IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v219y2021ics0360544220327365.html
   My bibliography  Save this article

Stochastic optimal operation model for a distributed integrated energy system based on multiple-scenario simulations

Author

Listed:
  • Mei, Fei
  • Zhang, Jiatang
  • Lu, Jixiang
  • Lu, Jinjun
  • Jiang, Yuhan
  • Gu, Jiaqi
  • Yu, Kun
  • Gan, Lei

Abstract

Problems related to the uncertainties of the sources and loads in integrated energy systems (IESs) are becoming more prominent with the interconnection of large-scale renewable energy sources and multi-energy loads. Moreover, such scenarios pose great challenges for the optimal operation of IESs. A distributed IES in an industrial park is regarded as the research object, and a stochastic optimal operation model based on multiple-scenario simulations is proposed to consider the prediction uncertainties arising in the case of distributed power generation and multi-energy loads. Specifically, scenario analysis for stochastic optimization is applied to address these prediction uncertainties in a two-part approach: operation scenario generation based on Latin hypercube sampling (LHS) and the reduction of multiple scenarios into a smaller number of more general scenarios based on K-means. Afterwards, a day-ahead stochastic optimal operation model for a distributed IES with the total operating economy as the decision-making objective is proposed based on typical operation scenarios. Moreover, the overall energy efficiency and new energy consumption capacity are all considered. In this way, the safe and economical operation of the IES can be guaranteed even under the negative influence of uncertainties. The validity and rationality of the proposed model are verified by analysis of examples.

