IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v326y2022ics0306261922011801.html
   My bibliography  Save this article

An efficient optimal energy flow model for integrated energy systems based on energy circuit modeling in the frequency domain

Author

Listed:
  • Chen, Binbin
  • Wu, Wenchuan
  • Guo, Qinglai
  • Sun, Hongbin

Abstract

With more energy networks being interconnected to form integrated energy systems (IESs), the optimal energy flow (OEF) problem has drawn increasing attention. Extant studies on OEF models mostly utilize the finite difference method (FDM) to address partial-differential-equation (PDE) constraints related to the dynamics in natural gas networks (NGNs) and district heating networks (DHNs). However, this time-domain approach suffers from a heavy computational burden with regard to achieving high finite-difference accuracy. In this paper, a novel OEF model that formulates NGN and DHN constraints in the frequency domain and corresponding model compaction techniques for efficient solving are contributed. First, an energy circuit method (ECM) that algebraizes the PDEs of NGNs and DHNs in the frequency domain is introduced. Then, an ECM-based OEF model is formulated, which contains fewer variables and constraints than an FDM-based OEF model and thereby yields better solving efficiency. Finally, variable space projection is employed to remove implicit variables, by which another constraint generation algorithm is enabled to remove redundant constraints. These two techniques further compact the OEF model and bring about a second improvement in solving efficiency. Numerical tests on actual systems indicate the final OEF model reduces variables and constraints by more than 95% and improves the solving efficiency by more than 10 times. In conclusion, the proposed OEF model and solving techniques well meet the optimization needs of large-scale IESs.

