IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v259y2020ics0306261919318823.html
   My bibliography  Save this article

A robust optimization approach for optimal load dispatch of community energy hub

Author

Listed:
  • Lu, Xinhui
  • Liu, Zhaoxi
  • Ma, Li
  • Wang, Lingfeng
  • Zhou, Kaile
  • Feng, Nanping

Abstract

As an important segment in the multi-energy systems, energy hub plays a significant role in improving the efficiency, flexibility and reliability of the multi-energy systems. In addition, load dispatch is an important optimization problem in the energy system, which has great significance to reduce energy consumption, environmental pollution and user's energy costs. In this regard, this paper proposes an optimal load dispatch model for a community energy hub, which aims to reduce the total cost of community energy hub, including operation cost and CO2 emission cost of the system. In the community energy hub, the combined heat and power (CHP) unit, gas boiler, heat storage unit, photovoltaic (PV) array, wind turbine (WT), and electric vehicles (EVs) are included. The uncertainties of EVs are modeled using the Monte Carlo simulation, and a robust optimization approach is adopted to deal with the future electricity price uncertainties. In addition, the proposed model comprehensively considers both electrical and thermal demand response (DR) programs. In the paper, three scheduling scenarios with different EV charging/discharging and DR settings are discussed. The simulation results show that the total costs can be effectively reduced by adopting coordinated charging/discharging mode for EVs. Meanwhile, the results also reveal that the consumers’ total cost can be further reduced by implementing the DR programs.

