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Electricity Curtailment Cost Coupled to Operation Model Facilitates Clean Energy Accommodation in Grid-Connected System

Author

Listed:
  • Qiumei Ma

    (School of Water Resources and Hydropower Engineering, North China Electric Power University, Beijing 102206, China)

  • Yawei Zhao

    (School of Water Resources and Hydropower Engineering, North China Electric Power University, Beijing 102206, China)

  • Changming Ji

    (School of Water Resources and Hydropower Engineering, North China Electric Power University, Beijing 102206, China)

  • Yanke Zhang

    (School of Water Resources and Hydropower Engineering, North China Electric Power University, Beijing 102206, China)

  • Bo Ming

    (State Key Laboratory of Eco-Hydraulics in Northwest Arid Region of China, Xi’an University of Technology, Xi’an 710048, China)

Abstract

Electricity transmission in a grid-connected system provides an effective solution to promoting clean energy accommodation. However, with arbitrary determination in current operation models, the clean energy utilization ratio (CEUR) is not satisfactory largely due to the lack of electricity curtailment (the electricity equivalent of clean energy curtailment) cost-dependent optimization. In this study, a curtailment cost-dependent multi-objective operation (CCMO) model was proposed to complementarily operate a grid-connected hybrid energy system, identify optimal CEUR, and thus maximally reduce electricity curtailment. The CCMO model centers on coupling the punishment cost of electricity curtailment with the multi-objective function defined as the total cost of each grid component. The CCMO model was solved to derive the optimal equilibrium solution determined based on multiple non-dominated solutions. A grid-connected hybrid energy system including the Yunnan, Guangdong, and Guangxi Power Grids was used to test the model performance. The results showed that the CCMO model’s CEUR was up to 100% at hourly scale and 96.9% on daily average, which were both significantly higher than those in the current operation models. Furthermore, the CCMO’s optimal equilibrium solution, i.e., respective minimum total cost of each grid component, can also identify optimal transmission schemes of the daily channel utilization to make the peak utilization hours largest.

Suggested Citation

  • Qiumei Ma & Yawei Zhao & Changming Ji & Yanke Zhang & Bo Ming, 2021. "Electricity Curtailment Cost Coupled to Operation Model Facilitates Clean Energy Accommodation in Grid-Connected System," Energies, MDPI, vol. 14(10), pages 1-21, May.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:10:p:2802-:d:554054
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    References listed on IDEAS

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    1. Liu, Benxi & Liao, Shengli & Cheng, Chuntian & Chen, Fu & Li, Weidong, 2018. "Hydropower curtailment in Yunnan Province, southwestern China: Constraint analysis and suggestions," Renewable Energy, Elsevier, vol. 121(C), pages 700-711.
    2. He, Yongxiu & Xu, Yang & Pang, Yuexia & Tian, Huiying & Wu, Rui, 2016. "A regulatory policy to promote renewable energy consumption in China: Review and future evolutionary path," Renewable Energy, Elsevier, vol. 89(C), pages 695-705.
    3. Kris De Decker, 2020. "Reorienting the Economy to the Rhythms of Nature: Learning to Live with Intermittent Energy Supply," American Journal of Economics and Sociology, Wiley Blackwell, vol. 79(3), pages 877-905, May.
    4. Cui, Qi & He, Ling & Han, Guoyi & Chen, Hao & Cao, Juanjuan, 2020. "Review on climate and water resource implications of reducing renewable power curtailment in China: A nexus perspective," Applied Energy, Elsevier, vol. 267(C).
    5. Lee, Jung Wan, 2013. "The contribution of foreign direct investment to clean energy use, carbon emissions and economic growth," Energy Policy, Elsevier, vol. 55(C), pages 483-489.
    6. Zheng-Xin Wang, 2015. "A Predictive Analysis of Clean Energy Consumption, Economic Growth and Environmental Regulation in China Using an Optimized Grey Dynamic Model," Computational Economics, Springer;Society for Computational Economics, vol. 46(3), pages 437-453, October.
    7. Wang, Xuebin & Chang, Jianxia & Meng, Xuejiao & Wang, Yimin, 2018. "Short-term hydro-thermal-wind-photovoltaic complementary operation of interconnected power systems," Applied Energy, Elsevier, vol. 229(C), pages 945-962.
    8. Silva Herran, Diego & Tachiiri, Kaoru & Matsumoto, Ken'ichi, 2019. "Global energy system transformations in mitigation scenarios considering climate uncertainties," Applied Energy, Elsevier, vol. 243(C), pages 119-131.
    9. Kaundinya, Deepak Paramashivan & Balachandra, P. & Ravindranath, N.H., 2009. "Grid-connected versus stand-alone energy systems for decentralized power--A review of literature," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 2041-2050, October.
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