IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i24p8048-d1299628.html
   My bibliography  Save this article

Adjustable Capability Evaluation of Integrated Energy Systems Considering Demand Response and Economic Constraints

Author

Listed:
  • Yang Li

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Rongqiang Li

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Linjun Shi

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Feng Wu

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Jianhua Zhou

    (State Grid Jiangsu Electric Power Company Research Institute, Nanjing 211100, China)

  • Jian Liu

    (State Grid Jiangsu Electric Power Company Research Institute, Nanjing 211100, China)

  • Keman Lin

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

Abstract

The coupling between multiple energy sources such as electricity, gas, and heat is strengthened in an integrated energy system (IES), and this, in turn, improves the operational flexibility of the IES. As an upper-level energy supply system, an IES can play a role as virtual energy storage, which can provide regulating power to smooth out the volatility from large-scale renewable energy generation. The establishment of an aggregating virtual energy storage model for IESs has become an important issue. Under this background, a multi-objective optimization-based adjustable capacity evaluation method is proposed in this paper. Firstly, the mathematical model of an IES considering the coupling of multiple kinds of energy forms is proposed. Then, an aggregating model considering demand response and economic constraints is established to demonstrate the adjustable capacity of the IES. In addition, multi-objective optimization is used to identify parameters in the proposed model, and the normal boundary intersection (NBI) method is used to solve the problem. Finally, a simulation example is provided to verify the effectiveness and feasibility of the proposed method. The external energy demand boundary of the IES can be modeled as virtual energy storage, and the coupling relations of electricity and gas can be presented. Case studies demonstrate that economic constraints narrow the adjustable capacity of the IES while the demand response extends it.