Suggested Citation

  • Chen, Binbin & Wu, Wenchuan & Guo, Qinglai & Sun, Hongbin, 2022. "An efficient optimal energy flow model for integrated energy systems based on energy circuit modeling in the frequency domain," Applied Energy, Elsevier, vol. 326(C).
  • Handle: RePEc:eee:appene:v:326:y:2022:i:c:s0306261922011801
    DOI: 10.1016/j.apenergy.2022.119923
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922011801
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2022.119923?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chertkov, Michael & Novitsky, Nikolai N., 2019. "Thermal Transients in District Heating Systems," Energy, Elsevier, vol. 184(C), pages 22-33.
    2. Wang, Yaran & You, Shijun & Zhang, Huan & Zheng, Xuejing & Zheng, Wandong & Miao, Qingwei & Lu, Gang, 2017. "Thermal transient prediction of district heating pipeline: Optimal selection of the time and spatial steps for fast and accurate calculation," Applied Energy, Elsevier, vol. 206(C), pages 900-910.
    3. Qu, Kaiping & Yu, Tao & Zhang, Xiaoshun & Li, Haofei, 2019. "Homogenized adjacent points method: A novel Pareto optimizer for linearized multi-objective optimal energy flow of integrated electricity and gas system," Applied Energy, Elsevier, vol. 233, pages 338-351.
    4. Wang, Haichao & Yin, Wusong & Abdollahi, Elnaz & Lahdelma, Risto & Jiao, Wenling, 2015. "Modelling and optimization of CHP based district heating system with renewable energy production and energy storage," Applied Energy, Elsevier, vol. 159(C), pages 401-421.
    5. Liu, Rong-Peng & Sun, Wei & Yin, Wenqian & Zhou, Dali & Hou, Yunhe, 2021. "Extended convex hull-based distributed optimal energy flow of integrated electricity-gas systems," Applied Energy, Elsevier, vol. 287(C).
    6. Siqin, Zhuoya & Niu, DongXiao & Li, MingYu & Gao, Tian & Lu, Yifan & Xu, Xiaomin, 2022. "Distributionally robust dispatching of multi-community integrated energy system considering energy sharing and profit allocation," Applied Energy, Elsevier, vol. 321(C).
    7. Yang, Jingwei & Zhang, Ning & Botterud, Audun & Kang, Chongqing, 2020. "Situation awareness of electricity-gas coupled systems with a multi-port equivalent gas network model," Applied Energy, Elsevier, vol. 258(C).
    8. Zhang, Menglin & Wu, Qiuwei & Wen, Jinyu & Zhou, Bo & Guan, Qinyue & Tan, Jin & Lin, Zhongwei & Fang, Fang, 2022. "Day-ahead stochastic scheduling of integrated electricity and heat system considering reserve provision by large-scale heat pumps," Applied Energy, Elsevier, vol. 307(C).
    9. Zhuang, Wennan & Zhou, Suyang & Gu, Wei & Chen, Xiaogang, 2021. "Optimized dispatching of city-scale integrated energy system considering the flexibilities of city gas gate station and line packing," Applied Energy, Elsevier, vol. 290(C).
    10. Fang, Xin & Cui, Hantao & Yuan, Haoyu & Tan, Jin & Jiang, Tao, 2019. "Distributionally-robust chance constrained and interval optimization for integrated electricity and natural gas systems optimal power flow with wind uncertainties," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    11. Wang, Cheng & Wei, Wei & Wang, Jianhui & Bi, Tianshu, 2019. "Convex optimization based adjustable robust dispatch for integrated electric-gas systems considering gas delivery priority," Applied Energy, Elsevier, vol. 239(C), pages 70-82.
    12. Lin, Wei & Jin, Xiaolong & Jia, Hongjie & Mu, Yunfei & Xu, Tao & Xu, Xiandong & Yu, Xiaodan, 2021. "Decentralized optimal scheduling for integrated community energy system via consensus-based alternating direction method of multipliers," Applied Energy, Elsevier, vol. 302(C).
    13. Pan, Guangsheng & Gu, Wei & Wu, Zhi & Lu, Yuping & Lu, Shuai, 2019. "Optimal design and operation of multi-energy system with load aggregator considering nodal energy prices," Applied Energy, Elsevier, vol. 239(C), pages 280-295.
    14. Moretti, Luca & Martelli, Emanuele & Manzolini, Giampaolo, 2020. "An efficient robust optimization model for the unit commitment and dispatch of multi-energy systems and microgrids," Applied Energy, Elsevier, vol. 261(C).
    15. Chen, Yuwei & Guo, Qinglai & Sun, Hongbin & Pan, Zhaoguang & Chen, Binbin, 2021. "Generalized phasor modeling of dynamic gas flow for integrated electricity-gas dispatch," Applied Energy, Elsevier, vol. 283(C).
    16. Shabanpour-Haghighi, Amin & Seifi, Ali Reza, 2016. "Effects of district heating networks on optimal energy flow of multi-carrier systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 379-387.
    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. Zhao, Tian & Li, Hang & Li, Xia & Sun, Qing-Han & Fang, Xuan-Yi & Ma, Huan & Chen, Qun, 2024. "A frequency domain dynamic simulation method for heat exchangers and thermal systems," Energy, Elsevier, vol. 286(C).
    2. Wei Jiang & Renjie Qi & Song Xu & Seiji Hashimoto, 2024. "Real-Time Simulation System for Small Scale Regional Integrated Energy Systems," Energies, MDPI, vol. 17(13), pages 1-25, June.

