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Probability-driven transmission expansion planning with high-penetration renewable power generation: A case study in northwestern China

Author

Listed:
  • Liang, Z.
  • Chen, H.
  • Chen, S.
  • Lin, Z.
  • Kang, C.

Abstract

The incorporation of power generation derived from renewable energy sources, or renewable power generation (RPG), into conventional electric power grids has been rapidly increasing on a large scale in recent years. However, this process can be expected to inevitably increase the uncertainty associated with transmission line investment owing to the inherent uncertainty associated with RPG. While robust transmission expansion planning (RTEP) has been commonly employed for optimizing transmission line investments, this method suffers from serious disadvantages such as the neglect of available RPG probability information, overly conservative solutions, and exceedingly time consuming solution processes. The present work addresses these disadvantages by modeling the probability of RPG uncertainty according to RPG output probabilities obtained over a long-term planning horizon based on available historical data using a hybrid probability uncertainty set constructed using 1-norm and ∞-norm metrics. A probability-driven RTEP model is then proposed to obtain an optimal investment strategy that provides a security guarantee under the worst-case RPG probability distribution, while alleviating the need for overly conservative solutions with high total expansion costs. In addition, a modified column-and-constraint generation algorithm is developed to solve the proposed tri-level probability-driven RTEP model, where the large-scale inner bi-level component of the optimization problem is decomposed into several small-scale linear models that can be solved in parallel. The proposed algorithm requires no dual variables in the inner problem and eliminates all highly non-convex bilinear terms like those obtained in conventional RTEP solution algorithms. This can effectively increase the computational speed of the solution process. The effectiveness, good applicability, and robustness of the proposed model and solution algorithm are demonstrated by numerical applications based on a Garver’s 6-bus test system and an existing electric grid system in northwestern China.

Suggested Citation

  • Liang, Z. & Chen, H. & Chen, S. & Lin, Z. & Kang, C., 2019. "Probability-driven transmission expansion planning with high-penetration renewable power generation: A case study in northwestern China," Applied Energy, Elsevier, vol. 255(C).
    • Handle: RePEc:eee:appene:v:255:y:2019:i:c:s030626191931284x
    DOI: 10.1016/j.apenergy.2019.113610
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    Citations

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    Cited by:

    1. Ranjbar, Hossein & Kazemi, Mostafa & Amjady, Nima & Zareipour, Hamidreza & Hosseini, Seyed Hamid, 2022. "Maximizing the utilization of existing grids for renewable energy integration," Renewable Energy, Elsevier, vol. 189(C), pages 618-629.
    2. Jia, Zhijie & Wen, Shiyan & Wang, Yao, 2023. "Power coming from the sky: Economic benefits of inter-regional power transmission in China," Energy Economics, Elsevier, vol. 119(C).
    3. Tumiran & Lesnanto Multa Putranto & Roni Irnawan & Sarjiya & Adi Priyanto & Suroso Isnandar & Ira Savitri, 2021. "Transmission Expansion Planning for the Optimization of Renewable Energy Integration in the Sulawesi Electricity System," Sustainability, MDPI, vol. 13(18), pages 1-20, September.
    4. Ding, Tao & Sun, Yuge & Huang, Can & Mu, Chenlu & Fan, Yuqi & Lin, Jiang & Qin, Yining, 2022. "Pathways of clean energy heating electrification programs for reducing carbon emissions in Northwest China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(C).
    5. Moradi-Sepahvand, Mojtaba & Amraee, Turaj, 2021. "Integrated expansion planning of electric energy generation, transmission, and storage for handling high shares of wind and solar power generation," Applied Energy, Elsevier, vol. 298(C).
    6. Khalid A. Alnowibet & Ahmad M. Alshamrani & Adel F. Alrasheedi, 2023. "A Bilevel Stochastic Optimization Framework for Market-Oriented Transmission Expansion Planning Considering Market Power," Energies, MDPI, vol. 16(7), pages 1-15, April.
    7. Demetriou, E. & Hadjistassou, C., 2021. "Can China decarbonize its electricity sector?," Energy Policy, Elsevier, vol. 148(PB).
    8. Xuejun Zheng & Shaorong Wang & Zia Ullah & Mengmeng Xiao & Chang Ye & Zhangping Lei, 2021. "A Novel Optimization Method for a Multi-Year Planning Scheme of an Active Distribution Network in a Large Planning Zone," Energies, MDPI, vol. 14(12), pages 1-16, June.
    9. Wu, Yunyun & Fang, Jiakun & Ai, Xiaomeng & Xue, Xizhen & Cui, Shichang & Chen, Xia & Wen, Jinyu, 2023. "Robust co-planning of AC/DC transmission network and energy storage considering uncertainty of renewable energy," Applied Energy, Elsevier, vol. 339(C).
    10. Yi Luo & Yin Zhang & Muyi Tang & Youbin Zhou & Ying Wang & Defu Cai & Haiguang Liu, 2021. "A Novel Receiving End Grid Planning Method with Mutually Exclusive Constraints in Alternating Current/Direct Current Lines," Sustainability, MDPI, vol. 13(13), pages 1-16, June.
    11. Yin, Xin & Chen, Haoyong & Liang, Zipeng & Zhu, Yanjin, 2023. "A Flexibility-oriented robust transmission expansion planning approach under high renewable energy resource penetration," Applied Energy, Elsevier, vol. 351(C).
    12. Zhang, Xiaodong & Patino-Echeverri, Dalia & Li, Mingquan & Wu, Libo, 2022. "A review of publicly available data sources for models to study renewables integration in China's power system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).

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