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Multi-objective electricity-gas flow with stochastic dispersion control for air pollutants using two-stage Pareto optimization

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  • Chen, Yixuan
  • Qu, Kaiping
  • Pan, Zhenning
  • Yu, Tao

Abstract

In consideration of the deteriorating global warming and air pollution, a multi-objective electricity-gas flow (MOEGF) is proposed in this paper. Different from the existing emission control modes, a novel stochastic dispersion (SD) control for air pollutants is formulated in the MOEGF. The SD control considering district-varying environmental tolerance introduces the Gauss puff dispersion model to precisely describe the emission dispersion, and further reduces the influence on air pollutant concentration. In addition, a scenario-based strategy is developed to enhance the robustness of SD control in the atmospheric condition uncertainty. To solve the MOEGF involving complex constraints and multiple conflictive objectives, a two-stage Pareto optimization framework is proposed. In the first stage, the nonconvex natural gas flow is linearized, and then an improved homogenized adjacent points method (I-HAPM) is developed to calculate a high-quality Pareto solution set of the simplified MOEGF, to provide diversified trade-off between objectives. Owing to the cooperation of the linearized gas flow and the Pareto optimizer I-HAPM, the computational quality and efficiency of the Pareto solution set is improved. In the second stage, a tailored compromise solution for operation is determined from the Pareto solution set according to real dispatch requirements, which is convexified with the penalty convex–concave procedure to guarantee the operation security. Case studies demonstrate that the MOEGF effectively reduces carbon emissions and influence on air pollution of the combined system, with a low-level cost sacrifice. Besides, the effectiveness of the two-stage Pareto optimization is validated.

Suggested Citation

  • Chen, Yixuan & Qu, Kaiping & Pan, Zhenning & Yu, Tao, 2020. "Multi-objective electricity-gas flow with stochastic dispersion control for air pollutants using two-stage Pareto optimization," Applied Energy, Elsevier, vol. 279(C).
    • Handle: RePEc:eee:appene:v:279:y:2020:i:c:s0306261920312599
    DOI: 10.1016/j.apenergy.2020.115773
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    References listed on IDEAS

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    Cited by:

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    2. Wang, Yubin & Dong, Wei & Yang, Qiang, 2022. "Multi-stage optimal energy management of multi-energy microgrid in deregulated electricity markets," Applied Energy, Elsevier, vol. 310(C).

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