Suggested Citation

  • Yang Li & Rongqiang Li & Linjun Shi & Feng Wu & Jianhua Zhou & Jian Liu & Keman Lin, 2023. "Adjustable Capability Evaluation of Integrated Energy Systems Considering Demand Response and Economic Constraints," Energies, MDPI, vol. 16(24), pages 1-24, December.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:24:p:8048-:d:1299628
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/24/8048/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/24/8048/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Yongli & Wang, Yudong & Huang, Yujing & Yang, Jiale & Ma, Yuze & Yu, Haiyang & Zeng, Ming & Zhang, Fuwei & Zhang, Yanfu, 2019. "Operation optimization of regional integrated energy system based on the modeling of electricity-thermal-natural gas network," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    2. Li, Guoqing & Zhang, Rufeng & Jiang, Tao & Chen, Houhe & Bai, Linquan & Cui, Hantao & Li, Xiaojing, 2017. "Optimal dispatch strategy for integrated energy systems with CCHP and wind power," Applied Energy, Elsevier, vol. 192(C), pages 408-419.
    3. Jiang, Yibo & Xu, Jian & Sun, Yuanzhang & Wei, Congying & Wang, Jing & Ke, Deping & Li, Xiong & Yang, Jun & Peng, Xiaotao & Tang, Bowen, 2017. "Day-ahead stochastic economic dispatch of wind integrated power system considering demand response of residential hybrid energy system," Applied Energy, Elsevier, vol. 190(C), pages 1126-1137.
    4. Sun, Qingkai & Wang, Xiaojun & Liu, Zhao & Mirsaeidi, Sohrab & He, Jinghan & Pei, Wei, 2022. "Multi-agent energy management optimization for integrated energy systems under the energy and carbon co-trading market," Applied Energy, Elsevier, vol. 324(C).
    5. Xiang, Yue & Cai, Hanhu & Gu, Chenghong & Shen, Xiaodong, 2020. "Cost-benefit analysis of integrated energy system planning considering demand response," Energy, Elsevier, vol. 192(C).
    6. Guo, Caishan & Luo, Fengji & Cai, Zexiang & Dong, Zhao Yang, 2021. "Integrated energy systems of data centers and smart grids: State-of-the-art and future opportunities," Applied Energy, Elsevier, vol. 301(C).
    7. Pan, Zhaoguang & Guo, Qinglai & Sun, Hongbin, 2017. "Feasible region method based integrated heat and electricity dispatch considering building thermal inertia," Applied Energy, Elsevier, vol. 192(C), pages 395-407.
    8. Zhang, Yuquan & Zang, Wei & Zheng, Jinhai & Cappietti, Lorenzo & Zhang, Jisheng & Zheng, Yuan & Fernandez-Rodriguez, E., 2021. "The influence of waves propagating with the current on the wake of a tidal stream turbine," Applied Energy, Elsevier, vol. 290(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. Kong, Xiangyu & Sun, Fangyuan & Huo, Xianxu & Li, Xue & Shen, Yu, 2020. "Hierarchical optimal scheduling method of heat-electricity integrated energy system based on Power Internet of Things," Energy, Elsevier, vol. 210(C).
    2. Hou, Langbo & Tong, Xi & Chen, Heng & Fan, Lanxin & Liu, Tao & Liu, Wenyi & Liu, Tong, 2024. "Optimized scheduling of smart community energy systems considering demand response and shared energy storage," Energy, Elsevier, vol. 295(C).
    3. Chen, Yuwei & Guo, Qinglai & Sun, Hongbin & Li, Zhengshuo & Pan, Zhaoguang & Wu, Wenchuan, 2019. "A water mass method and its application to integrated heat and electricity dispatch considering thermal inertias," Energy, Elsevier, vol. 181(C), pages 840-852.
    4. Mu, Chenlu & Ding, Tao & Qu, Ming & Zhou, Quan & Li, Fangxing & Shahidehpour, Mohammad, 2020. "Decentralized optimization operation for the multiple integrated energy systems with energy cascade utilization," Applied Energy, Elsevier, vol. 280(C).
    5. Zheng, Lingwei & Wu, Hao & Guo, Siqi & Sun, Xinyu, 2023. "Real-time dispatch of an integrated energy system based on multi-stage reinforcement learning with an improved action-choosing strategy," Energy, Elsevier, vol. 277(C).
    6. Liang, Ziwen & Mu, Longhua, 2024. "Multi-agent low-carbon optimal dispatch of regional integrated energy system based on mixed game theory," Energy, Elsevier, vol. 295(C).
    7. Jiajia Li & Jinfu Liu & Peigang Yan & Xingshuo Li & Guowen Zhou & Daren Yu, 2021. "Operation Optimization of Integrated Energy System under a Renewable Energy Dominated Future Scene Considering Both Independence and Benefit: A Review," Energies, MDPI, vol. 14(4), pages 1-36, February.
    8. Yang, Xiaohui & Chen, Zaixing & Huang, Xin & Li, Ruixin & Xu, Shaoping & Yang, Chunsheng, 2021. "Robust capacity optimization methods for integrated energy systems considering demand response and thermal comfort," Energy, Elsevier, vol. 221(C).
    9. Qiao, Zheng & Guo, Qinglai & Sun, Hongbin & Sheng, Tongtian, 2018. "Multi-time period optimized configuration and scheduling of gas storage in gas-fired power plants," Applied Energy, Elsevier, vol. 226(C), pages 924-934.
    10. Lingmin, Chen & Jiekang, Wu & Fan, Wu & Huiling, Tang & Changjie, Li & Yan, Xiong, 2020. "Energy flow optimization method for multi-energy system oriented to combined cooling, heating and power," Energy, Elsevier, vol. 211(C).
    11. Tan, Jin & Wu, Qiuwei & Wei, Wei & Liu, Feng & Li, Canbing & Zhou, Bin, 2020. "Decentralized robust energy and reserve Co-optimization for multiple integrated electricity and heating systems," Energy, Elsevier, vol. 205(C).
    12. Tan, Jin & Wu, Qiuwei & Hu, Qinran & Wei, Wei & Liu, Feng, 2020. "Adaptive robust energy and reserve co-optimization of integrated electricity and heating system considering wind uncertainty," Applied Energy, Elsevier, vol. 260(C).
    13. Wang, Rutian & Wen, Xiangyun & Wang, Xiuyun & Fu, Yanbo & Zhang, Yu, 2022. "Low carbon optimal operation of integrated energy system based on carbon capture technology, LCA carbon emissions and ladder-type carbon trading," Applied Energy, Elsevier, vol. 311(C).
    14. Wu, Xuewei & Zhang, Bin & Nielsen, Mads Pagh & Chen, Zhe, 2024. "Multi-stage planning of integrated electricity-gas-heating system in the context of carbon emission reduction," Applied Energy, Elsevier, vol. 358(C).
    15. Wang, Yongli & Ma, Yuze & Song, Fuhao & Ma, Yang & Qi, Chengyuan & Huang, Feifei & Xing, Juntai & Zhang, Fuwei, 2020. "Economic and efficient multi-objective operation optimization of integrated energy system considering electro-thermal demand response," Energy, Elsevier, vol. 205(C).
    16. Turk, Ana & Wu, Qiuwei & Zhang, Menglin & Østergaard, Jacob, 2020. "Day-ahead stochastic scheduling of integrated multi-energy system for flexibility synergy and uncertainty balancing," Energy, Elsevier, vol. 196(C).
    17. Li, Xiaozhu & Wang, Weiqing & Wang, Haiyun, 2021. "A novel bi-level robust game model to optimize a regionally integrated energy system with large-scale centralized renewable-energy sources in Western China," Energy, Elsevier, vol. 228(C).
    18. Jiang, Tao & Dong, Xinru & Zhang, Rufeng & Li, Xue, 2023. "Strategic active and reactive power scheduling of integrated community energy systems in day-ahead distribution electricity market," Applied Energy, Elsevier, vol. 336(C).
    19. Yang, Dechang & Wang, Ming & Yang, Ruiqi & Zheng, Yingying & Pandzic, Hrvoje, 2021. "Optimal dispatching of an energy system with integrated compressed air energy storage and demand response," Energy, Elsevier, vol. 234(C).
    20. Zhang, Zhi & Zhang, Yuquan & Zheng, Yuan & Zhang, Jisheng & Fernandez-Rodriguez, Emmanuel & Zang, Wei & Ji, Renwei, 2023. "Power fluctuation and wake characteristics of tidal stream turbine subjected to wave and current interaction," Energy, Elsevier, vol. 264(C).

    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:16:y:2023:i:24:p:8048-:d:1299628. 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.