IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i6p2299-d776248.html
   My bibliography  Save this article

Simultaneous Distribution Network Reconfiguration and Optimal Allocation of Renewable-Based Distributed Generators and Shunt Capacitors under Uncertain Conditions

Author

Listed:
  • Mahmoud M. Sayed

    (Electrical Power and Machines Department, Faculty of Engineering, Cairo University, Cairo 12613, Egypt)

  • Mohamed Y. Mahdy

    (Electrical Power and Machines Department, Faculty of Engineering, Cairo University, Cairo 12613, Egypt)

  • Shady H. E. Abdel Aleem

    (Department of Electrical Engineering, Valley High Institute of Engineering and Technology, Science Valley Academy, Qalubia 44971, Egypt)

  • Hosam K. M. Youssef

    (Electrical Power and Machines Department, Faculty of Engineering, Cairo University, Cairo 12613, Egypt)

  • Tarek A. Boghdady

    (Electrical Power and Machines Department, Faculty of Engineering, Cairo University, Cairo 12613, Egypt)

Abstract

Smart grid technology has received ample attention in past years to develop the traditional power distribution network and to enable the integration of distributed generation units (DGs) to satisfy increasing demand loads and to improve network performance. In addition to DGs, integration of shunt capacitors (SCs) along with network reconfiguration can also play an important role in improving network performance. Besides, network reconfiguration can help to increase the distributed generation hosting capacity of the network. Some of the research in the literature have presented and discussed the problem of optimal integration of renewable DGs and SCs along with optimal network reconfiguration, while the network load variability and/or the intermittent nature of renewable DGs are neglected. For the work presented in this paper, the SHADE optimization algorithm along with the SOE reconfiguration method have been employed for solving the aforementioned optimization problem with consideration of uncertainty related to both the network load and the output power of the renewable DGs. Maximizing the hosting capacity (HC) of the DGs and reducing network power losses in addition to improving the voltage profile have been considered as optimization objectives. Five different case studies have been conducted considering 33-bus and 59-bus distribution networks. The obtained results validate the effectiveness and the superiority of the employed techniques for maximizing the HC up to 17% and reducing power losses up to 95%. Besides, the results also depict the effect of SC integration and the consideration of uncertainties on achieving the optimization objectives with realistic modeling of the optimization problem.

