IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v215y2021ipbs0360544220322532.html
   My bibliography  Save this article

Distribution Network Reconfiguration based on LMP at DG connected busses using game theory and self-adaptive FWA

Author

Listed:
  • Azad-Farsani, Ehsan
  • Sardou, Iman Goroohi
  • Abedini, Saeed

Abstract

Distribution Company (DISCO) uses some price-based policies to maximize the profit of Distributed Generation (DG) owners and minimize the power loss of the network. Meanwhile, loss of the network is minimized by Distribution Network Reconfiguration (DNR). In this paper, a hybrid market-based DNR methodology is proposed to concurrently obtain both the optimal configuration of the network, and Locational Marginal Prices (LMPs) at DG connected busses. Firework Algorithm (FWA) is coupled with an Iterative Game-Based algorithm (IGBA) to procure the optimal configuration of the network with the objective of power loss minimization. In the hybrid methodology, through the search process of FWA in finding the optimal configuration, the IGBA is run for each candidate configuration. Using the IGBA, the LMPs of DG units is calculated based on their share of loss reduction which is determined by the game theory. Also, in the FW algorithm, improper selection of coefficients leads to inefficiency of the algorithm. Therefore, a self-adaptive framework is proposed to specify the value of coefficients during the evolution of algorithm. The effectiveness of the proposed methodology is evaluated using a practical system.

Suggested Citation

  • Azad-Farsani, Ehsan & Sardou, Iman Goroohi & Abedini, Saeed, 2021. "Distribution Network Reconfiguration based on LMP at DG connected busses using game theory and self-adaptive FWA," Energy, Elsevier, vol. 215(PB).
    • Handle: RePEc:eee:energy:v:215:y:2021:i:pb:s0360544220322532
    DOI: 10.1016/j.energy.2020.119146
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220322532
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.119146?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. Azizivahed, Ali & Narimani, Hossein & Fathi, Mehdi & Naderi, Ehsan & Safarpour, Hamid Reza & Narimani, Mohammad Rasoul, 2018. "Multi-objective dynamic distribution feeder reconfiguration in automated distribution systems," Energy, Elsevier, vol. 147(C), pages 896-914.
    2. Azad-Farsani, Ehsan & Agah, S.M.M. & Askarian-Abyaneh, Hossein & Abedi, Mehrdad & Hosseinian, S.H., 2016. "Stochastic LMP (Locational marginal price) calculation method in distribution systems to minimize loss and emission based on Shapley value and two-point estimate method," Energy, Elsevier, vol. 107(C), pages 396-408.
    3. Sedighizadeh, Mostafa & Esmaili, Masoud & Esmaeili, Mobin, 2014. "Application of the hybrid Big Bang-Big Crunch algorithm to optimal reconfiguration and distributed generation power allocation in distribution systems," Energy, Elsevier, vol. 76(C), pages 920-930.
    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. Wang, Hong-Jiang & Pan, Jeng-Shyang & Nguyen, Trong-The & Weng, Shaowei, 2022. "Distribution network reconfiguration with distributed generation based on parallel slime mould algorithm," Energy, Elsevier, vol. 244(PB).

