IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v185y2017ip2p1684-1693.html
   My bibliography  Save this article

Simultaneous allocation of distributed energy resource using improved particle swarm optimization

Author

Listed:
  • Kanwar, Neeraj
  • Gupta, Nikhil
  • Niazi, K.R.
  • Swarnkar, Anil
  • Bansal, R.C.

Abstract

Smart grid initiatives require integrated solution for radial distribution networks (RDNs) to achieve their optimum performance. The optimal allocation of distributed energy resources (DERs), such as shunt capacitors and distributed generation, when integrated with distribution network reconfiguration (DNR), can achieve desired objectives of smart distribution systems. This paper addresses a multi-objective formulation for simultaneous allocation of DERs in RDNs to maximize annual savings by reducing the charges for annual energy losses, peak power losses and substation capacity release against the annual charges incurred to purchase DERs while maintaining better node voltage profiles and feeder current profiles. An improved particle swarm optimization (IPSO) method is proposed to overcome against the inherent tendency of local trappings in PSO. A node sensitivity-based guided search algorithm (GSA) is also suggested to enhance the overall performance of the optimizing tool. GSA virtually squeezes the problem search space without loss of diversity. Distribution networks are optimally reconfigured after optimally placing DERs. The proposed method is investigated on the benchmark IEEE 33-bus and 69-bus test distribution systems. The application results show that the proposed integrated approach is very useful for electric utilities to enhance their profits and stagger their future expansion plans.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:185:y:2017:i:p2:p:1684-1693
    DOI: 10.1016/j.apenergy.2016.01.093
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261916300800
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2016.01.093?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ahmed M. Mahmoud & Shady H. E. Abdel Aleem & Almoataz Y. Abdelaziz & Mohamed Ezzat, 2022. "Towards Maximizing Hosting Capacity by Optimal Planning of Active and Reactive Power Compensators and Voltage Regulators: Case Study," Sustainability, MDPI, vol. 14(20), pages 1-34, October.
    2. Viana, Matheus Sabino & Manassero, Giovanni & Udaeta, Miguel E.M., 2018. "Analysis of demand response and photovoltaic distributed generation as resources for power utility planning," Applied Energy, Elsevier, vol. 217(C), pages 456-466.
    3. Hannan, M.A. & Lipu, M.S. Hossain & Ker, Pin Jern & Begum, R.A. & Agelidis, Vasilios G. & Blaabjerg, F., 2019. "Power electronics contribution to renewable energy conversion addressing emission reduction: Applications, issues, and recommendations," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    4. Luo, Lizi & Gu, Wei & Zhang, Xiao-Ping & Cao, Ge & Wang, Weijun & Zhu, Gang & You, Dingjun & Wu, Zhi, 2018. "Optimal siting and sizing of distributed generation in distribution systems with PV solar farm utilized as STATCOM (PV-STATCOM)," Applied Energy, Elsevier, vol. 210(C), pages 1092-1100.
    5. 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.
    6. Zhu, Jiawei & Lin, Yishuai & Lei, Weidong & Liu, Youquan & Tao, Mengling, 2019. "Optimal household appliances scheduling of multiple smart homes using an improved cooperative algorithm," Energy, Elsevier, vol. 171(C), pages 944-955.
    7. Veerasamy, Veerapandiyan & Abdul Wahab, Noor Izzri & Ramachandran, Rajeswari & Othman, Mohammad Lutfi & Hizam, Hashim & Devendran, Vidhya Sagar & Irudayaraj, Andrew Xavier Raj & Vinayagam, Arangarajan, 2021. "Recurrent network based power flow solution for voltage stability assessment and improvement with distributed energy sources," Applied Energy, Elsevier, vol. 302(C).
    8. 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.
    9. André Ulrich & Sergej Baum & Ingo Stadler & Christian Hotz & Eberhard Waffenschmidt, 2023. "Maximising Distribution Grid Utilisation by Optimising E-Car Charging Using Smart Meter Gateway Data," Energies, MDPI, vol. 16(9), pages 1-20, April.
    10. Heidari, A. & Mortazavi, S.S. & Bansal, R.C., 2020. "Stochastic effects of ice storage on improvement of an energy hub optimal operation including demand response and renewable energies," Applied Energy, Elsevier, vol. 261(C).
    11. Li, Yang & Feng, Bo & Li, Guoqing & Qi, Junjian & Zhao, Dongbo & Mu, Yunfei, 2018. "Optimal distributed generation planning in active distribution networks considering integration of energy storage," Applied Energy, Elsevier, vol. 210(C), pages 1073-1081.
    12. Mohammadhafez Bazrafshan & Likhitha Yalamanchili & Nikolaos Gatsis & Juan Gomez, 2019. "Stochastic Planning of Distributed PV Generation," Energies, MDPI, vol. 12(3), pages 1-20, January.
    13. Md Shafiul Alam & Fahad Saleh Al-Ismail & Mohammad Ali Abido, 2021. "PV/Wind-Integrated Low-Inertia System Frequency Control: PSO-Optimized Fractional-Order PI-Based SMES Approach," Sustainability, MDPI, vol. 13(14), pages 1-21, July.
    14. Singh, Pushpendra & Meena, Nand K. & Yang, Jin & Vega-Fuentes, Eduardo & Bishnoi, Shree Krishna, 2020. "Multi-criteria decision making monarch butterfly optimization for optimal distributed energy resources mix in distribution networks," Applied Energy, Elsevier, vol. 278(C).
    15. Li, Yang & Feng, Bo & Wang, Bin & Sun, Shuchao, 2022. "Joint planning of distributed generations and energy storage in active distribution networks: A Bi-Level programming approach," Energy, Elsevier, vol. 245(C).
    16. Kumar, Abhishek & Meena, Nand K. & Singh, Arvind R. & Deng, Yan & He, Xiangning & Bansal, R.C. & Kumar, Praveen, 2019. "Strategic integration of battery energy storage systems with the provision of distributed ancillary services in active distribution systems," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    17. 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.
    18. Hossain, Md Alamgir & Pota, Hemanshu Roy & Squartini, Stefano & Zaman, Forhad & Guerrero, Josep M., 2019. "Energy scheduling of community microgrid with battery cost using particle swarm optimisation," Applied Energy, Elsevier, vol. 254(C).
    19. 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).
    20. Pereira, Luan D.L. & Yahyaoui, Imene & Fiorotti, Rodrigo & de Menezes, Luíza S. & Fardin, Jussara F. & Rocha, Helder R.O. & Tadeo, Fernando, 2022. "Optimal allocation of distributed generation and capacitor banks using probabilistic generation models with correlations," Applied Energy, Elsevier, vol. 307(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:eee:appene:v:185:y:2017:i:p2:p:1684-1693. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.