IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i9p3298-d806746.html
   My bibliography  Save this article

Multiple Power Supply Capacity Planning Research for New Power System Based on Situation Awareness

Author

Listed:
  • Dahu Li

    (Hubei Collaborative Innovation Center for High-Efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan 430068, China
    State Grid Hubei Electric Power Co., Ltd., Wuhan 430077, China)

  • Xiaoda Cheng

    (Hubei Collaborative Innovation Center for High-Efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan 430068, China)

  • Leijiao Ge

    (School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China)

  • Wentao Huang

    (Hubei Collaborative Innovation Center for High-Efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan 430068, China)

  • Jun He

    (Hubei Collaborative Innovation Center for High-Efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan 430068, China)

  • Zhongwei He

    (Enshi Power Supply Company, State Grid Hubei Electric Power Co., Ltd., Enshi 445699, China)

Abstract

In the context of new power systems, reasonable capacity optimization of multiple power systems can not only reduce carbon emissions, but also improve system safety and stability. This paper proposes a situation awareness-based capacity optimization strategy for wind-photovoltaic-thermal power systems and establishes a bi-level model for system capacity optimization. The upper-level model considers environmental protection and economy, and carries out multi-objective optimization of the system capacity planning solution with the objectives of minimizing carbon emissions and total system cost over the whole life cycle of the system, further obtaining a set of capacity planning solutions based on the Pareto frontier. A Pareto optimal solution set decision method based on grey relativity analysis is proposed to quantitatively assess the comprehensive economic–environmental properties of the system. The capacity planning solutions obtained from the upper model are used as the input to the lower model. The lower model integrates system stability, environmental protection, and economy and further optimizes the set of capacity planning solutions obtained from the upper model with the objective of maximizing the inertia security region and the best comprehensive economic–environmental properties to obtain the optimal capacity planning scheme. The NSGA-II modified algorithm (improved NSGA-II algorithm based on dominant strength, INSGA2-DS) is used to solve the upper model, and the Cplex solver is called on to solve the lower model. Finally, the modified IEEE-39 node algorithm is used to verify that the optimized capacity planning scheme can effectively improve the system security and stability and reduce the carbon emissions and total system cost throughout the system life cycle.

