s030626192031672x
    DOI: 10.1016/j.apenergy.2020.116284
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    2. Shen, Feifei & Zhao, Liang & Wang, Meihong & Du, Wenli & Qian, Feng, 2022. "Data-driven adaptive robust optimization for energy systems in ethylene plant under demand uncertainty," Applied Energy, Elsevier, vol. 307(C).
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    5. Zhang, Juntao & Cheng, Chuntian & Yu, Shen & Su, Huaying, 2022. "Chance-constrained co-optimization for day-ahead generation and reserve scheduling of cascade hydropower–variable renewable energy hybrid systems," Applied Energy, Elsevier, vol. 324(C).
    6. Qiu, Haifeng & Gu, Wei & Liu, Pengxiang & Sun, Qirun & Wu, Zhi & Lu, Xi, 2022. "Application of two-stage robust optimization theory in power system scheduling under uncertainties: A review and perspective," Energy, Elsevier, vol. 251(C).

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