IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i1p245-d1312386.html
   My bibliography  Save this article

Low-Carbon Optimal Scheduling of Integrated Energy System Considering Multiple Uncertainties and Electricity–Heat Integrated Demand Response

Author

Listed:
  • Hongwei Li

    (School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China)

  • Xingmin Li

    (Baiyin Power Supply Company of State Grid Gansu Power Supply Company, Baiyin 730900, China)

  • Siyu Chen

    (School of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China)

  • Shuaibing Li

    (School of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China)

  • Yongqiang Kang

    (School of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China)

  • Xiping Ma

    (Electric Power Research Institute of State Grid Gansu Electric Power Company, Lanzhou 730070, China)

Abstract

To realize the low-carbon operation of integrated energy systems (IESs), this paper proposes a low-carbon optimal scheduling method. First of all, considering the integrated demand response of price-based electricity and heating, an economic scheduling model of the IES integrated demand response based on chance-constrained programming is proposed to minimize the integrated operating cost in an uncertain environment. Through the comprehensive demand response model, the impact of the demand response ratio on the operating economy of the IES is explored. Afterward, the carbon emission index is introduced, and gas turbines and energy storage devices are used as the actuators of multi-energy coupling to further explore the potential interactions between the coupling capacities of various heterogeneous energy sources and carbon emissions. Finally, the original uncertainty model is transformed into a mixed-integer linear-programming model and solved using sequence operation theory and the linearization method. The results show that the operating economy of the IES is improved by coordinating the uncertainty of the integrated demand response and renewable energy. In addition, the tradeoff between the working economy and reliability of the EIS can be balanced via the setting of an appropriate confidence level for the opportunity constraints.

Suggested Citation

  • Hongwei Li & Xingmin Li & Siyu Chen & Shuaibing Li & Yongqiang Kang & Xiping Ma, 2024. "Low-Carbon Optimal Scheduling of Integrated Energy System Considering Multiple Uncertainties and Electricity–Heat Integrated Demand Response," Energies, MDPI, vol. 17(1), pages 1-20, January.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:1:p:245-:d:1312386
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/1/245/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/1/245/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Chengshan & Jiao, Bingqi & Guo, Li & Tian, Zhe & Niu, Jide & Li, Siwei, 2016. "Robust scheduling of building energy system under uncertainty," Applied Energy, Elsevier, vol. 167(C), pages 366-376.
    2. Boßmann, Tobias & Eser, Eike Johannes, 2016. "Model-based assessment of demand-response measures—A comprehensive literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1637-1656.
    3. Wang, Dan & Hu, Qing'e & Jia, Hongjie & Hou, Kai & Du, Wei & Chen, Ning & Wang, Xudong & Fan, Menghua, 2019. "Integrated demand response in district electricity-heating network considering double auction retail energy market based on demand-side energy stations," Applied Energy, Elsevier, vol. 248(C), pages 656-678.
    4. Guevara, Esnil & Babonneau, Fréderic & Homem-de-Mello, Tito & Moret, Stefano, 2020. "A machine learning and distributionally robust optimization framework for strategic energy planning under uncertainty," Applied Energy, Elsevier, vol. 271(C).
    5. Kong, Xiangyu & Xiao, Jie & Liu, Dehong & Wu, Jianzhong & Wang, Chengshan & Shen, Yu, 2020. "Robust stochastic optimal dispatching method of multi-energy virtual power plant considering multiple uncertainties," Applied Energy, Elsevier, vol. 279(C).
    6. Li, Junkai & Ge, Shaoyun & Zhang, Shida & Xu, Zhengyang & Wang, Liyong & Wang, Chengshan & Liu, Hong, 2022. "A multi-objective stochastic-information gap decision model for soft open points planning considering power fluctuation and growth uncertainty," Applied Energy, Elsevier, vol. 317(C).
    7. Mazzoni, Stefano & Sze, Jia Yin & Nastasi, Benedetto & Ooi, Sean & Desideri, Umberto & Romagnoli, Alessandro, 2021. "A techno-economic assessment on the adoption of latent heat thermal energy storage systems for district cooling optimal dispatch & operations," Applied Energy, Elsevier, vol. 289(C).
    Full references (including those not matched with items on IDEAS)