Suggested Citation

  • Lu, Xinhui & Liu, Zhaoxi & Ma, Li & Wang, Lingfeng & Zhou, Kaile & Feng, Nanping, 2020. "A robust optimization approach for optimal load dispatch of community energy hub," Applied Energy, Elsevier, vol. 259(C).
  • Handle: RePEc:eee:appene:v:259:y:2020:i:c:s0306261919318823
    DOI: 10.1016/j.apenergy.2019.114195
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261919318823
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2019.114195?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. Guille, Christophe & Gross, George, 2009. "A conceptual framework for the vehicle-to-grid (V2G) implementation," Energy Policy, Elsevier, vol. 37(11), pages 4379-4390, November.
    2. Sharifzadeh, Mahdi & Hien, Raymond Khoo Teck & Shah, Nilay, 2019. "China’s roadmap to low-carbon electricity and water: Disentangling greenhouse gas (GHG) emissions from electricity-water nexus via renewable wind and solar power generation, and carbon capture and sto," Applied Energy, Elsevier, vol. 235(C), pages 31-42.
    3. Dimitris Bertsimas & Melvyn Sim, 2004. "The Price of Robustness," Operations Research, INFORMS, vol. 52(1), pages 35-53, February.
    4. Najafi, Arsalan & Falaghi, Hamid & Contreras, Javier & Ramezani, Maryam, 2016. "Medium-term energy hub management subject to electricity price and wind uncertainty," Applied Energy, Elsevier, vol. 168(C), pages 418-433.
    5. Bordignon, Silvano & Bunn, Derek W. & Lisi, Francesco & Nan, Fany, 2013. "Combining day-ahead forecasts for British electricity prices," Energy Economics, Elsevier, vol. 35(C), pages 88-103.
    6. Yu, Mengmeng & Lu, Renzhi & Hong, Seung Ho, 2016. "A real-time decision model for industrial load management in a smart grid," Applied Energy, Elsevier, vol. 183(C), pages 1488-1497.
    7. Maroufmashat, Azadeh & Elkamel, Ali & Fowler, Michael & Sattari, Sourena & Roshandel, Ramin & Hajimiragha, Amir & Walker, Sean & Entchev, Evgueniy, 2015. "Modeling and optimization of a network of energy hubs to improve economic and emission considerations," Energy, Elsevier, vol. 93(P2), pages 2546-2558.
    8. Hadayeghparast, Shahrzad & SoltaniNejad Farsangi, Alireza & Shayanfar, Heidarali, 2019. "Day-ahead stochastic multi-objective economic/emission operational scheduling of a large scale virtual power plant," Energy, Elsevier, vol. 172(C), pages 630-646.
    9. Beigvand, Soheil Derafshi & Abdi, Hamdi & La Scala, Massimo, 2017. "A general model for energy hub economic dispatch," Applied Energy, Elsevier, vol. 190(C), pages 1090-1111.
    10. Weisser, Daniel, 2007. "A guide to life-cycle greenhouse gas (GHG) emissions from electric supply technologies," Energy, Elsevier, vol. 32(9), pages 1543-1559.
    11. Bento, P.M.R. & Pombo, J.A.N. & Calado, M.R.A. & Mariano, S.J.P.S., 2018. "A bat optimized neural network and wavelet transform approach for short-term price forecasting," Applied Energy, Elsevier, vol. 210(C), pages 88-97.
    12. Zhou, Kaile & Yang, Shanlin & Shao, Zhen, 2016. "Energy Internet: The business perspective," Applied Energy, Elsevier, vol. 178(C), pages 212-222.
    13. Mohammadi, Mohammad & Noorollahi, Younes & Mohammadi-ivatloo, Behnam & Yousefi, Hossein, 2017. "Energy hub: From a model to a concept – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 1512-1527.
    14. Zhou, Kaile & Fu, Chao & Yang, Shanlin, 2016. "Big data driven smart energy management: From big data to big insights," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 215-225.
    15. Chen, Yue & Wei, Wei & Liu, Feng & Wu, Qiuwei & Mei, Shengwei, 2018. "Analyzing and validating the economic efficiency of managing a cluster of energy hubs in multi-carrier energy systems," Applied Energy, Elsevier, vol. 230(C), pages 403-416.
    16. Aghaei, Jamshid & Alizadeh, Mohammad-Iman, 2013. "Multi-objective self-scheduling of CHP (combined heat and power)-based microgrids considering demand response programs and ESSs (energy storage systems)," Energy, Elsevier, vol. 55(C), pages 1044-1054.
    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. Lu, Xinhui & Liu, Zhaoxi & Ma, Li & Wang, Lingfeng & Zhou, Kaile & Yang, Shanlin, 2020. "A robust optimization approach for coordinated operation of multiple energy hubs," Energy, Elsevier, vol. 197(C).
    2. Zou, Juan & Yang, Xu & Liu, Zhongbing & Liu, Jiangyang & Zhang, Ling & Zheng, Jinhua, 2021. "Multiobjective bilevel optimization algorithm based on preference selection to solve energy hub system planning problems," Energy, Elsevier, vol. 232(C).
    3. Lasemi, Mohammad Ali & Arabkoohsar, Ahmad & Hajizadeh, Amin & Mohammadi-ivatloo, Behnam, 2022. "A comprehensive review on optimization challenges of smart energy hubs under uncertainty factors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    4. El-Afifi, Magda I. & El-Saadawi, Magdi M. & Sedhom, Bishoy E. & Eladl, Abdelfattah A., 2024. "An IoT-fog-cloud consensus-based energy management algorithm of multi-agent smart energy hubs considering packet losses and uncertainty," Renewable Energy, Elsevier, vol. 221(C).
    5. 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).
    6. Qiu, Dawei & Dong, Zihang & Zhang, Xi & Wang, Yi & Strbac, Goran, 2022. "Safe reinforcement learning for real-time automatic control in a smart energy-hub," Applied Energy, Elsevier, vol. 309(C).
    7. Lu, Xinhui & Li, Haobin & Zhou, Kaile & Yang, Shanlin, 2023. "Optimal load dispatch of energy hub considering uncertainties of renewable energy and demand response," Energy, Elsevier, vol. 262(PB).
    8. Guo, Shiliang & Li, Pengpeng & Ma, Kai & Yang, Bo & Yang, Jie, 2022. "Robust energy management for industrial microgrid considering charging and discharging pressure of electric vehicles," Applied Energy, Elsevier, vol. 325(C).
    9. Jia, Kunqi & Guo, Ge & Xiao, Jucheng & Zhou, Huan & Wang, Zhihua & He, Guangyu, 2019. "Data compression approach for the home energy management system," Applied Energy, Elsevier, vol. 247(C), pages 643-656.
    10. Liu, Bo & Hou, Yufan & Luan, Wenpeng & Liu, Zishuai & Chen, Sheng & Yu, Yixin, 2023. "A divide-and-conquer method for compression and reconstruction of smart meter data," Applied Energy, Elsevier, vol. 336(C).
    11. Rezaei, Navid & Pezhmani, Yasin & Khazali, Amirhossein, 2022. "Economic-environmental risk-averse optimal heat and power energy management of a grid-connected multi microgrid system considering demand response and bidding strategy," Energy, Elsevier, vol. 240(C).
    12. Ramos-Teodoro, Jerónimo & Rodríguez, Francisco & Berenguel, Manuel & Torres, José Luis, 2018. "Heterogeneous resource management in energy hubs with self-consumption: Contributions and application example," Applied Energy, Elsevier, vol. 229(C), pages 537-550.
    13. Lu, Qing & Lü, Shuaikang & Leng, Yajun, 2019. "A Nash-Stackelberg game approach in regional energy market considering users’ integrated demand response," Energy, Elsevier, vol. 175(C), pages 456-470.
    14. Mohammadi, Mohammad & Noorollahi, Younes & Mohammadi-ivatloo, Behnam & Yousefi, Hossein, 2017. "Energy hub: From a model to a concept – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 1512-1527.
    15. O’Dwyer, Edward & Pan, Indranil & Acha, Salvador & Shah, Nilay, 2019. "Smart energy systems for sustainable smart cities: Current developments, trends and future directions," Applied Energy, Elsevier, vol. 237(C), pages 581-597.
    16. Alqahtani, Mohammed & Hu, Mengqi, 2020. "Integrated energy scheduling and routing for a network of mobile prosumers," Energy, Elsevier, vol. 200(C).
    17. Nikmehr, Nima, 2020. "Distributed robust operational optimization of networked microgrids embedded interconnected energy hubs," Energy, Elsevier, vol. 199(C).
    18. 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).
    19. Zhang, Hongyu & Tomasgard, Asgeir & Knudsen, Brage Rugstad & Svendsen, Harald G. & Bakker, Steffen J. & Grossmann, Ignacio E., 2022. "Modelling and analysis of offshore energy hubs," Energy, Elsevier, vol. 261(PA).
    20. Ma, Tengfei & Wu, Junyong & Hao, Liangliang & Lee, Wei-Jen & Yan, Huaguang & Li, Dezhi, 2018. "The optimal structure planning and energy management strategies of smart multi energy systems," Energy, Elsevier, vol. 160(C), pages 122-141.

    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:259:y:2020:i:c:s0306261919318823. 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.