IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i6p3645-d775447.html
   My bibliography  Save this article

A Two-Stage Planning Optimization Study of an Integrated Energy System Considering Uncertainty

Author

Listed:
  • Lijun Tang

    (School of Energy and Power Engineering, Chongqing University, Chongqing 400044, China
    Electric Power Research Institute, Yunnan Electric Power Grid Co., Ltd., Kunming 650217, China)

  • Xiaolong Gou

    (School of Energy and Power Engineering, Chongqing University, Chongqing 400044, China)

  • Junyu Liang

    (Electric Power Research Institute, Yunnan Electric Power Grid Co., Ltd., Kunming 650217, China)

  • Yang Yang

    (Electric Power Research Institute, Yunnan Electric Power Grid Co., Ltd., Kunming 650217, China)

  • Xingyu Yuan

    (Electric Power Research Institute, Yunnan Electric Power Grid Co., Ltd., Kunming 650217, China)

  • Jiaquan Yang

    (Electric Power Research Institute, Yunnan Electric Power Grid Co., Ltd., Kunming 650217, China)

  • Yuting Yan

    (Electric Power Research Institute, Yunnan Electric Power Grid Co., Ltd., Kunming 650217, China)

  • Dada Wang

    (Electric Power Research Institute, Yunnan Electric Power Grid Co., Ltd., Kunming 650217, China)

  • Yongli Wang

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Xin Chen

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Bo Yuan

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Siyi Tao

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

Abstract

In the context of a rapidly evolving integrated energy system (IES), taking into account the uncertainty of the renewable energy output can make integrated energy system planning more realistic. In view of this, this paper proposes an integrated energy system planning approach that takes uncertainty into account. Firstly, in order to accurately describe the renewable energy output scenarios, this paper describes the IES model and introduces the scenario analysis methods used. Secondly, an integrated energy system equipment output model is constructed, the corresponding constraints and objective functions are set, an improved particle swarm-ant colony optimization algorithm is used for the solution, and a planning solution strategy considering uncertainty is proposed. Finally, the above conclusions are verified by the actual case data of a park, and the results show that the method proposed in this paper is economical and reasonable.