    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. Qiu, Haifeng & Gu, Wei & Liu, Pengxiang & Sun, Qirun & Wu, Zhi & Lu, Xi, 2022. "Application of two-stage robust optimization theory in power system scheduling under uncertainties: A review and perspective," Energy, Elsevier, vol. 251(C).
    2. Lv, Chaoxian & Liang, Rui & Jin, Wei & Chai, Yuanyuan & Yang, Tiankai, 2022. "Multi-stage resilience scheduling of electricity-gas integrated energy system with multi-level decentralized reserve," Applied Energy, Elsevier, vol. 317(C).
    3. McPherson, Madeleine & Stoll, Brady, 2020. "Demand response for variable renewable energy integration: A proposed approach and its impacts," Energy, Elsevier, vol. 197(C).
    4. Majdalani, Naim & Aelenei, Daniel & Lopes, Rui Amaral & Silva, Carlos Augusto Santo, 2020. "The potential of energy flexibility of space heating and cooling in Portugal," Utilities Policy, Elsevier, vol. 66(C).
    5. Zhang, Jiyuan & Tang, Hailong & Chen, Min, 2019. "Linear substitute model-based uncertainty analysis of complicated non-linear energy system performance (case study of an adaptive cycle engine)," Applied Energy, Elsevier, vol. 249(C), pages 87-108.
    6. Navid Shirzadi & Hadise Rasoulian & Fuzhan Nasiri & Ursula Eicker, 2022. "Resilience Enhancement of an Urban Microgrid during Off-Grid Mode Operation Using Critical Load Indicators," Energies, MDPI, vol. 15(20), pages 1-15, October.
    7. Li, Yang & Wang, Bin & Yang, Zhen & Li, Jiazheng & Chen, Chen, 2022. "Hierarchical stochastic scheduling of multi-community integrated energy systems in uncertain environments via Stackelberg game," Applied Energy, Elsevier, vol. 308(C).
    8. Liu, Liu & Niu, Jianlei & Wu, Jian-Yong, 2023. "Improving energy efficiency of photovoltaic/thermal systems by cooling with PCM nano-emulsions: An indoor experimental study," Renewable Energy, Elsevier, vol. 203(C), pages 568-582.
    9. Ahmad, Tanveer & Madonski, Rafal & Zhang, Dongdong & Huang, Chao & Mujeeb, Asad, 2022. "Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    10. Maurer, Jona & Tschuch, Nicolai & Krebs, Stefan & Bhattacharya, Kankar & Cañizares, Claudio & Hohmann, Sören, 2023. "Toward transactive control of coupled electric power and district heating networks," Applied Energy, Elsevier, vol. 332(C).
    11. Zhang, Liu & Zhang, Kaitian & Zheng, Zhong & Chai, Yi & Lian, Xiaoyuan & Zhang, Kai & Xu, Zhaojun & Chen, Sujun, 2023. "Two-stage distributionally robust integrated scheduling of oxygen distribution and steelmaking-continuous casting in steel enterprises," Applied Energy, Elsevier, vol. 351(C).
    12. Zheng, Ling & Zhou, Bin & Cao, Yijia & Wing Or, Siu & Li, Yong & Wing Chan, Ka, 2022. "Hierarchical distributed multi-energy demand response for coordinated operation of building clusters," Applied Energy, Elsevier, vol. 308(C).
    13. Liu, Zhiqiang & Cui, Yanping & Wang, Jiaqiang & Yue, Chang & Agbodjan, Yawovi Souley & Yang, Yu, 2022. "Multi-objective optimization of multi-energy complementary integrated energy systems considering load prediction and renewable energy production uncertainties," Energy, Elsevier, vol. 254(PC).
    14. Ali Darudi & Hannes Weigt, 2024. "Review and Assessment of Decarbonized Future Electricity Markets," Energies, MDPI, vol. 17(18), pages 1-38, September.
    15. Ju, Liwei & Yin, Zhe & Lu, Xiaolong & Yang, Shenbo & Li, Peng & Rao, Rao & Tan, Zhongfu, 2022. "A Tri-dimensional Equilibrium-based stochastic optimal dispatching model for a novel virtual power plant incorporating carbon Capture, Power-to-Gas and electric vehicle aggregator," Applied Energy, Elsevier, vol. 324(C).
    16. Dorotić, Hrvoje & Ban, Marko & Pukšec, Tomislav & Duić, Neven, 2020. "Impact of wind penetration in electricity markets on optimal power-to-heat capacities in a local district heating system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    17. Kang, Jidong & Wu, Zhuochun & Ng, Tsan Sheng & Su, Bin, 2023. "A stochastic-robust optimization model for inter-regional power system planning," European Journal of Operational Research, Elsevier, vol. 310(3), pages 1234-1248.
    18. 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).
    19. Ji, Ling & Zhang, Bei-Bei & Huang, Guo-He & Xie, Yu-Lei & Niu, Dong-Xiao, 2018. "GHG-mitigation oriented and coal-consumption constrained inexact robust model for regional energy structure adjustment – A case study for Jiangsu Province, China," Renewable Energy, Elsevier, vol. 123(C), pages 549-562.
    20. Michał Jasiński & Tomasz Sikorski & Dominika Kaczorowska & Jacek Rezmer & Vishnu Suresh & Zbigniew Leonowicz & Paweł Kostyła & Jarosław Szymańda & Przemysław Janik & Jacek Bieńkowski & Przemysław Prus, 2021. "A Case Study on a Hierarchical Clustering Application in a Virtual Power Plant: Detection of Specific Working Conditions from Power Quality Data," Energies, MDPI, vol. 14(4), pages 1-13, February.

    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:17:y:2024:i:1:p:245-:d:1312386. 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.