IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i23p7930-d688385.html
   My bibliography  Save this article

Research on a Bi-Level Collaborative Optimization Method for Planning and Operation of Multi-Energy Complementary Systems

Author

Listed:
  • Changrong Liu

    (School of Energy Science and Engineering, Central South University, Changsha 410083, China
    School of Civil Engineering, Hunan University of Technology, Zhuzhou 412007, China)

  • Hanqing Wang

    (School of Energy Science and Engineering, Central South University, Changsha 410083, China
    School of Civil Engineering, Central South University of Forestry and Technology, Changsha 410004, China)

  • Zhiqiang Liu

    (School of Energy Science and Engineering, Central South University, Changsha 410083, China)

  • Zhiyong Wang

    (School of Civil Engineering, Hunan University of Technology, Zhuzhou 412007, China)

  • Sheng Yang

    (School of Energy Science and Engineering, Central South University, Changsha 410083, China)

Abstract

Multi-energy complementary systems (MCSs) are complex multilevel systems. In the process of system planning, many aspects—such as power planning, investment cost, and environmental impact—should be considered. However, different decision makers tend to have different levels of control objectives, and the multilevel problems of the system need to be solved effectively with comprehensive judgment. Therefore, based on the terminal MCS energy structure model, the optimization method of MCS planning and operation coordination, considering the influence of planning and operation in the system’s life cycle, is studied in this paper. Consequently, the research on the collaborative optimization strategy of MCS construction and operation was carried out based on the bi-level multi-objective optimization theory in this paper. Considering the mutual restraint and correlation between system construction and operation in practical engineering, a bi-level optimization model for collaborative optimization of MCS construction and operation was constructed. To solve the model effectively, the existing non-dominated sorting genetic algorithm III (NSGA-III) was improved by the authors on the basis of previous research, which could enhance the global search ability and convergence speed of the algorithm. To effectively improve and strengthen the reliability of energy supply, and increase the comprehensive energy utilization of the system, the effects of carbon transaction cost and renewable energy penetration were considered in the optimization process. Based on an engineering example, the bi-level model was solved and analyzed. It should be noted that the optimization results of the model were verified to be applicable and effective by comparison with the single-level multi-objective programming optimization. The findings of this paper could provide theoretical reference and practical guidance for the planning and operation of MCSs, making them significant for social application.

Suggested Citation

  • Changrong Liu & Hanqing Wang & Zhiqiang Liu & Zhiyong Wang & Sheng Yang, 2021. "Research on a Bi-Level Collaborative Optimization Method for Planning and Operation of Multi-Energy Complementary Systems," Energies, MDPI, vol. 14(23), pages 1-20, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:7930-:d:688385
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/23/7930/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/23/7930/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yuehao Zhao & Ke Peng & Bingyin Xu & Huimin Li & Yuquan Liu & Xinhui Zhang, 2018. "Bilevel Optimal Dispatch Strategy for a Multi-Energy System of Industrial Parks by Considering Integrated Demand Response," Energies, MDPI, vol. 11(8), pages 1-21, July.
    2. Wakui, Tetsuya & Hashiguchi, Moe & Sawada, Kento & Yokoyama, Ryohei, 2019. "Two-stage design optimization based on artificial immune system and mixed-integer linear programming for energy supply networks," Energy, Elsevier, vol. 170(C), pages 1228-1248.
    3. Yu Huang & Shuqin Li & Peng Ding & Yan Zhang & Kai Yang & Weiting Zhang, 2019. "Optimal Operation for Economic and Exergetic Objectives of a Multiple Energy Carrier System Considering Demand Response Program," Energies, MDPI, vol. 12(20), pages 1-21, October.
    4. Yuhong Wang & Xu Zhou & Yunxiang Shi & Zongsheng Zheng & Qi Zeng & Lei Chen & Bo Xiang & Rui Huang, 2021. "Transmission Network Expansion Planning Considering Wind Power and Load Uncertainties Based on Multi-Agent DDQN," Energies, MDPI, vol. 14(19), pages 1-28, September.
    5. G. S. Liu & J. Y. Han & J. Z. Zhang, 2001. "Exact Penalty Functions for Convex Bilevel Programming Problems," Journal of Optimization Theory and Applications, Springer, vol. 110(3), pages 621-643, September.
    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. Li, Ye & Liu, Zihan & Sang, Yufeng & Hu, Jingfan & Li, Bojia & Zhang, Xinyu & Jurasz, Jakub & Zheng, Wandong, 2023. "Optimization of integrated energy system for low-carbon community considering the feasibility and application limitation," Applied Energy, Elsevier, vol. 348(C).
    2. Yuan, Yi & Ding, Tao & Chang, Xinyue & Jia, Wenhao & Xue, Yixun, 2024. "A distributed multi-objective optimization method for scheduling of integrated electricity and hydrogen systems," Applied Energy, Elsevier, vol. 355(C).

