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A Low-Carbon Dispatch Model in a Wind Power Integrated System Considering Wind Speed Forecasting and Energy-Environmental Efficiency

Author

Listed:
  • Daojun Chen

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, China)

  • Qingwu Gong

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, China)

  • Bichang Zou

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, China)

  • Xiaohui Zhang

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, China)

  • Jian Zhao

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, China)

Abstract

This paper introduces the “Energy-Environmental Efficiency” concept of building a low-carbon dispatch model of wind-incorporated power systems from the perspective of environmental protection and low-carbon dispatch promotion based on the existing economic environmental dispatch. A rolling auto-regressive and moving-average model is adopted to forecast wind speeds for the next 24 h and reduce the disadvantages brought about to the power system dispatch by wind speed fluctuations. A fuzzy satisfaction-maximizing approach is employed to convert the multi-objective decision-making problem in the low-carbon dispatch model into a single nonlinear one. Particle swarm optimization with a simulated annealing algorithm hybrid is used for better solutions. Simulation results show that the energy-environmental efficiency concept benefits the optimization of the proposed power system dispatch, and the proposed low-carbon dispatch model is reasonable and practical.

Suggested Citation

  • Daojun Chen & Qingwu Gong & Bichang Zou & Xiaohui Zhang & Jian Zhao, 2012. "A Low-Carbon Dispatch Model in a Wind Power Integrated System Considering Wind Speed Forecasting and Energy-Environmental Efficiency," Energies, MDPI, vol. 5(4), pages 1-26, April.
    • Handle: RePEc:gam:jeners:v:5:y:2012:i:4:p:1245-1270:d:17367
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    References listed on IDEAS

    as
    1. Sfetsos, A., 2000. "A comparison of various forecasting techniques applied to mean hourly wind speed time series," Renewable Energy, Elsevier, vol. 21(1), pages 23-35.
    2. Foley, Aoife M. & Leahy, Paul G. & Marvuglia, Antonino & McKeogh, Eamon J., 2012. "Current methods and advances in forecasting of wind power generation," Renewable Energy, Elsevier, vol. 37(1), pages 1-8.
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    Cited by:

    1. Shao, Changzheng & Ding, Yi & Wang, Jianhui, 2019. "A low-carbon economic dispatch model incorporated with consumption-side emission penalty scheme," Applied Energy, Elsevier, vol. 238(C), pages 1084-1092.
    2. Stojiljković, Mirko M., 2017. "Bi-level multi-objective fuzzy design optimization of energy supply systems aided by problem-specific heuristics," Energy, Elsevier, vol. 137(C), pages 1231-1251.
    3. Elena Sosnina & Andrey Dar’enkov & Andrey Kurkin & Ivan Lipuzhin & Andrey Mamonov, 2022. "Review of Efficiency Improvement Technologies of Wind Diesel Hybrid Systems for Decreasing Fuel Consumption," Energies, MDPI, vol. 16(1), pages 1-38, December.
    4. Wei Wei & Yile Liang & Feng Liu & Shengwei Mei & Fang Tian, 2014. "Taxing Strategies for Carbon Emissions: A Bilevel Optimization Approach," Energies, MDPI, vol. 7(4), pages 1-18, April.

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