IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i16p3126-d257634.html
   My bibliography  Save this article

Multi-period Market Operation of Transmission-Distribution Systems Based on Heterogeneous Decomposition and Coordination

Author

Listed:
  • Cong Liu

    (China Electric Power Research Institute, Beijing 100192, China)

  • Jingyang Zhou

    (China Electric Power Research Institute, Beijing 100192, China)

  • Yi Pan

    (China Electric Power Research Institute, Beijing 100192, China)

  • Zhiyi Li

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Yifei Wang

    (School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

  • Dan Xu

    (China Electric Power Research Institute, Beijing 100192, China)

  • Qiang Ding

    (China Electric Power Research Institute, Beijing 100192, China)

  • Zhiqiang Luo

    (National Electric Power Dispatching and Control Center, Beijing 100031, China)

  • Mohammad Shahidehpour

    (Robert W. Galvin Center for Electricity Innovation at Illinois Institute of Technology, Chicago, IL 60616, USA)

Abstract

The integration of shiftable/curtailment distribution generators (DGs) along with quick-response storage has not only increased the transaction’s flexibility but also puzzled the bidding willingness of transmission-connected market players (TMPs). In this paper, the method of heterogeneous decomposition and coordination (HGDC) is applied to decompose the integrated transmission-distribution market framework into a bi-level problem with a transmission wholesale market master problem and several distribution retail market subproblems in a decentralized organization structure. The price-based bidding willingness of demand-side resources’ (DSRs’) aggregator is simulated considering the relation between distribution system operator’s (DSO’s) operation modes and its equivalent market transactive price. Besides the traditional mixed-integer linear programming (MILP) model, the active reconfiguration model of DSOs based on mixed-integer second-order conic programming (MI-SOCP) is proposed to rearrange its operation switch status and elaborate its operation cost according to the market transaction. Multi-period optimal operation modes could be obtained through an HGDC-based iteration process by coordinating the transmission system operator (TSO) with DSOs and considering the market energy equilibrium and reserve requirements for security considerations. Karush-Kuhn-Tucker (KKT) conditions are used to testify the optimality and convergence of the bi-level model in theory. The T5-3D33 case is employed to illustrate the efficiency of the proposed model and method.

Suggested Citation

  • Cong Liu & Jingyang Zhou & Yi Pan & Zhiyi Li & Yifei Wang & Dan Xu & Qiang Ding & Zhiqiang Luo & Mohammad Shahidehpour, 2019. "Multi-period Market Operation of Transmission-Distribution Systems Based on Heterogeneous Decomposition and Coordination," Energies, MDPI, vol. 12(16), pages 1-20, August.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:16:p:3126-:d:257634
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/16/3126/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/16/3126/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Jianxiao & Zhong, Haiwang & Tang, Wenyuan & Rajagopal, Ram & Xia, Qing & Kang, Chongqing & Wang, Yi, 2017. "Optimal bidding strategy for microgrids in joint energy and ancillary service markets considering flexible ramping products," Applied Energy, Elsevier, vol. 205(C), pages 294-303.
    2. Gerard, Helena & Rivero Puente, Enrique Israel & Six, Daan, 2018. "Coordination between transmission and distribution system operators in the electricity sector: A conceptual framework," Utilities Policy, Elsevier, vol. 50(C), pages 40-48.
    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. Schwidtal, J.M. & Piccini, P. & Troncia, M. & Chitchyan, R. & Montakhabi, M. & Francis, C. & Gorbatcheva, A. & Capper, T. & Mustafa, M.A. & Andoni, M. & Robu, V. & Bahloul, M. & Scott, I.J. & Mbavarir, 2023. "Emerging business models in local energy markets: A systematic review of peer-to-peer, community self-consumption, and transactive energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 179(C).
    2. Capper, Timothy & Gorbatcheva, Anna & Mustafa, Mustafa A. & Bahloul, Mohamed & Schwidtal, Jan Marc & Chitchyan, Ruzanna & Andoni, Merlinda & Robu, Valentin & Montakhabi, Mehdi & Scott, Ian J. & Franci, 2022. "Peer-to-peer, community self-consumption, and transactive energy: A systematic literature review of local energy market models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).