Suggested Citation

  • Dahu Li & Xiaoda Cheng & Leijiao Ge & Wentao Huang & Jun He & Zhongwei He, 2022. "Multiple Power Supply Capacity Planning Research for New Power System Based on Situation Awareness," Energies, MDPI, vol. 15(9), pages 1-24, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3298-:d:806746
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/9/3298/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/9/3298/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yumin Xu & Yansheng Lang & Boying Wen & Xiaonan Yang, 2019. "An Innovative Planning Method for the Optimal Capacity Allocation of a Hybrid Wind–PV–Pumped Storage Power System," Energies, MDPI, vol. 12(14), pages 1-14, July.
    2. Ardente, Fulvio & Beccali, Marco & Cellura, Maurizio & Lo Brano, Valerio, 2008. "Energy performances and life cycle assessment of an Italian wind farm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(1), pages 200-217, January.
    3. Ifedayo Oladeji & Ramon Zamora & Tek Tjing Lie, 2021. "An Online Security Prediction and Control Framework for Modern Power Grids," Energies, MDPI, vol. 14(20), pages 1-27, October.
    4. Kumar, Indraneel & Tyner, Wallace E. & Sinha, Kumares C., 2016. "Input–output life cycle environmental assessment of greenhouse gas emissions from utility scale wind energy in the United States," Energy Policy, Elsevier, vol. 89(C), pages 294-301.
    5. Bowen Yang & Yougui Guo & Xi Xiao & Peigen Tian, 2020. "Bi-level Capacity Planning of Wind-PV-Battery Hybrid Generation System Considering Return on Investment," Energies, MDPI, vol. 13(12), pages 1-18, June.
    6. Niina Helistö & Juha Kiviluoma & Hannele Holttinen & Jose Daniel Lara & Bri‐Mathias Hodge, 2019. "Including operational aspects in the planning of power systems with large amounts of variable generation: A review of modeling approaches," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 8(5), September.
    7. Xu, M. & Zhuan, X., 2013. "Optimal planning for wind power capacity in an electric power system," Renewable Energy, Elsevier, vol. 53(C), pages 280-286.
    8. Jicheng Liu & Dandan He, 2018. "Profit Allocation of Hybrid Power System Planning in Energy Internet: A Cooperative Game Study," Sustainability, MDPI, vol. 10(2), pages 1-19, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Leijiao Ge & Jun Yan & Yonghui Sun & Zhongguan Wang, 2022. "Situational Awareness for Smart Distribution Systems," Energies, MDPI, vol. 15(11), pages 1-3, June.
    2. Weiqiang Qiu & Sheng Zhou & Yang Yang & Xiaoying Lv & Ting Lv & Yuge Chen & Ying Huang & Kunming Zhang & Hongfei Yu & Yunchu Wang & Yuanqian Ma & Zhenzhi Lin, 2023. "Application Prospect, Development Status and Key Technologies of Shared Energy Storage toward Renewable Energy Accommodation Scenario in the Context of China," Energies, MDPI, vol. 16(2), pages 1-21, January.
    3. Xuejun Li & Jiaxin Qian & Changhai Yang & Boyang Chen & Xiang Wang & Zongnan Jiang, 2024. "New Power System Planning and Evolution Path with Multi-Flexibility Resource Coordination," Energies, MDPI, vol. 17(1), pages 1-20, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. S. Cucurachi & E. Borgonovo & R. Heijungs, 2016. "A Protocol for the Global Sensitivity Analysis of Impact Assessment Models in Life Cycle Assessment," Risk Analysis, John Wiley & Sons, vol. 36(2), pages 357-377, February.
    3. Rashedi, A. & Sridhar, I. & Tseng, K.J., 2013. "Life cycle assessment of 50MW wind firms and strategies for impact reduction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 89-101.
    4. Alimou, Yacine & Maïzi, Nadia & Bourmaud, Jean-Yves & Li, Marion, 2020. "Assessing the security of electricity supply through multi-scale modeling: The TIMES-ANTARES linking approach," Applied Energy, Elsevier, vol. 279(C).
    5. Emblemsvåg, Jan, 2022. "Wind energy is not sustainable when balanced by fossil energy," Applied Energy, Elsevier, vol. 305(C).
    6. Niklas Andersen & Ola Eriksson & Karl Hillman & Marita Wallhagen, 2016. "Wind Turbines’ End-of-Life: Quantification and Characterisation of Future Waste Materials on a National Level," Energies, MDPI, vol. 9(12), pages 1-24, November.
    7. Zhao, Xiaoli & Cai, Qiong & Zhang, Sufang & Luo, Kaiyan, 2017. "The substitution of wind power for coal-fired power to realize China's CO2 emissions reduction targets in 2020 and 2030," Energy, Elsevier, vol. 120(C), pages 164-178.
    8. Lee, Shun-Chung & Shih, Li-Hsing, 2010. "Renewable energy policy evaluation using real option model -- The case of Taiwan," Energy Economics, Elsevier, vol. 32(Supplemen), pages 67-78, September.
    9. Botor, Benjamin & Böcker, Benjamin & Kallabis, Thomas & Weber, Christoph, 2021. "Information shocks and profitability risks for power plant investments – impacts of policy instruments," Energy Economics, Elsevier, vol. 102(C).
    10. Savino, Matteo M. & Manzini, Riccardo & Della Selva, Vincenzo & Accorsi, Riccardo, 2017. "A new model for environmental and economic evaluation of renewable energy systems: The case of wind turbines," Applied Energy, Elsevier, vol. 189(C), pages 739-752.
    11. Yang, Jin & Chen, Bin, 2016. "Energy–water nexus of wind power generation systems," Applied Energy, Elsevier, vol. 169(C), pages 1-13.
    12. Sun, Yinong & Frew, Bethany & Dalvi, Sourabh & Dhulipala, Surya C., 2022. "Insights into methodologies and operational details of resource adequacy assessment: A case study with application to a broader flexibility framework," Applied Energy, Elsevier, vol. 328(C).
    13. Olexandr Shavolkin & Iryna Shvedchykova & Michal Kolcun & Dušan Medved’, 2022. "Improvement of the Grid-Tied Solar-Wind System with a Storage Battery for the Self-Consumption of a Local Object," Energies, MDPI, vol. 15(14), pages 1-18, July.
    14. Lien-Chieh Lee & Yuan Wang & Yuanyuan Yan & Jian Zuo, 2018. "Greenhouse Gas Emissions Embodied in the Chinese International Trade of Computer Products," Sustainability, MDPI, vol. 10(5), pages 1-15, May.
    15. Dupont, Elise & Koppelaar, Rembrandt & Jeanmart, Hervé, 2018. "Global available wind energy with physical and energy return on investment constraints," Applied Energy, Elsevier, vol. 209(C), pages 322-338.
    16. Ji, Shiyu & Chen, Bin, 2016. "Carbon footprint accounting of a typical wind farm in China," Applied Energy, Elsevier, vol. 180(C), pages 416-423.
    17. Liu, Xiuli & Guo, Pibin & Yue, Xiaohang & Qi, Xiaoyan & Guo, Shufeng & Zhou, Xijun, 2021. "Measuring metabolic efficiency of the Beijing–Tianjin–Hebei urban agglomeration: A slacks-based measures method," Resources Policy, Elsevier, vol. 70(C).
    18. Dijkman, T.J. & Benders, R.M.J., 2010. "Comparison of renewable fuels based on their land use using energy densities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(9), pages 3148-3155, December.
    19. Luigi Maffei & Massimiliano Masullo & Maria Di Gabriele & Nefta-Eleftheria P. Votsi & John D. Pantis & Vincenzo Paolo Senese, 2015. "Auditory Recognition of Familiar and Unfamiliar Subjects with Wind Turbine Noise," IJERPH, MDPI, vol. 12(4), pages 1-15, April.
    20. Jadidoleslam, Morteza & Ebrahimi, Akbar & Latify, Mohammad Amin, 2017. "Probabilistic transmission expansion planning to maximize the integration of wind power," Renewable Energy, Elsevier, vol. 114(PB), pages 866-878.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3298-:d:806746. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.