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One-segment linearization modeling of electricity-gas system optimization

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  • Bao, Shiyuan
  • Yang, Zhifang
  • Guo, Lin
  • Yu, Juan
  • Dai, Wei

Abstract

Linear models of energy systems are preferable in industries because linear optimization problems bring benefits in convergence, efficiency, and convenience for pricing. The nonlinear model of the natural-gas system is a major bottleneck for the fully-linear modeling of electricity-gas systems. In this paper, a one-segment linear gas flow model without the need of integer variables is presented. To facilitate the linear formulation of the gas flow model, a deep learning method is applied to predict the interval of the gas pressure. A special setting of the linearization interval is proposed, which avoids the influence of the range of the linearization interval on the optimal energy flow (OEF) solution. Based on the one-segment linear gas flow model, a fully-linear OEF model of the electricity-gas system is formulated. The efficiency of the proposed method is much improved compared with the traditional mixed integer linear programming approach.

Suggested Citation

  • Bao, Shiyuan & Yang, Zhifang & Guo, Lin & Yu, Juan & Dai, Wei, 2020. "One-segment linearization modeling of electricity-gas system optimization," Energy, Elsevier, vol. 197(C).
    • Handle: RePEc:eee:energy:v:197:y:2020:i:c:s0360544220303376
    DOI: 10.1016/j.energy.2020.117230
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    References listed on IDEAS

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    Cited by:

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    2. Liu, Haizhou & Shen, Xinwei & Guo, Qinglai & Sun, Hongbin, 2021. "A data-driven approach towards fast economic dispatch in electricity–gas coupled systems based on artificial neural network," Applied Energy, Elsevier, vol. 286(C).
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    4. Huang, Gang & Wang, Jianhui & Wang, Cheng & Guo, Chuangxin, 2021. "Cascading imbalance in coupled gas-electric energy systems," Energy, Elsevier, vol. 231(C).

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