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Economic Emission Dispatch for Wind Power Integrated System with Carbon Trading Mechanism

Author

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  • Jingliang Jin

    (College of Science, Nantong University, Nantong 226001, China
    College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 210023, China)

  • Qinglan Wen

    (College of Science, Nantong University, Nantong 226001, China)

  • Xianyue Zhang

    (College of Science, Nantong University, Nantong 226001, China)

  • Siqi Cheng

    (College of Science, Nantong University, Nantong 226001, China)

  • Xiaojun Guo

    (College of Science, Nantong University, Nantong 226001, China
    College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 210023, China)

Abstract

Nowadays, the power system is faced with some new changes from low-carbon approaches, though these approaches have proved to be effective in developing low-carbon electricity. Specifically, wind power integration and carbon trading influence the traditional economic emission dispatch (EED) mode, allowing for the disturbance of wind power uncertainties and the fluctuation of carbon trading price. Aiming at the above problems, this study firstly builds a stochastic EED model in the form of chance-constrained programming associated with wind power reliability. Next, wind power features are deduced from the statistic characteristics of wind speed, and thus the established model is converted to a deterministic form. After that, an auxiliary decision-making method based on the technique for order preference by similarity to an ideal solution (TOPSIS) is designed to draw the optimal solution based upon the specific requirements of carbon emission control. The simulation results eventually indicate that the minimization of fuel costs and carbon emissions comes at the expense of wind power reliability. Meanwhile, carbon emission reduction can be effectively realized by carbon trading rather than a substantial increase in fuel costs, and carbon trading may help to improve power generation efficiency. Furthermore, carbon trading prices could be determined by the demands of carbon emission reduction and power generation efficiency improvement.

Suggested Citation

  • Jingliang Jin & Qinglan Wen & Xianyue Zhang & Siqi Cheng & Xiaojun Guo, 2021. "Economic Emission Dispatch for Wind Power Integrated System with Carbon Trading Mechanism," Energies, MDPI, vol. 14(7), pages 1-17, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:7:p:1870-:d:525596
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    References listed on IDEAS

    as
    1. Kuo, Ting, 2017. "A modified TOPSIS with a different ranking index," European Journal of Operational Research, Elsevier, vol. 260(1), pages 152-160.
    2. Yuan, Jiahai & Hu, Zhaoguang, 2011. "Low carbon electricity development in China--An IRSP perspective based on Super Smart Grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(6), pages 2707-2713, August.
    3. Zhou, Wei & Yang, Hongxing & Fang, Zhaohong, 2006. "Wind power potential and characteristic analysis of the Pearl River Delta region, China," Renewable Energy, Elsevier, vol. 31(6), pages 739-753.
    4. Basu, M., 2014. "Fuel constrained economic emission dispatch using nondominated sorting genetic algorithm-II," Energy, Elsevier, vol. 78(C), pages 649-664.
    5. Zhang, Shijie & Wei, Jing & Chen, Xi & Zhao, Yuhao, 2020. "China in global wind power development: Role, status and impact," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    6. 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.
    7. Chen, J.J. & Qi, B.X. & Peng, K. & Li, Y. & Zhao, Y.L., 2020. "Conditional value-at-credibility for random fuzzy wind power in demand response integrated multi-period economic emission dispatch," Applied Energy, Elsevier, vol. 261(C).
    8. Jin, Jingliang & Zhou, Dequn & Zhou, Peng & Miao, Zhuang, 2014. "Environmental/economic power dispatch with wind power," Renewable Energy, Elsevier, vol. 71(C), pages 234-242.
    9. Li, Jinying & Li, Sisi & Wu, Fan, 2020. "Research on carbon emission reduction benefit of wind power project based on life cycle assessment theory," Renewable Energy, Elsevier, vol. 155(C), pages 456-468.
    10. Kahrl, Fredrich & Williams, Jim & Jianhua, Ding & Junfeng, Hu, 2011. "Challenges to China's transition to a low carbon electricity system," Energy Policy, Elsevier, vol. 39(7), pages 4032-4041, July.
    11. Chang, Tian Pau, 2011. "Estimation of wind energy potential using different probability density functions," Applied Energy, Elsevier, vol. 88(5), pages 1848-1856, May.
    12. Elattar, Ehab E., 2018. "Modified harmony search algorithm for combined economic emission dispatch of microgrid incorporating renewable sources," Energy, Elsevier, vol. 159(C), pages 496-507.
    13. Jin, Jingliang & Zhou, Dequn & Zhou, Peng & Qian, Shuqu & Zhang, Mingming, 2016. "Dispatching strategies for coordinating environmental awareness and risk perception in wind power integrated system," Energy, Elsevier, vol. 106(C), pages 453-463.
    14. Chen, Min-Rong & Zeng, Guo-Qiang & Lu, Kang-Di, 2019. "Constrained multi-objective population extremal optimization based economic-emission dispatch incorporating renewable energy resources," Renewable Energy, Elsevier, vol. 143(C), pages 277-294.
    15. Li, Yang & Wang, Jinlong & Zhao, Dongbo & Li, Guoqing & Chen, Chen, 2018. "A two-stage approach for combined heat and power economic emission dispatch: Combining multi-objective optimization with integrated decision making," Energy, Elsevier, vol. 162(C), pages 237-254.
    16. Mok, Ken L. & Han, Seung H. & Choi, Seokjin, 2014. "The implementation of clean development mechanism (CDM) in the construction and built environment industry," Energy Policy, Elsevier, vol. 65(C), pages 512-523.
    17. Kwon, Soon-Duck, 2010. "Uncertainty analysis of wind energy potential assessment," Applied Energy, Elsevier, vol. 87(3), pages 856-865, March.
    18. Lin, Boqiang & Chen, Yufang, 2019. "Impacts of policies on innovation in wind power technologies in China," Applied Energy, Elsevier, vol. 247(C), pages 682-691.
    19. Sagbansua, Lutfu & Balo, Figen, 2017. "Decision making model development in increasing wind farm energy efficiency," Renewable Energy, Elsevier, vol. 109(C), pages 354-362.
    20. Zhao, Xiaoli & Liu, Suwei & Yan, Fengguang & Yuan, Ziqian & Liu, Zhiwen, 2017. "Energy conservation, environmental and economic value of the wind power priority dispatch in China," Renewable Energy, Elsevier, vol. 111(C), pages 666-675.
    21. Dariush Khezrimotlagh & Yao Chen, 2018. "The Optimization Approach," International Series in Operations Research & Management Science, in: Decision Making and Performance Evaluation Using Data Envelopment Analysis, chapter 0, pages 107-134, Springer.
    22. Jin, Jingliang & Zhou, Peng & Li, Chenyu & Bai, Yang & Wen, Qinglan, 2020. "Optimization of power dispatching strategies integrating management attitudes with low carbon factors," Renewable Energy, Elsevier, vol. 155(C), pages 555-568.
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    Cited by:

    1. Can Ding & Yiyuan Zhou & Qingchang Ding & Kaiming Li, 2022. "Integrated Carbon-Capture-Based Low-Carbon Economic Dispatch of Power Systems Based on EEMD-LSTM-SVR Wind Power Forecasting," Energies, MDPI, vol. 15(5), pages 1-27, February.
    2. Wang, Yajun & Wang, Jidong & Cao, Man & Kong, Xiangyu & Abderrahim, Bouchedjira & Yuan, Long & Vartosh, Aris, 2023. "Dynamic emission dispatch considering the probabilistic model with multiple smart energy system players based on a developed fuzzy theory-based harmony search algorithm," Energy, Elsevier, vol. 269(C).

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