    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. Jiang, Tao & Wu, Chenghao & Huang, Tao & Zhang, Rufeng & Li, Xue, 2024. "Optimal market participation of VPPs in TSO-DSO coordinated energy and flexibility markets," Applied Energy, Elsevier, vol. 360(C).
    2. 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).
    3. Nemanja Mišljenović & Matej Žnidarec & Goran Knežević & Damir Šljivac & Andreas Sumper, 2023. "A Review of Energy Management Systems and Organizational Structures of Prosumers," Energies, MDPI, vol. 16(7), pages 1-32, March.
    4. Correa-Florez, Carlos Adrian & Michiorri, Andrea & Kariniotakis, Georges, 2018. "Robust optimization for day-ahead market participation of smart-home aggregators," Applied Energy, Elsevier, vol. 229(C), pages 433-445.
    5. Marius Buchmann, 2019. "How decentralization drives a change of the institutional framework on the distribution grid level in the electricity sector – the case of local congestion markets," Bremen Energy Working Papers 0031, Bremen Energy Research.
    6. Dranka, Géremi Gilson & Ferreira, Paula & Vaz, A. Ismael F., 2021. "A review of co-optimization approaches for operational and planning problems in the energy sector," Applied Energy, Elsevier, vol. 304(C).
    7. Mehdizadeh, Ali & Taghizadegan, Navid & Salehi, Javad, 2018. "Risk-based energy management of renewable-based microgrid using information gap decision theory in the presence of peak load management," Applied Energy, Elsevier, vol. 211(C), pages 617-630.
    8. Capper, Timothy & Gorbatcheva, Anna & Mustafa, Mustafa A. & Bahloul, Mohamed & Schwidtal, Jan Marc & Chitchyan, Ruzanna & Andoni, Merlinda & Robu, Valentin & Montakhabi, Mehdi & Scott, Ian J. & Franci, 2022. "Peer-to-peer, community self-consumption, and transactive energy: A systematic literature review of local energy market models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    9. Tang, Hong & Wang, Shengwei, 2022. "Multi-level optimal dispatch strategy and profit-sharing mechanism for unlocking energy flexibilities of non-residential building clusters in electricity markets of multiple flexibility services," Renewable Energy, Elsevier, vol. 201(P1), pages 35-45.
    10. Bahramara, Salah & Sheikhahmadi, Pouria & Golpîra, Hêmin, 2019. "Co-optimization of energy and reserve in standalone micro-grid considering uncertainties," Energy, Elsevier, vol. 176(C), pages 792-804.
    11. Gonzalez Venegas, Felipe & Petit, Marc & Perez, Yannick, 2021. "Active integration of electric vehicles into distribution grids: Barriers and frameworks for flexibility services," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    12. Vosughi, Amirkhosro & Tamimi, Ali & King, Alexandra Beatrice & Majumder, Subir & Srivastava, Anurag K., 2022. "Cyber–physical vulnerability and resiliency analysis for DER integration: A review, challenges and research needs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    13. Henryk Majchrzak & Michał Kozioł, 2021. "Analysis of Various Options for Balancing Power Systems’ Peak Load," Energies, MDPI, vol. 14(2), pages 1-20, January.
    14. Schwidtal, Jan Marc & Agostini, Marco & Coppo, Massimiliano & Bignucolo, Fabio & Lorenzoni, Arturo, 2023. "Optimized operation of distributed energy resources: The opportunities of value stacking for Power-to-Gas aggregated with PV," Applied Energy, Elsevier, vol. 334(C).
    15. Cui, Mingjian & Zhang, Jie, 2018. "Estimating ramping requirements with solar-friendly flexible ramping product in multi-timescale power system operations," Applied Energy, Elsevier, vol. 225(C), pages 27-41.
    16. Neuhoff, Karsten & Richstein, Jörn, 2017. "TSO-DSO-PX Cooperation. Report on the key elements of debate from a workshop of the Future Power Market Platform," EconStor Research Reports 167313, ZBW - Leibniz Information Centre for Economics.
    17. Yeh, Wei-Chang & He, Min-Fan & Huang, Chia-Ling & Tan, Shi-Yi & Zhang, Xianyong & Huang, Yaohong & Li, Li, 2020. "New genetic algorithm for economic dispatch of stand-alone three-modular microgrid in DongAo Island," Applied Energy, Elsevier, vol. 263(C).
    18. Rafal Dzikowski, 2020. "DSO–TSO Coordination of Day-Ahead Operation Planning with the Use of Distributed Energy Resources," Energies, MDPI, vol. 13(14), pages 1-25, July.
    19. Wang, Yubin & Yang, Qiang & Zhou, Yue & Zheng, Yanchong, 2024. "A risk-averse day-ahead bidding strategy of transactive energy sharing microgrids with data-driven chance constraints," Applied Energy, Elsevier, vol. 353(PB).
    20. Nikpour, Ahmad & Nateghi, Abolfazl & Shafie-khah, Miadreza & Catalão, João P.S., 2021. "Day-ahead optimal bidding of microgrids considering uncertainties of price and renewable energy resources," Energy, Elsevier, vol. 227(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:12:y:2019:i:16:p:3126-:d:257634. 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.