    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. Li, J.Y. & Chen, J.J. & Wang, Y.X. & Chen, W.G., 2024. "Combining multi-step reconfiguration with many-objective reduction as iterative bi-level scheduling for stochastic distribution network," Energy, Elsevier, vol. 290(C).
    2. Das, Bikash & Mukherjee, V. & Das, Debapriya, 2019. "Optimum DG placement for known power injection from utility/substation by a novel zero bus load flow approach," Energy, Elsevier, vol. 175(C), pages 228-249.
    3. Narimani, Hossein & Razavi, Seyed-Ehsan & Azizivahed, Ali & Naderi, Ehsan & Fathi, Mehdi & Ataei, Mohammad H. & Narimani, Mohammad Rasoul, 2018. "A multi-objective framework for multi-area economic emission dispatch," Energy, Elsevier, vol. 154(C), pages 126-142.
    4. Mbuli, N. & Ngaha, W.S., 2022. "A survey of big bang big crunch optimisation in power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    5. Wang, Hong-Jiang & Pan, Jeng-Shyang & Nguyen, Trong-The & Weng, Shaowei, 2022. "Distribution network reconfiguration with distributed generation based on parallel slime mould algorithm," Energy, Elsevier, vol. 244(PB).
    6. Jordehi, A. Rezaee, 2015. "Optimisation of electric distribution systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1088-1100.
    7. Arasteh, Hamidreza & Sepasian, Mohammad Sadegh & Vahidinasab, Vahid, 2016. "An aggregated model for coordinated planning and reconfiguration of electric distribution networks," Energy, Elsevier, vol. 94(C), pages 786-798.
    8. Mukhopadhyay, Bineeta & Das, Debapriya, 2020. "Multi-objective dynamic and static reconfiguration with optimized allocation of PV-DG and battery energy storage system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    9. Voswinkel, Simon & Höckner, Jonas & Khalid, Abuzar & Weber, Christoph, 2022. "Sharing congestion management costs among system operators using the Shapley value," Applied Energy, Elsevier, vol. 317(C).
    10. Terlouw, Tom & AlSkaif, Tarek & Bauer, Christian & van Sark, Wilfried, 2019. "Multi-objective optimization of energy arbitrage in community energy storage systems using different battery technologies," Applied Energy, Elsevier, vol. 239(C), pages 356-372.
    11. Tolabi, H.B. & Ara, A. Lashkar & Hosseini, R., 2020. "A new thief and police algorithm and its application in simultaneous reconfiguration with optimal allocation of capacitor and distributed generation units," Energy, Elsevier, vol. 203(C).
    12. Karhinen, Santtu & Huuki, Hannu, 2020. "How are the long distances between renewable energy sources and load centres reflected in locational marginal prices?," Energy, Elsevier, vol. 210(C).
    13. Hadidian Moghaddam, Mohammad Jafar & Kalam, Akhtar & Nowdeh, Saber Arabi & Ahmadi, Abdollah & Babanezhad, Manoochehr & Saha, Sajeeb, 2019. "Optimal sizing and energy management of stand-alone hybrid photovoltaic/wind system based on hydrogen storage considering LOEE and LOLE reliability indices using flower pollination algorithm," Renewable Energy, Elsevier, vol. 135(C), pages 1412-1434.
    14. Zeng, Bo & Wen, Junqiang & Shi, Jinyue & Zhang, Jianhua & Zhang, Yuying, 2016. "A multi-level approach to active distribution system planning for efficient renewable energy harvesting in a deregulated environment," Energy, Elsevier, vol. 96(C), pages 614-624.
    15. Mostafa Abdo & Salah Kamel & Mohamed Ebeed & Juan Yu & Francisco Jurado, 2018. "Solving Non-Smooth Optimal Power Flow Problems Using a Developed Grey Wolf Optimizer," Energies, MDPI, vol. 11(7), pages 1-16, June.
    16. Luis A. Gallego Pareja & Jesús M. López-Lezama & Oscar Gómez Carmona, 2022. "A Mixed-Integer Linear Programming Model for the Simultaneous Optimal Distribution Network Reconfiguration and Optimal Placement of Distributed Generation," Energies, MDPI, vol. 15(9), pages 1-26, April.
    17. Cremers, Sho & Robu, Valentin & Zhang, Peter & Andoni, Merlinda & Norbu, Sonam & Flynn, David, 2023. "Efficient methods for approximating the Shapley value for asset sharing in energy communities," Applied Energy, Elsevier, vol. 331(C).
    18. Li, Peng & Ji, Jie & Ji, Haoran & Song, Guanyu & Wang, Chengshan & Wu, Jianzhong, 2020. "Self-healing oriented supply restoration method based on the coordination of multiple SOPs in active distribution networks," Energy, Elsevier, vol. 195(C).
    19. Badran, Ola & Mekhilef, Saad & Mokhlis, Hazlie & Dahalan, Wardiah, 2017. "Optimal reconfiguration of distribution system connected with distributed generations: A review of different methodologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 854-867.
    20. Bo Li & Panpan Zhang & Xiangjun Li & Shengxian Cao, 2019. "Distributed Absorption and Half-Search Approach for Economic Dispatch Problem in Smart Grids," Energies, MDPI, vol. 12(8), pages 1-21, April.

    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:215:y:2021:i:pb:s0360544220322532. 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.