    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. Zhang, Suhan & Gu, Wei & Lu, Hai & Qiu, Haifeng & Lu, Shuai & Wang, Dada & Liang, Junyu & Li, Wenyun, 2021. "Superposition-principle based decoupling method for energy flow calculation in district heating networks," Applied Energy, Elsevier, vol. 295(C).
    2. Zhao, Baining & Qian, Tong & Li, Weiwei & Xin, Yanli & Zhao, Wei & Lin, Zekang & Tang, Wenhu & Jin, Xin & Cao, Wangzhang & Pan, Tingzhe, 2024. "Fast distributed co-optimization of electricity and natural gas systems hedging against wind fluctuation and uncertainty," Energy, Elsevier, vol. 298(C).
    3. Qin, Xin & Sun, Hongbin & Shen, Xinwei & Guo, Ye & Guo, Qinglai & Xia, Tian, 2019. "A generalized quasi-dynamic model for electric-heat coupling integrated energy system with distributed energy resources," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    4. Xie, Zichan & Wang, Haichao & Hua, Pengmin & Lahdelma, Risto, 2023. "Discrete event simulation for dynamic thermal modelling of district heating pipe," Energy, Elsevier, vol. 285(C).
    5. Golmohamadi, Hessam & Larsen, Kim Guldstrand & Jensen, Peter Gjøl & Hasrat, Imran Riaz, 2022. "Integration of flexibility potentials of district heating systems into electricity markets: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    6. Li, Xia & Zhao, Tian & Sun, Qing-Han & Chen, Qun, 2022. "Frequency response function method for dynamic gas flow modeling and its application in pipeline system leakage diagnosis," Applied Energy, Elsevier, vol. 324(C).
    7. Tian, Xingtao & Lin, Xiaojie & Zhong, Wei & Zhou, Yi & Cong, Feiyun, 2024. "Optimal dispatch of integrated electricity and heating systems considering the quality-quantity regulation of heating systems to promote renewable energy consumption," Energy, Elsevier, vol. 300(C).
    8. Qin, Yuxiao & Liu, Pei & Li, Zheng, 2024. "Enhancing accuracy of flexibility characterization in integrated energy system design: A variable temporal resolution optimization method," Energy, Elsevier, vol. 288(C).
    9. Fan, Guozhu & Peng, Chunhua & Wang, Xuekui & Wu, Peng & Yang, Yifan & Sun, Huijuan, 2024. "Optimal scheduling of integrated energy system considering renewable energy uncertainties based on distributionally robust adaptive MPC," Renewable Energy, Elsevier, vol. 226(C).
    10. Alabi, Tobi Michael & Aghimien, Emmanuel I. & Agbajor, Favour D. & Yang, Zaiyue & Lu, Lin & Adeoye, Adebusola R. & Gopaluni, Bhushan, 2022. "A review on the integrated optimization techniques and machine learning approaches for modeling, prediction, and decision making on integrated energy systems," Renewable Energy, Elsevier, vol. 194(C), pages 822-849.
    11. Qu, Kaiping & Yu, Tao & Pan, Zhenning & Zhang, Xiaoshun, 2020. "Point estimate-based stochastic robust dispatch for electricity-gas combined system under wind uncertainty using iterative convex optimization," Energy, Elsevier, vol. 211(C).
    12. Fusco, Andrea & Gioffrè, Domenico & Francesco Castelli, Alessandro & Bovo, Cristian & Martelli, Emanuele, 2023. "A multi-stage stochastic programming model for the unit commitment of conventional and virtual power plants bidding in the day-ahead and ancillary services markets," Applied Energy, Elsevier, vol. 336(C).
    13. Hosseini, Seyed Hamid Reza & Allahham, Adib & Walker, Sara Louise & Taylor, Phil, 2020. "Optimal planning and operation of multi-vector energy networks: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    14. Polimeni, Simone & Moretti, Luca & Martelli, Emanuele & Leva, Sonia & Manzolini, Giampaolo, 2023. "A novel stochastic model for flexible unit commitment of off-grid microgrids," Applied Energy, Elsevier, vol. 331(C).
    15. Sadeghian, Omid & Mohammadpour Shotorbani, Amin & Mohammadi-Ivatloo, Behnam & Sadiq, Rehan & Hewage, Kasun, 2021. "Risk-averse maintenance scheduling of generation units in combined heat and power systems with demand response," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    16. Cui, Shiting & Zhu, Ruijin & Wu, Jun, 2024. "A double layer energy cooperation framework for prosumer groups in high altitude areas," Renewable Energy, Elsevier, vol. 224(C).
    17. Jiang, Sufan & Gao, Shan & Pan, Guangsheng & Zhao, Xin & Liu, Yu & Guo, Yasen & Wang, Sicheng, 2020. "A novel robust security constrained unit commitment model considering HVDC regulation," Applied Energy, Elsevier, vol. 278(C).
    18. Yi, Zonggen & Luo, Yusheng & Westover, Tyler & Katikaneni, Sravya & Ponkiya, Binaka & Sah, Suba & Mahmud, Sadab & Raker, David & Javaid, Ahmad & Heben, Michael J. & Khanna, Raghav, 2022. "Deep reinforcement learning based optimization for a tightly coupled nuclear renewable integrated energy system," Applied Energy, Elsevier, vol. 328(C).
    19. Wang, Yi & Qiu, Dawei & Sun, Mingyang & Strbac, Goran & Gao, Zhiwei, 2023. "Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach," Applied Energy, Elsevier, vol. 335(C).
    20. Han, Fengwu & Zeng, Jianfeng & Lin, Junjie & Zhao, Yunlong & Gao, Chong, 2023. "A stochastic hierarchical optimization and revenue allocation approach for multi-regional integrated energy systems based on cooperative games," Applied Energy, Elsevier, vol. 350(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:eee:appene:v:326:y:2022:i:c:s0306261922011801. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.