IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v123y2018icp334-341.html
   My bibliography  Save this article

New method for optimal allocation of distribution generation aimed at active losses reduction

Author

Listed:
  • da Rosa, William M.
  • Teixeira, Julio C.
  • Belati, Edmarcio A.

Abstract

A new methodology for studying the effect of wind power intermittency on electric power systems is proposed in this paper. The proposed stochastic method is based on the optimal power flow and sensitivity analysis techniques. These techniques are applied on a computational tool capable of allocating the intermittent energy with an exhaustive search technique with low computation time. The methodology was applied to reduce losses in the distribution systems of 34 and 70 buses. The results are compared with fixed power allocation considering the mean power of three different conditions: the average wind speed; generator capacity factor and maximum generator capacity. They showed that a stochastic method that considers each value of wind speed is necessary to determine the correct bus to allocate intermittent wind power.

Suggested Citation

  • da Rosa, William M. & Teixeira, Julio C. & Belati, Edmarcio A., 2018. "New method for optimal allocation of distribution generation aimed at active losses reduction," Renewable Energy, Elsevier, vol. 123(C), pages 334-341.
    • Handle: RePEc:eee:renene:v:123:y:2018:i:c:p:334-341
    DOI: 10.1016/j.renene.2018.02.065
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S096014811830209X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2018.02.065?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. Rezaee Jordehi, Ahmad, 2016. "Allocation of distributed generation units in electric power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 893-905.
    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. Sadeghian, Hamidreza & Wang, Zhifang, 2020. "A novel impact-assessment framework for distributed PV installations in low-voltage secondary networks," Renewable Energy, Elsevier, vol. 147(P1), pages 2179-2194.
    2. 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.
    3. de Oliveira, Glauber Cardoso & Bertone, Edoardo & Stewart, Rodney A., 2022. "Challenges, opportunities, and strategies for undertaking integrated precinct-scale energy–water system planning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(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. Aouss Gabash & Pu Li, 2016. "On Variable Reverse Power Flow-Part II: An Electricity Market Model Considering Wind Station Size and Location," Energies, MDPI, vol. 9(4), pages 1-13, March.
    2. Hee-Kwan Shin & Jae-Min Cho & Eul-Bum Lee, 2019. "Electrical Power Characteristics and Economic Analysis of Distributed Generation System Using Renewable Energy: Applied to Iron and Steel Plants," Sustainability, MDPI, vol. 11(22), pages 1-27, November.
    3. 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.
    4. Reza Sirjani, 2018. "Optimal Placement and Sizing of PV-STATCOM in Power Systems Using Empirical Data and Adaptive Particle Swarm Optimization," Sustainability, MDPI, vol. 10(3), pages 1-15, March.
    5. 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).
    6. Chaduvula, Hemanth & Das, Debapriya, 2023. "Analysis of microgrid configuration with optimal power injection from grid using point estimate method embedded fuzzy-particle swarm optimization," Energy, Elsevier, vol. 282(C).
    7. Ana Lagos & Joaquín E. Caicedo & Gustavo Coria & Andrés Romero Quete & Maximiliano Martínez & Gastón Suvire & Jesús Riquelme, 2022. "State-of-the-Art Using Bibliometric Analysis of Wind-Speed and -Power Forecasting Methods Applied in Power Systems," Energies, MDPI, vol. 15(18), pages 1-40, September.
    8. Fathy, Ahmed, 2022. "A novel artificial hummingbird algorithm for integrating renewable based biomass distributed generators in radial distribution systems," Applied Energy, Elsevier, vol. 323(C).
    9. Eshan Karunarathne & Jagadeesh Pasupuleti & Janaka Ekanayake & Dilini Almeida, 2021. "The Optimal Placement and Sizing of Distributed Generation in an Active Distribution Network with Several Soft Open Points," Energies, MDPI, vol. 14(4), pages 1-20, February.
    10. Eshan Karunarathne & Jagadeesh Pasupuleti & Janaka Ekanayake & Dilini Almeida, 2020. "Optimal Placement and Sizing of DGs in Distribution Networks Using MLPSO Algorithm," Energies, MDPI, vol. 13(23), pages 1-25, November.
    11. Zhou, Kaile & Yang, Shanlin & Shao, Zhen, 2016. "Energy Internet: The business perspective," Applied Energy, Elsevier, vol. 178(C), pages 212-222.
    12. 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.
    13. 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).
    14. Fu, Xueqian & Guo, Qinglai & Sun, Hongbin & Pan, Zhaoguang & Xiong, Wen & Wang, Li, 2017. "Typical scenario set generation algorithm for an integrated energy system based on the Wasserstein distance metric," Energy, Elsevier, vol. 135(C), pages 153-170.
    15. Yu-Cheol Jeong & Eul-Bum Lee & Douglas Alleman, 2019. "Reducing Voltage Volatility with Step Voltage Regulators: A Life-Cycle Cost Analysis of Korean Solar Photovoltaic Distributed Generation," Energies, MDPI, vol. 12(4), pages 1-16, February.
    16. Rahmat Khezri & Amin Mahmoudi & Hirohisa Aki & S. M. Muyeen, 2021. "Optimal Planning of Remote Area Electricity Supply Systems: Comprehensive Review, Recent Developments and Future Scopes," Energies, MDPI, vol. 14(18), pages 1-29, September.
    17. Fu, Xueqian & Guo, Qinglai & Sun, Hongbin & Zhang, Xiurong & Wang, Li, 2017. "Estimation of the failure probability of an integrated energy system based on the first order reliability method," Energy, Elsevier, vol. 134(C), pages 1068-1078.
    18. Li, Bei & Roche, Robin & Paire, Damien & Miraoui, Abdellatif, 2018. "Optimal sizing of distributed generation in gas/electricity/heat supply networks," Energy, Elsevier, vol. 151(C), pages 675-688.
    19. Maen Z. Kreishan & Ahmed F. Zobaa, 2021. "Optimal Allocation and Operation of Droop-Controlled Islanded Microgrids: A Review," Energies, MDPI, vol. 14(15), pages 1-45, July.
    20. Luis Fernando Grisales-Noreña & Daniel Gonzalez Montoya & Carlos Andres Ramos-Paja, 2018. "Optimal Sizing and Location of Distributed Generators Based on PBIL and PSO Techniques," Energies, MDPI, vol. 11(4), pages 1-27, 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:renene:v:123:y:2018:i:c:p:334-341. 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/renewable-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.