Suggested Citation

  • Mei, Fei & Zhang, Jiatang & Lu, Jixiang & Lu, Jinjun & Jiang, Yuhan & Gu, Jiaqi & Yu, Kun & Gan, Lei, 2021. "Stochastic optimal operation model for a distributed integrated energy system based on multiple-scenario simulations," Energy, Elsevier, vol. 219(C).
    • Handle: RePEc:eee:energy:v:219:y:2021:i:c:s0360544220327365
    DOI: 10.1016/j.energy.2020.119629
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220327365
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.119629?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Bahrami, Arian & Teimourian, Amir & Okoye, Chiemeka Onyeka & Khosravi, Nima, 2019. "Assessing the feasibility of wind energy as a power source in Turkmenistan; a major opportunity for Central Asia's energy market," Energy, Elsevier, vol. 183(C), pages 415-427.
    2. Fu, Xueqian & Guo, Qinglai & Sun, Hongbin & Zhang, Xiurong & Wang, Li, 2017. "Estimation of the failure probability of an integrated energy system based on the first order reliability method," Energy, Elsevier, vol. 134(C), pages 1068-1078.
    3. 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.
    4. Bornapour, Mosayeb & Hooshmand, Rahmat-Allah & Khodabakhshian, Amin & Parastegari, Moein, 2016. "Optimal coordinated scheduling of combined heat and power fuel cell, wind, and photovoltaic units in micro grids considering uncertainties," Energy, Elsevier, vol. 117(P1), pages 176-189.
    5. Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.
    6. Pan, Zhaoguang & Guo, Qinglai & Sun, Hongbin, 2016. "Interactions of district electricity and heating systems considering time-scale characteristics based on quasi-steady multi-energy flow," Applied Energy, Elsevier, vol. 167(C), pages 230-243.
    7. Aien, Morteza & Rashidinejad, Masoud & Firuz-Abad, Mahmud Fotuhi, 2015. "Probabilistic optimal power flow in correlated hybrid wind-PV power systems: A review and a new approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 1437-1446.
    8. Sadeghian, H.R. & Ardehali, M.M., 2016. "A novel approach for optimal economic dispatch scheduling of integrated combined heat and power systems for maximum economic profit and minimum environmental emissions based on Benders decomposition," Energy, Elsevier, vol. 102(C), pages 10-23.
    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. 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).
    2. Zhang, Suhan & Gu, Wei & Qiu, Haifeng & Yao, Shuai & Pan, Guangsheng & Chen, Xiaogang, 2021. "State estimation models of district heating networks for integrated energy system considering incomplete measurements," Applied Energy, Elsevier, vol. 282(PA).
    3. Wang, Dan & Zhi, Yun-qiang & Jia, Hong-jie & Hou, Kai & Zhang, Shen-xi & Du, Wei & Wang, Xu-dong & Fan, Meng-hua, 2019. "Optimal scheduling strategy of district integrated heat and power system with wind power and multiple energy stations considering thermal inertia of buildings under different heating regulation modes," Applied Energy, Elsevier, vol. 240(C), pages 341-358.
    4. Chi, Lixun & Su, Huai & Zio, Enrico & Qadrdan, Meysam & Zhou, Jing & Zhang, Li & Fan, Lin & Yang, Zhaoming & Xie, Fei & Zuo, Lili & Zhang, Jinjun, 2023. "A systematic framework for the assessment of the reliability of energy supply in Integrated Energy Systems based on a quasi-steady-state model," Energy, Elsevier, vol. 263(PB).
    5. Ma, Tengfei & Pei, Wei & Xiao, Hao & Kong, Li & Mu, Yunfei & Pu, Tianjiao, 2020. "The energy management strategies based on dynamic energy pricing for community integrated energy system considering the interactions between suppliers and users," Energy, Elsevier, vol. 211(C).
    6. Chi, Lixun & Su, Huai & Zio, Enrico & Qadrdan, Meysam & Li, Xueyi & Zhang, Li & Fan, Lin & Zhou, Jing & Yang, Zhaoming & Zhang, Jinjun, 2021. "Data-driven reliability assessment method of Integrated Energy Systems based on probabilistic deep learning and Gaussian mixture Model-Hidden Markov Model," Renewable Energy, Elsevier, vol. 174(C), pages 952-970.
    7. Liu, Tianhao & Tian, Jun & Zhu, Hongyu & Goh, Hui Hwang & Liu, Hui & Wu, Thomas & Zhang, Dongdong, 2023. "Key technologies and developments of multi-energy system: Three-layer framework, modelling and optimisation," Energy, Elsevier, vol. 277(C).
    8. Zhang, Menglin & Wu, Qiuwei & Wen, Jinyu & Lin, Zhongwei & Fang, Fang & Chen, Qun, 2021. "Optimal operation of integrated electricity and heat system: A review of modeling and solution methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    9. Bao, Zhejing & Ye, Yangli & Liu, Ruijie & Cheng, Weidong & Zhao, Qiang & Wu, Ting, 2022. "Scheduling coordination of back pressure CHP coupled electricity-heat energy system with adaptive constraint strategy to accommodate uncertain wind power," Energy, Elsevier, vol. 240(C).
    10. Wang, Yang & Zhang, Shanhong & Chow, David & Kuckelkorn, Jens M., 2021. "Evaluation and optimization of district energy network performance: Present and future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    11. Fu, Xueqian & Guo, Qinglai & Sun, Hongbin & Zhang, Xiurong & Wang, Li, 2017. "Estimation of the failure probability of an integrated energy system based on the first order reliability method," Energy, Elsevier, vol. 134(C), pages 1068-1078.
    12. Song, William Hasung & Wang, Yang & Gillich, Aaron & Ford, Andy & Hewitt, Mark, 2019. "Modelling development and analysis on the Balanced Energy Networks (BEN) in London," Applied Energy, Elsevier, vol. 233, pages 114-125.
    13. 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.
    14. 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.
    15. Huang, Shaojun & Tang, Weichu & Wu, Qiuwei & Li, Canbing, 2019. "Network constrained economic dispatch of integrated heat and electricity systems through mixed integer conic programming," Energy, Elsevier, vol. 179(C), pages 464-474.
    16. Li, Xue & Li, Wenming & Zhang, Rufeng & Jiang, Tao & Chen, Houhe & Li, Guoqing, 2020. "Collaborative scheduling and flexibility assessment of integrated electricity and district heating systems utilizing thermal inertia of district heating network and aggregated buildings," Applied Energy, Elsevier, vol. 258(C).
    17. Ping Li & Haixia Wang & Quan Lv & Weidong Li, 2017. "Combined Heat and Power Dispatch Considering Heat Storage of Both Buildings and Pipelines in District Heating System for Wind Power Integration," Energies, MDPI, vol. 10(7), pages 1-19, June.
    18. Zhang, Tong & Li, Zhigang & Wu, Q.H. & Zhou, Xiaoxin, 2019. "Decentralized state estimation of combined heat and power systems using the asynchronous alternating direction method of multipliers," Applied Energy, Elsevier, vol. 248(C), pages 600-613.
    19. Hadis Moradi & Mahdi Esfahanian & Amir Abtahi & Ali Zilouchian, 2017. "Modeling a Hybrid Microgrid Using Probabilistic Reconfiguration under System Uncertainties," Energies, MDPI, vol. 10(9), pages 1-17, September.
    20. Andrea Menapace & Simone Santopietro & Rudy Gargano & Maurizio Righetti, 2021. "Stochastic Generation of District Heat Load," Energies, MDPI, vol. 14(17), pages 1-17, August.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:energy:v:219:y:2021:i:c:s0360544220327365. 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.journals.elsevier.com/energy .

    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.