Suggested Citation

  • Lijun Tang & Xiaolong Gou & Junyu Liang & Yang Yang & Xingyu Yuan & Jiaquan Yang & Yuting Yan & Dada Wang & Yongli Wang & Xin Chen & Bo Yuan & Siyi Tao, 2022. "A Two-Stage Planning Optimization Study of an Integrated Energy System Considering Uncertainty," Sustainability, MDPI, vol. 14(6), pages 1-22, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:6:p:3645-:d:775447
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/6/3645/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/6/3645/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Yongli & Huang, Feifei & Tao, Siyi & Ma, Yang & Ma, Yuze & Liu, Lin & Dong, Fugui, 2022. "Multi-objective planning of regional integrated energy system aiming at exergy efficiency and economy," Applied Energy, Elsevier, vol. 306(PB).
    2. Shixiong Qi & Xiuli Wang & Xue Li & Tao Qian & Qiwen Zhang, 2019. "Enhancing Integrated Energy Distribution System Resilience through a Hierarchical Management Strategy in District Multi-Energy Systems," Sustainability, MDPI, vol. 11(15), pages 1-20, July.
    3. Yiqi Li & Jing Zhang & Zhoujun Ma & Yang Peng & Shuwen Zhao, 2021. "An Energy Management Optimization Method for Community Integrated Energy System Based on User Dominated Demand Side Response," Energies, MDPI, vol. 14(15), pages 1-22, July.
    4. Kang Qian & Tong Lv & Yue Yuan, 2021. "Integrated Energy System Planning Optimization Method and Case Analysis Based on Multiple Factors and A Three-Level Process," Sustainability, MDPI, vol. 13(13), pages 1-22, July.
    5. Zhang, Yachao & Xie, Shiwei & Shu, Shengwen, 2022. "Multi-stage robust optimization of a multi-energy coupled system considering multiple uncertainties," Energy, Elsevier, vol. 238(PC).
    6. Shahryari, E. & Shayeghi, H. & Mohammadi-ivatloo, B. & Moradzadeh, M., 2019. "A copula-based method to consider uncertainties for multi-objective energy management of microgrid in presence of demand response," Energy, Elsevier, vol. 175(C), pages 879-890.
    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. Lucio Laureti & Alessandro Massaro & Alberto Costantiello & Angelo Leogrande, 2023. "The Impact of Renewable Electricity Output on Sustainability in the Context of Circular Economy: A Global Perspective," Sustainability, MDPI, vol. 15(3), pages 1-29, January.
    2. Zhang, Qian & Qi, Jingwen & Zhen, Lu, 2023. "Optimization of integrated energy system considering multi-energy collaboration in carbon-free hydrogen port," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 180(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. Xu, Jing & Wang, Xiaoying & Gu, Yujiong & Ma, Suxia, 2023. "A data-based day-ahead scheduling optimization approach for regional integrated energy systems with varying operating conditions," Energy, Elsevier, vol. 283(C).
    2. Wang, Yongli & Guo, Lu & Wang, Yanan & Zhang, Yunfei & Zhang, Siwen & Liu, Zeqiang & Xing, Juntai & Liu, Ximei, 2024. "Bi-level programming optimization method of rural integrated energy system based on coupling coordination degree of energy equipment," Energy, Elsevier, vol. 298(C).
    3. Jiyong Li & Zeyi Hua & Lin Tian & Peiwen Chen & Hao Dong, 2024. "Optimal Capacity Allocation for Life Cycle Multiobjective Integrated Energy Systems Considering Capacity Tariffs and Eco-Indicator 99," Sustainability, MDPI, vol. 16(20), pages 1-22, October.
    4. Qiuyi Hong & Fanlin Meng & Jian Liu, 2023. "Customised Multi-Energy Pricing: Model and Solutions," Energies, MDPI, vol. 16(4), pages 1-31, February.
    5. Wang, Yubin & Dong, Wei & Yang, Qiang, 2022. "Multi-stage optimal energy management of multi-energy microgrid in deregulated electricity markets," Applied Energy, Elsevier, vol. 310(C).
    6. Wang, Qi & Huang, Chunyi & Wang, Chengmin & Li, Kangping & Xie, Ning, 2024. "Joint optimization of bidding and pricing strategy for electric vehicle aggregator considering multi-agent interactions," Applied Energy, Elsevier, vol. 360(C).
    7. Zhang, Liu & Zheng, Zhong & Chai, Yi & Zhang, Kaitian & Lian, Xiaoyuan & Zhang, Kai & Zhao, Liuqiang, 2024. "Enhancing robustness: Multi-stage adaptive robust scheduling of oxygen systems in steel enterprises under demand uncertainty," Applied Energy, Elsevier, vol. 359(C).
    8. Zhao, Naixin & Gu, Wenbo & Zheng, Zipeng & Ma, Tao, 2023. "Multi-objective bi-level planning of the integrated energy system considering uncertain user loads and carbon emission during the equipment manufacturing process," Renewable Energy, Elsevier, vol. 216(C).
    9. Zhu, Jizhong & Dong, Hanjiang & Zheng, Weiye & Li, Shenglin & Huang, Yanting & Xi, Lei, 2022. "Review and prospect of data-driven techniques for load forecasting in integrated energy systems," Applied Energy, Elsevier, vol. 321(C).
    10. Yang, Xiaohui & Wang, Xiaopeng & Deng, Yeheng & Mei, Linghao & Deng, Fuwei & Zhang, Zhonglian, 2023. "Integrated energy system scheduling model based on non-complete interval multi-objective fuzzy optimization," Renewable Energy, Elsevier, vol. 218(C).
    11. Chen, Xiaodong & Ge, Xinxin & Sun, Rongfu & Wang, Fei & Mi, Zengqiang, 2024. "A SVM based demand response capacity prediction model considering internal factors under composite program," Energy, Elsevier, vol. 300(C).
    12. Zhang, Y.Q. & Chen, J.J. & Wang, Y.X. & Feng, L., 2024. "Enhancing resilience of agricultural microgrid through electricity–heat–water based multi-energy hub considering irradiation intensity uncertainty," Renewable Energy, Elsevier, vol. 220(C).
    13. Tan, Mao & Li, Zibin & Su, Yongxin & Ren, Yuling & Wang, Ling & Wang, Rui, 2024. "Dual time-scale robust optimization for energy management of distributed energy community considering source-load uncertainty," Renewable Energy, Elsevier, vol. 226(C).
    14. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N., 2022. "Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    15. Masoud Agabalaye-Rahvar & Amin Mansour-Saatloo & Mohammad Amin Mirzaei & Behnam Mohammadi-Ivatloo & Kazem Zare & Amjad Anvari-Moghaddam, 2020. "Robust Optimal Operation Strategy for a Hybrid Energy System Based on Gas-Fired Unit, Power-to-Gas Facility and Wind Power in Energy Markets," Energies, MDPI, vol. 13(22), pages 1-21, November.
    16. Seshu Kumar, R. & Phani Raghav, L. & Koteswara Raju, D. & Singh, Arvind R., 2021. "Impact of multiple demand side management programs on the optimal operation of grid-connected microgrids," Applied Energy, Elsevier, vol. 301(C).
    17. Roy, Nibir Baran & Das, Debapriya, 2024. "Stochastic power allocation of distributed tri-generation plants and energy storage units in a zero bus microgrid with electric vehicles and demand response," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    18. Zhu, Yansong & Liu, Jizhen & Hu, Yong & Xie, Yan & Zeng, Deliang & Li, Ruilian, 2024. "Distributionally robust optimization model considering deep peak shaving and uncertainty of renewable energy," Energy, Elsevier, vol. 288(C).
    19. Zhao, Xiangming & Guo, Jianxiang & He, Maogang, 2023. "Multi-objective optimization and improvement of multi-energy combined cooling, heating and power system based on system simplification," Renewable Energy, Elsevier, vol. 217(C).
    20. Davide Di Battista & Chiara Barchiesi & Luca Di Paolo & Simona Abbate & Sara Sorvillo & Andrea Cinocca & Roberto Carapellucci & Dario Ciamponi & Dina Cardone & Salvatore Corroppolo & Roberto Cipollone, 2021. "The Reporting of Sustainable Energy Action Plans of Municipalities: Methodology and Results of Case Studies from the Abruzzo Region," Energies, MDPI, vol. 14(18), pages 1-17, September.

    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:jsusta:v:14:y:2022:i:6:p:3645-:d:775447. 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.