    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. Xinghua Wang & Fucheng Zhong & Yilin Xu & Xixian Liu & Zezhong Li & Jianan Liu & Zhuoli Zhao, 2023. "Extraction and Joint Method of PV–Load Typical Scenes Considering Temporal and Spatial Distribution Characteristics," Energies, MDPI, vol. 16(18), pages 1-19, September.
    2. Mohamed M. Refaat & Shady H. E. Abdel Aleem & Yousry Atia & Ziad M. Ali & Adel El-Shahat & Mahmoud M. Sayed, 2021. "A Mathematical Approach to Simultaneously Plan Generation and Transmission Expansion Based on Fault Current Limiters and Reliability Constraints," Mathematics, MDPI, vol. 9(21), pages 1-21, November.
    3. Zhiqing Meng & Chuangyin Dang & Rui Shen & Ming Jiang, 2012. "An Objective Penalty Function of Bilevel Programming," Journal of Optimization Theory and Applications, Springer, vol. 153(2), pages 377-387, May.
    4. Gu, Haifei & Li, Yang & Yu, Jie & Wu, Chen & Song, Tianli & Xu, Jinzhou, 2020. "Bi-level optimal low-carbon economic dispatch for an industrial park with consideration of multi-energy price incentives," Applied Energy, Elsevier, vol. 262(C).
    5. Marina Prechtel & Günter Leugering & Paul Steinmann & Michael Stingl, 2012. "Optimal Design of Brittle Composite Materials: a Nonsmooth Approach," Journal of Optimization Theory and Applications, Springer, vol. 155(3), pages 962-985, December.
    6. Xia, Tian & Huang, Wujing & Lu, Xi & Zhang, Ning & Kang, Chongqing, 2020. "Planning district multiple energy systems considering year-round operation," Energy, Elsevier, vol. 213(C).
    7. Dezhou Kong & Jianru Jing & Tingyue Gu & Xuanyue Wei & Xingning Sa & Yimin Yang & Zhiang Zhang, 2023. "Theoretical Analysis of Integrated Community Energy Systems (ICES) Considering Integrated Demand Response (IDR): A Review of the System Modelling and Optimization," Energies, MDPI, vol. 16(10), pages 1-22, May.
    8. Wakui, Tetsuya & Hashiguchi, Moe & Yokoyama, Ryohei, 2021. "Structural design of distributed energy networks by a hierarchical combination of variable- and constraint-based decomposition methods," Energy, Elsevier, vol. 224(C).
    9. Kirchem, Dana & Lynch, Muireann Á. & Bertsch, Valentin & Casey, Eoin, 2020. "Modelling demand response with process models and energy systems models: Potential applications for wastewater treatment within the energy-water nexus," Applied Energy, Elsevier, vol. 260(C).
    10. Li, Haoran & Zhang, Chenghui & Sun, Bo, 2022. "Deep integration planning of sustainable energies in district energy system and distributed energy station," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    11. Tiago Pinto, 2023. "Artificial Intelligence as a Booster of Future Power Systems," Energies, MDPI, vol. 16(5), pages 1-4, February.
    12. Tostado-Véliz, Marcos & Rezaee Jordehi, Ahmad & Mansouri, Seyed Amir & Zhou, Yuekuan & Jurado, Francisco, 2024. "A local electricity-hydrogen market model for industrial parks," Applied Energy, Elsevier, vol. 360(C).
    13. Gabriel Pesántez & Wilian Guamán & José Córdova & Miguel Torres & Pablo Benalcazar, 2024. "Reinforcement Learning for Efficient Power Systems Planning: A Review of Operational and Expansion Strategies," Energies, MDPI, vol. 17(9), pages 1-25, May.
    14. Kong, Xiangyu & Liu, Dehong & Xiao, Jie & Wang, Chengshan, 2019. "A multi-agent optimal bidding strategy in microgrids based on artificial immune system," Energy, Elsevier, vol. 189(C).
    15. Wakui, Tetsuya & Hashiguchi, Moe & Yokoyama, Ryohei, 2020. "A near-optimal solution method for coordinated operation planning problem of power- and heat-interchange networks using column generation-based decomposition," Energy, Elsevier, vol. 197(C).
    16. Li, Rong & Guo, Su & Yang, Yong & Liu, Deyou, 2020. "Optimal sizing of wind/ concentrated solar plant/ electric heater hybrid renewable energy system based on two-stage stochastic programming," Energy, Elsevier, vol. 209(C).
    17. Dai, Wei & Yang, Zhifang & Yu, Juan & Cui, Wei & Li, Wenyuan & Li, Jinghua & Liu, Hui, 2021. "Economic dispatch of interconnected networks considering hidden flexibility," Energy, Elsevier, vol. 223(C).
    18. Yu Huang & Weizhen Hou & Yiran Huang & Jiayu Li & Qixian Li & Dongfeng Wang & Yan Zhang, 2020. "Multi-Objective Optimal Operation for Steam Power Scheduling Based on Economic and Exergetic Analysis," Energies, MDPI, vol. 13(8), pages 1-18, April.
    19. Morteza Vahid-Ghavidel & Mohammad Sadegh Javadi & Matthew Gough & Sérgio F. Santos & Miadreza Shafie-khah & João P.S. Catalão, 2020. "Demand Response Programs in Multi-Energy Systems: A Review," Energies, MDPI, vol. 13(17), pages 1-17, August.
    20. Wakui, Tetsuya & Sawada, Kento & Yokoyama, Ryohei & Aki, Hirohisa, 2019. "Predictive management for energy supply networks using photovoltaics, heat pumps, and battery by two-stage stochastic programming and rule-based control," Energy, Elsevier, vol. 179(C), pages 1302-1319.

    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:14:y:2021:i:23:p:7930-:d:688385. 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.