Suggested Citation

  • Mahmoud M. Sayed & Mohamed Y. Mahdy & Shady H. E. Abdel Aleem & Hosam K. M. Youssef & Tarek A. Boghdady, 2022. "Simultaneous Distribution Network Reconfiguration and Optimal Allocation of Renewable-Based Distributed Generators and Shunt Capacitors under Uncertain Conditions," Energies, MDPI, vol. 15(6), pages 1-27, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:2299-:d:776248
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/6/2299/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/6/2299/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kanwar, Neeraj & Gupta, Nikhil & Niazi, K.R. & Swarnkar, Anil & Bansal, R.C., 2017. "Simultaneous allocation of distributed energy resource using improved particle swarm optimization," Applied Energy, Elsevier, vol. 185(P2), pages 1684-1693.
    2. Jin, Xiaolong & Mu, Yunfei & Jia, Hongjie & Wu, Jianzhong & Xu, Xiandong & Yu, Xiaodan, 2016. "Optimal day-ahead scheduling of integrated urban energy systems," Applied Energy, Elsevier, vol. 180(C), pages 1-13.
    3. Kavousi-Fard, Abdollah & Niknam, Taher, 2014. "Multi-objective stochastic Distribution Feeder Reconfiguration from the reliability point of view," Energy, Elsevier, vol. 64(C), pages 342-354.
    4. Azizivahed, Ali & Narimani, Hossein & Naderi, Ehsan & Fathi, Mehdi & Narimani, Mohammad Rasoul, 2017. "A hybrid evolutionary algorithm for secure multi-objective distribution feeder reconfiguration," Energy, Elsevier, vol. 138(C), pages 355-373.
    5. Prakash, Prem & Khatod, Dheeraj K., 2016. "Optimal sizing and siting techniques for distributed generation in distribution systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 111-130.
    6. kianmehr, Ehsan & Nikkhah, Saman & Rabiee, Abbas, 2019. "Multi-objective stochastic model for joint optimal allocation of DG units and network reconfiguration from DG owner’s and DisCo’s perspectives," Renewable Energy, Elsevier, vol. 132(C), pages 471-485.
    7. Zidan, Aboelsood & El-Saadany, Ehab F., 2013. "Distribution system reconfiguration for energy loss reduction considering the variability of load and local renewable generation," Energy, Elsevier, vol. 59(C), pages 698-707.
    8. Ben Hamida, Imen & Salah, Saoussen Brini & Msahli, Faouzi & Mimouni, Mohamed Faouzi, 2018. "Optimal network reconfiguration and renewable DG integration considering time sequence variation in load and DGs," Renewable Energy, Elsevier, vol. 121(C), pages 66-80.
    9. Salman Khodayifar & Mohammad A. Raayatpanah & Abbas Rabiee & Hamed Rahimian & Panos M. Pardalos, 2018. "Optimal Long-Term Distributed Generation Planning and Reconfiguration of Distribution Systems: An Accelerating Benders’ Decomposition Approach," Journal of Optimization Theory and Applications, Springer, vol. 179(1), pages 283-310, October.
    10. Tuballa, Maria Lorena & Abundo, Michael Lochinvar, 2016. "A review of the development of Smart Grid technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 710-725.
    11. Kalambe, Shilpa & Agnihotri, Ganga, 2014. "Loss minimization techniques used in distribution network: bibliographical survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 184-200.
    12. Niknam, Taher & Fard, Abdollah Kavousi & Seifi, Alireza, 2012. "Distribution feeder reconfiguration considering fuel cell/wind/photovoltaic power plants," Renewable Energy, Elsevier, vol. 37(1), pages 213-225.
    13. Sultana, Beenish & Mustafa, M.W. & Sultana, U. & Bhatti, Abdul Rauf, 2016. "Review on reliability improvement and power loss reduction in distribution system via network reconfiguration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 297-310.
    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. Bruno Silva Torres & Luiz Eduardo Borges da Silva & Camila Paes Salomon & Carlos Henrique Valério de Moraes, 2022. "Integrating Smart Grid Devices into the Traditional Protection of Distribution Networks," Energies, MDPI, vol. 15(7), pages 1-28, March.
    2. Ola Badran & Jafar Jallad, 2023. "Multi-Objective Decision Approach for Optimal Real-Time Switching Sequence of Network Reconfiguration Realizing Maximum Load Capacity," Energies, MDPI, vol. 16(19), pages 1-32, September.
    3. Zifa Liu & Jieyu Li & Yunyang Liu & Puyang Yu & Junteng Shao, 2022. "Collaborative Optimized Operation Model of Multi-Character Distribution Network Considering Multiple Uncertain Factors and Demand Response," Energies, MDPI, vol. 15(12), pages 1-19, 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. 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).
    2. kianmehr, Ehsan & Nikkhah, Saman & Rabiee, Abbas, 2019. "Multi-objective stochastic model for joint optimal allocation of DG units and network reconfiguration from DG owner’s and DisCo’s perspectives," Renewable Energy, Elsevier, vol. 132(C), pages 471-485.
    3. Sultana, Beenish & Mustafa, M.W. & Sultana, U. & Bhatti, Abdul Rauf, 2016. "Review on reliability improvement and power loss reduction in distribution system via network reconfiguration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 297-310.
    4. Azizivahed, Ali & Narimani, Hossein & Naderi, Ehsan & Fathi, Mehdi & Narimani, Mohammad Rasoul, 2017. "A hybrid evolutionary algorithm for secure multi-objective distribution feeder reconfiguration," Energy, Elsevier, vol. 138(C), pages 355-373.
    5. 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.
    6. Syed Ali Abbas Kazmi & Muhammad Khuram Shahzad & Dong Ryeol Shin, 2017. "Multi-Objective Planning Techniques in Distribution Networks: A Composite Review," Energies, MDPI, vol. 10(2), pages 1-44, February.
    7. Syed Ali Abbas Kazmi & Muhammad Khuram Shahzad & Akif Zia Khan & Dong Ryeol Shin, 2017. "Smart Distribution Networks: A Review of Modern Distribution Concepts from a Planning Perspective," Energies, MDPI, vol. 10(4), pages 1-47, April.
    8. Kavousi-Fard, Abdollah & Abbasi, Alireza & Rostami, Mohammad-Amin & Khosravi, Abbas, 2015. "Optimal distribution feeder reconfiguration for increasing the penetration of plug-in electric vehicles and minimizing network costs," Energy, Elsevier, vol. 93(P2), pages 1693-1703.
    9. Ben Hamida, Imen & Salah, Saoussen Brini & Msahli, Faouzi & Mimouni, Mohamed Faouzi, 2018. "Optimal network reconfiguration and renewable DG integration considering time sequence variation in load and DGs," Renewable Energy, Elsevier, vol. 121(C), pages 66-80.
    10. 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.
    11. Arun Onlam & Daranpob Yodphet & Rongrit Chatthaworn & Chayada Surawanitkun & Apirat Siritaratiwat & Pirat Khunkitti, 2019. "Power Loss Minimization and Voltage Stability Improvement in Electrical Distribution System via Network Reconfiguration and Distributed Generation Placement Using Novel Adaptive Shuffled Frogs Leaping," Energies, MDPI, vol. 12(3), pages 1-12, February.
    12. Tolabi, Hajar Bagheri & Ali, Mohd Hasan & Shahrin Bin Md Ayob, & Rizwan, M., 2014. "Novel hybrid fuzzy-Bees algorithm for optimal feeder multi-objective reconfiguration by considering multiple-distributed generation," Energy, Elsevier, vol. 71(C), pages 507-515.
    13. Chenyang Ma & Wei Wang & Zhiqiang Cai & Jiangbin Zhao, 2022. "Maintenance optimization of reconfigurable systems based on multi-objective Birnbaum importance," Journal of Risk and Reliability, , vol. 236(2), pages 277-289, April.
    14. Sirjani, Reza & Rezaee Jordehi, Ahmad, 2017. "Optimal placement and sizing of distribution static compensator (D-STATCOM) in electric distribution networks: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 688-694.
    15. Grover-Silva, Etta & Girard, Robin & Kariniotakis, George, 2018. "Optimal sizing and placement of distribution grid connected battery systems through an SOCP optimal power flow algorithm," Applied Energy, Elsevier, vol. 219(C), pages 385-393.
    16. Pesaran H.A., Mahmoud & Nazari-Heris, Morteza & Mohammadi-Ivatloo, Behnam & Seyedi, Heresh, 2020. "A hybrid genetic particle swarm optimization for distributed generation allocation in power distribution networks," Energy, Elsevier, vol. 209(C).
    17. 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).
    18. Esmaeeli, M. & Kazemi, A. & Shayanfar, H.A. & Haghifam, M.-R., 2015. "Multistage distribution substations planning considering reliability and growth of energy demand," Energy, Elsevier, vol. 84(C), pages 357-364.
    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. Bayat, A. & Bagheri, A., 2019. "Optimal active and reactive power allocation in distribution networks using a novel heuristic approach," Applied Energy, Elsevier, vol. 233, pages 71-85.

    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:15:y:2022:i:6:p:2299-:d:776248. 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.