IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v147y2018icp980-994.html
   My bibliography  Save this article

Flower pollination algorithm based multi-objective congestion management considering optimal capacities of distributed generations

Author

Listed:
  • Peesapati, Rajagopal
  • Yadav, Vinod Kumar
  • Kumar, Niranjan

Abstract

Transmission Congestion creates hindrance that limit the most economical supply to reach demands. Hence, it is relieved at the earliest to make optimum utilization of available transmission network in order to achieve maximum profits. In this work, optimal capacities of distributed generation (DG) units are inserted to remove the congestion in the transmission lines of bulk power system. Multi-objectives like real power losses, investment costs, voltage deviations and line capacities are converted into single objective and is minimized to obtain the optimal capacities of the DG units. Flower Pollination Algorithm (FPA) is implemented to achieve the best capacities of the DGs that are operating at unity (UPF) and 0.9 lagging power factors. The capacities of DGs are obtained at multiple locations instead of single optimal or sub-optimal location in order to improve the practical feasibility while connecting the DGs. The proposed methodology is practiced on IEEE 30 and 118 bus system to check the effectiveness. Further, the result obtained by FPA are compared with Genetic algorithm (GA) and Particle Swarm Optimization (PSO) approaches in terms of real power losses (RPL) and line flows. Results conveyed that the proposed algorithm had superior features, stable convergence characteristics and good computational efficiency.

Suggested Citation

  • Peesapati, Rajagopal & Yadav, Vinod Kumar & Kumar, Niranjan, 2018. "Flower pollination algorithm based multi-objective congestion management considering optimal capacities of distributed generations," Energy, Elsevier, vol. 147(C), pages 980-994.
    • Handle: RePEc:eee:energy:v:147:y:2018:i:c:p:980-994
    DOI: 10.1016/j.energy.2018.01.077
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544218300951
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2018.01.077?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. Rahiminejad, A. & Vahidi, B. & Hejazi, M.A. & Shahrooyan, S., 2016. "Optimal scheduling of dispatchable distributed generation in smart environment with the aim of energy loss minimization," Energy, Elsevier, vol. 116(P1), pages 190-201.
    2. Esmaili, Masoud & Shayanfar, Heidar Ali & Moslemi, Ramin, 2014. "Locating series FACTS devices for multi-objective congestion management improving voltage and transient stability," European Journal of Operational Research, Elsevier, vol. 236(2), pages 763-773.
    3. Aman, M.M. & Jasmon, G.B. & Bakar, A.H.A. & Mokhlis, H., 2014. "A new approach for optimum simultaneous multi-DG distributed generation Units placement and sizing based on maximization of system loadability using HPSO (hybrid particle swarm optimization) algorithm," Energy, Elsevier, vol. 66(C), pages 202-215.
    4. Esmaili, Masoud & Shayanfar, Heidar Ali & Amjady, Nima, 2009. "Multi-objective congestion management incorporating voltage and transient stabilities," Energy, Elsevier, vol. 34(9), pages 1401-1412.
    5. Kayal, Partha & Chanda, C.K., 2015. "Optimal mix of solar and wind distributed generations considering performance improvement of electrical distribution network," Renewable Energy, Elsevier, vol. 75(C), pages 173-186.
    6. Sultana, U. & Khairuddin, Azhar B. & Mokhtar, A.S. & Zareen, N. & Sultana, Beenish, 2016. "Grey wolf optimizer based placement and sizing of multiple distributed generation in the distribution system," Energy, Elsevier, vol. 111(C), pages 525-536.
    7. Esmaili, Masoud & Firozjaee, Esmail Chaktan & Shayanfar, Heidar Ali, 2014. "Optimal placement of distributed generations considering voltage stability and power losses with observing voltage-related constraints," Applied Energy, Elsevier, vol. 113(C), pages 1252-1260.
    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. Mahmoud G. Hemeida & Salem Alkhalaf & Al-Attar A. Mohamed & Abdalla Ahmed Ibrahim & Tomonobu Senjyu, 2020. "Distributed Generators Optimization Based on Multi-Objective Functions Using Manta Rays Foraging Optimization Algorithm (MRFO)," Energies, MDPI, vol. 13(15), pages 1-37, July.
    2. Panda, Mitali & Nayak, Yogesh Kumar, 2022. "Impact analysis of renewable energy Distributed Generation in deregulated electricity markets: A Context of Transmission Congestion Problem," Energy, Elsevier, vol. 254(PC).
    3. 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.
    4. Tamás Bányai & Béla Illés & Miklós Gubán & Ákos Gubán & Fabian Schenk & Ágota Bányai, 2019. "Optimization of Just-In-Sequence Supply: A Flower Pollination Algorithm-Based Approach," Sustainability, MDPI, vol. 11(14), pages 1-26, July.
    5. Sadhan Gope & A. K. Goswami & P. K. Tiwari, 2020. "Transmission congestion management with integration of wind farm: a possible solution methodology for deregulated power market," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(2), pages 287-296, April.
    6. Rasheed Abdulkader & Hayder M. A. Ghanimi & Pankaj Dadheech & Meshal Alharbi & Walid El-Shafai & Mostafa M. Fouda & Moustafa H. Aly & Dhivya Swaminathan & Sudhakar Sengan, 2023. "Soft Computing in Smart Grid with Decentralized Generation and Renewable Energy Storage System Planning," Energies, MDPI, vol. 16(6), pages 1-24, March.
    7. Israfil Hussain & Dulal Chandra Das & Nidul Sinha & Abdul Latif & S. M. Suhail Hussain & Taha Selim Ustun, 2020. "Performance Assessment of an Islanded Hybrid Power System with Different Storage Combinations Using an FPA-Tuned Two-Degree-of-Freedom (2DOF) Controller," Energies, MDPI, vol. 13(21), pages 1-20, October.

