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A Power System Optimal Dispatch Strategy Considering the Flow of Carbon Emissions and Large Consumers

Author

Listed:
  • Jun Yang

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

  • Xin Feng

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

  • Yufei Tang

    (Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI 02881, USA)

  • Jun Yan

    (Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI 02881, USA)

  • Haibo He

    (Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI 02881, USA)

  • Chao Luo

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

Abstract

The carbon emissions trading market and direct power purchases by large consumers are two promising directions of power system development. To trace the carbon emission flow in the power grid, the theory of carbon emission flow is improved by allocating power loss to the load side. Based on the improved carbon emission flow theory, an optimal dispatch model is proposed to optimize the cost of both large consumers and the power grid, which will benefit from the carbon emissions trading market. Moreover, to better simulate reality, the direct purchase of power by large consumers is also considered in this paper. The OPF (optimal power flow) method is applied to solve the problem. To evaluate our proposed optimal dispatch strategy, an IEEE 30-bus system is used to test the performance. The effects of the price of carbon emissions and the price of electricity from normal generators and low-carbon generators with regards to the optimal dispatch are analyzed. The simulation results indicate that the proposed strategy can significantly reduce both the operation cost of the power grid and the power utilization cost of large consumers.

Suggested Citation

  • Jun Yang & Xin Feng & Yufei Tang & Jun Yan & Haibo He & Chao Luo, 2015. "A Power System Optimal Dispatch Strategy Considering the Flow of Carbon Emissions and Large Consumers," Energies, MDPI, vol. 8(9), pages 1-20, August.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:9:p:9087-9106:d:54802
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    References listed on IDEAS

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    1. Shi, Bin & Yan, Lie-Xiang & Wu, Wei, 2013. "Multi-objective optimization for combined heat and power economic dispatch with power transmission loss and emission reduction," Energy, Elsevier, vol. 56(C), pages 135-143.
    2. Wang, Qunwei & Su, Bin & Sun, Jiasen & Zhou, Peng & Zhou, Dequn, 2015. "Measurement and decomposition of energy-saving and emissions reduction performance in Chinese cities," Applied Energy, Elsevier, vol. 151(C), pages 85-92.
    3. Tsai, Ming-Tang & Yen, Chih-Wei, 2011. "The influence of carbon dioxide trading scheme on economic dispatch of generators," Applied Energy, Elsevier, vol. 88(12), pages 4811-4816.
    4. Tang, Ling & Wu, Jiaqian & Yu, Lean & Bao, Qin, 2015. "Carbon emissions trading scheme exploration in China: A multi-agent-based model," Energy Policy, Elsevier, vol. 81(C), pages 152-169.
    5. Yu, Shiwei & Wei, Yi-Ming & Guo, Haixiang & Ding, Liping, 2014. "Carbon emission coefficient measurement of the coal-to-power energy chain in China," Applied Energy, Elsevier, vol. 114(C), pages 290-300.
    6. Zhou, Y. & Li, Y.P. & Huang, G.H., 2015. "Planning sustainable electric-power system with carbon emission abatement through CDM under uncertainty," Applied Energy, Elsevier, vol. 140(C), pages 350-364.
    7. Zhou, P. & Zhang, L. & Zhou, D.Q. & Xia, W.J., 2013. "Modeling economic performance of interprovincial CO2 emission reduction quota trading in China," Applied Energy, Elsevier, vol. 112(C), pages 1518-1528.
    8. Li, Ying & Lukszo, Zofia & Weijnen, Margot, 2015. "The implications of CO2 price for China’s power sector decarbonization," Applied Energy, Elsevier, vol. 146(C), pages 53-64.
    9. Yang, Jun & He, Lifu & Fu, Siyao, 2014. "An improved PSO-based charging strategy of electric vehicles in electrical distribution grid," Applied Energy, Elsevier, vol. 128(C), pages 82-92.
    10. Albino, Vito & Ardito, Lorenzo & Dangelico, Rosa Maria & Messeni Petruzzelli, Antonio, 2014. "Understanding the development trends of low-carbon energy technologies: A patent analysis," Applied Energy, Elsevier, vol. 135(C), pages 836-854.
    11. Jubril, A.M. & Olaniyan, O.A. & Komolafe, O.A. & Ogunbona, P.O., 2014. "Economic-emission dispatch problem: A semi-definite programming approach," Applied Energy, Elsevier, vol. 134(C), pages 446-455.
    12. Zhong, Haiwang & Xia, Qing & Chen, Yuguo & Kang, Chongqing, 2015. "Energy-saving generation dispatch toward a sustainable electric power industry in China," Energy Policy, Elsevier, vol. 83(C), pages 14-25.
    13. Park, Taeil & Kim, Changyoon & Kim, Hyoungkwan, 2014. "A real option-based model to valuate CDM projects under uncertain energy policies for emission trading," Applied Energy, Elsevier, vol. 131(C), pages 288-296.
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    Cited by:

    1. Mahdi, Fahad Parvez & Vasant, Pandian & Kallimani, Vish & Watada, Junzo & Fai, Patrick Yeoh Siew & Abdullah-Al-Wadud, M., 2018. "A holistic review on optimization strategies for combined economic emission dispatch problem," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 3006-3020.
    2. Chen, Yihsu & Zhang, Duan & Takashima, Ryuta, 2019. "Carbon emission forensic in the energy sector: Is it worth the effort?," Energy Policy, Elsevier, vol. 128(C), pages 868-878.
    3. Guoqiang Sun & Tong Chen & Zhinong Wei & Yonghui Sun & Haixiang Zang & Sheng Chen, 2016. "A Carbon Price Forecasting Model Based on Variational Mode Decomposition and Spiking Neural Networks," Energies, MDPI, vol. 9(1), pages 1-16, January.
    4. Chao Yang & Heyang Sun & Tong Li & Hengji Xie & Zhenjiang Lei & Jinliang Song & He Cai & Jiaxuan Yang & Gangjun Gong & Shuai Ren, 2022. "Coupled Model and Node Importance Evaluation of Electric Power Cyber-Physical Systems Considering Carbon Power Flow," Energies, MDPI, vol. 15(21), pages 1-21, November.

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