    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. Panda, Mitali & Nayak, Yogesh Kumar, 2022. "Impact analysis of renewable energy Distributed Generation in deregulated electricity markets: A Context of Transmission Congestion Problem," Energy, Elsevier, vol. 254(PC).
    2. Mahesh Kumar & Amir Mahmood Soomro & Waqar Uddin & Laveet Kumar, 2022. "Optimal Multi-Objective Placement and Sizing of Distributed Generation in Distribution System: A Comprehensive Review," Energies, MDPI, vol. 15(21), pages 1-48, October.
    3. Razavi, Seyed-Ehsan & Rahimi, Ehsan & Javadi, Mohammad Sadegh & Nezhad, Ali Esmaeel & Lotfi, Mohamed & Shafie-khah, Miadreza & Catalão, João P.S., 2019. "Impact of distributed generation on protection and voltage regulation of distribution systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 157-167.
    4. Sultana, U. & Khairuddin, Azhar B. & Aman, M.M. & Mokhtar, A.S. & Zareen, N., 2016. "A review of optimum DG placement based on minimization of power losses and voltage stability enhancement of distribution system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 63(C), pages 363-378.
    5. Sultana, U. & Khairuddin, Azhar B. & Sultana, Beenish & Rasheed, Nadia & Qazi, Sajid Hussain & Malik, Nimra Riaz, 2018. "Placement and sizing of multiple distributed generation and battery swapping stations using grasshopper optimizer algorithm," Energy, Elsevier, vol. 165(PA), pages 408-421.
    6. 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.
    7. Mahesh Kumar & Perumal Nallagownden & Irraivan Elamvazuthi, 2017. "Optimal Placement and Sizing of Renewable Distributed Generations and Capacitor Banks into Radial Distribution Systems," Energies, MDPI, vol. 10(6), pages 1-25, June.
    8. 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).
    9. Moradijoz, M. & Moghaddam, M. Parsa & Haghifam, M.R., 2018. "A flexible active distribution system expansion planning model: A risk-based approach," Energy, Elsevier, vol. 145(C), pages 442-457.
    10. Cabrera-Tobar, Ana & Bullich-Massagué, Eduard & Aragüés-Peñalba, Mònica & Gomis-Bellmunt, Oriol, 2016. "Review of advanced grid requirements for the integration of large scale photovoltaic power plants in the transmission system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 971-987.
    11. Nikkhah, Saman & Rabiee, Abbas, 2018. "Optimal wind power generation investment, considering voltage stability of power systems," Renewable Energy, Elsevier, vol. 115(C), pages 308-325.
    12. Monteiro, Raul V.A. & Guimarães, Geraldo C. & Silva, Fernando Bento & da Silva Teixeira, Raoni F. & Carvalho, Bismarck C. & Finazzi, Antônio de P. & de Vasconcellos, Arnulfo B., 2018. "A medium-term analysis of the reduction in technical losses on distribution systems with variable demand using artificial neural networks: An Electrical Energy Storage approach," Energy, Elsevier, vol. 164(C), pages 1216-1228.
    13. Das, Sangeeta & Das, Debapriya & Patra, Amit, 2019. "Operation of distribution network with optimal placement and sizing of dispatchable DGs and shunt capacitors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    14. Abbas Rabiee & Ali Abdali & Seyed Masoud Mohseni-Bonab & Mohsen Hazrati, 2021. "Risk-Averse Scheduling of Combined Heat and Power-Based Microgrids in Presence of Uncertain Distributed Energy Resources," Sustainability, MDPI, vol. 13(13), pages 1-24, June.
    15. Ji, Haoran & Wang, Chengshan & Li, Peng & Song, Guanyu & Yu, Hao & Wu, Jianzhong, 2019. "Quantified analysis method for operational flexibility of active distribution networks with high penetration of distributed generators," Applied Energy, Elsevier, vol. 239(C), pages 706-714.
    16. Derafshian, Mehdi & Amjady, Nima, 2015. "Optimal design of power system stabilizer for power systems including doubly fed induction generator wind turbines," Energy, Elsevier, vol. 84(C), pages 1-14.
    17. Kools, L. & Phillipson, F., 2016. "Data granularity and the optimal planning of distributed generation," Energy, Elsevier, vol. 112(C), pages 342-352.
    18. Xydas, Erotokritos & Marmaras, Charalampos & Cipcigan, Liana M., 2016. "A multi-agent based scheduling algorithm for adaptive electric vehicles charging," Applied Energy, Elsevier, vol. 177(C), pages 354-365.
    19. Nagaraju Dharavat & Suresh Kumar Sudabattula & Suresh Velamuri & Sachin Mishra & Naveen Kumar Sharma & Mohit Bajaj & Elmazeg Elgamli & Mokhtar Shouran & Salah Kamel, 2022. "Optimal Allocation of Renewable Distributed Generators and Electric Vehicles in a Distribution System Using the Political Optimization Algorithm," Energies, MDPI, vol. 15(18), pages 1-25, September.
    20. Tabandeh, Abbas & Abdollahi, Amir & Rashidinejad, Masoud, 2016. "Reliability constrained congestion management with uncertain negawatt demand response firms considering repairable advanced metering infrastructures," Energy, Elsevier, vol. 104(C), pages 213-228.

    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:147:y:2018:i:c:p:980-994. 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.