IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v94y2016icp29-37.html
   My bibliography  Save this article

A new smart approach for state estimation of distribution grids considering renewable energy sources

Author

Listed:
  • Khorshidi, Reza
  • Shabaninia, Faridon
  • Niknam, Taher

Abstract

The idea of smart grids has created new opportunities in the electrical networks for monitoring the system status. One of the valuable and significant techniques for monitoring the smart grids is state estimation. In this way, this paper proposes a sufficient state estimation algorithm for inclusive monitoring of the distribution systems in the presence of RESs (renewable energy sources). The proposed method is a hybrid technique using WLS (weighted least square) method and FA (firefly algorithm) to reach more reliable and accurate state estimation of the network. FA is equipped with new optimization operators that make it possible to solve the multi-modal problems using an automatic sub-division feature. In order to improve the overall search ability of the algorithm, a new two-phase modification method is proposed. The proposed hybrid method can estimate the voltage angle using the WLS method. The simulation results show more optimal cost function value with faster response with escaping from the several local optima of the problem.

Suggested Citation

  • Khorshidi, Reza & Shabaninia, Faridon & Niknam, Taher, 2016. "A new smart approach for state estimation of distribution grids considering renewable energy sources," Energy, Elsevier, vol. 94(C), pages 29-37.
  • Handle: RePEc:eee:energy:v:94:y:2016:i:c:p:29-37
    DOI: 10.1016/j.energy.2015.10.096
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544215014735
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2015.10.096?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. Bahmani-Firouzi, Bahman & Farjah, Ebrahim & Azizipanah-Abarghooee, Rasoul, 2013. "An efficient scenario-based and fuzzy self-adaptive learning particle swarm optimization approach for dynamic economic emission dispatch considering load and wind power uncertainties," Energy, Elsevier, vol. 50(C), pages 232-244.
    2. Niknam, Taher & Firouzi, Bahman Bahmani, 2009. "A practical algorithm for distribution state estimation including renewable energy sources," Renewable Energy, Elsevier, vol. 34(11), pages 2309-2316.
    3. Baziar, Aliasghar & Kavousi-Fard, Abdollah, 2013. "Considering uncertainty in the optimal energy management of renewable micro-grids including storage devices," Renewable Energy, Elsevier, vol. 59(C), pages 158-166.
    4. Niknam, Taher & Kavousi Fard, Abdollah & Baziar, Aliasghar, 2012. "Multi-objective stochastic distribution feeder reconfiguration problem considering hydrogen and thermal energy production by fuel cell power plants," Energy, Elsevier, vol. 42(1), pages 563-573.
    5. Georgilakis, Pavlos S. & Katsigiannis, Yiannis A., 2009. "Reliability and economic evaluation of small autonomous power systems containing only renewable energy sources," Renewable Energy, Elsevier, vol. 34(1), pages 65-70.
    6. Kavousi-Fard, Abdollah & Abunasri, Alireza & Zare, Alireza & Hoseinzadeh, Rasool, 2014. "Impact of plug-in hybrid electric vehicles charging demand on the optimal energy management of renewable micro-grids," Energy, Elsevier, vol. 78(C), pages 904-915.
    7. 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.
    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. István Táczi & Bálint Sinkovics & István Vokony & Bálint Hartmann, 2021. "The Challenges of Low Voltage Distribution System State Estimation—An Application Oriented Review," Energies, MDPI, vol. 14(17), pages 1-17, August.
    2. Sepideh Radhoush & Maryam Bahramipanah & Hashem Nehrir & Zagros Shahooei, 2022. "A Review on State Estimation Techniques in Active Distribution Networks: Existing Practices and Their Challenges," Sustainability, MDPI, vol. 14(5), pages 1-16, February.
    3. Anna Glazunova & Evgenii Semshikov & Michael Negnevitsky, 2021. "Real-Time Flexibility Assessment for Power Systems with High Wind Energy Penetration," Mathematics, MDPI, vol. 9(17), pages 1-16, August.
    4. Israa T. Aziz & Hai Jin & Ihsan H. Abdulqadder & Sabah M. Alturfi & Wisam H. Alobaidi & Firas M.F. Flaih, 2019. "T 2 S 2 G: A Novel Two-Tier Secure Smart Grid Architecture to Protect Network Measurements," Energies, MDPI, vol. 12(13), pages 1-24, July.
    5. Ahmad, Fiaz & Rasool, Akhtar & Ozsoy, Emre & Sekar, Raja & Sabanovic, Asif & Elitaş, Meltem, 2018. "Distribution system state estimation-A step towards smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2659-2671.
    6. Cisneros-Magaña, Rafael & Medina-Rios, Aurelio & Fuerte-Esquivel, Claudio R. & Segundo-Ramírez, Juan, 2022. "Harmonic state estimation based on discrete exponential expansion, singular value decomposition and a variable measurement model," Energy, Elsevier, vol. 249(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. 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.
    2. Tabatabaee, Sajad & Mortazavi, Seyed Saeedallah & Niknam, Taher, 2017. "Stochastic scheduling of local distribution systems considering high penetration of plug-in electric vehicles and renewable energy sources," Energy, Elsevier, vol. 121(C), pages 480-490.
    3. Ben Christopher, S.J. & Carolin Mabel, M., 2020. "A bio-inspired approach for probabilistic energy management of micro-grid incorporating uncertainty in statistical cost estimation," Energy, Elsevier, vol. 203(C).
    4. Javidsharifi, Mahshid & Niknam, Taher & Aghaei, Jamshid & Mokryani, Geev, 2018. "Multi-objective short-term scheduling of a renewable-based microgrid in the presence of tidal resources and storage devices," Applied Energy, Elsevier, vol. 216(C), pages 367-381.
    5. Abdi, Hamdi & Beigvand, Soheil Derafshi & Scala, Massimo La, 2017. "A review of optimal power flow studies applied to smart grids and microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 742-766.
    6. Kasaei, Mohammad Javad & Gandomkar, Majid & Nikoukar, Javad, 2017. "Optimal management of renewable energy sources by virtual power plant," Renewable Energy, Elsevier, vol. 114(PB), pages 1180-1188.
    7. 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.
    8. Praveen Agrawal & Neeraj Kanwar & Nikhil Gupta & Khaleequr Rehman Niazi & Anil Swarnkar & Nand K. Meena & Jin Yang, 2020. "Reliability and Network Performance Enhancement by Reconfiguring Underground Distribution Systems," Energies, MDPI, vol. 13(18), pages 1-16, September.
    9. Niknam, Taher & Meymand, Hamed Zeinoddini & Nayeripour, Majid, 2010. "A practical algorithm for optimal operation management of distribution network including fuel cell power plants," Renewable Energy, Elsevier, vol. 35(8), pages 1696-1714.
    10. 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.
    11. 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.
    12. Najibi, Fatemeh & Niknam, Taher & Kavousi-Fard, Abdollah, 2016. "Optimal stochastic management of renewable MG (micro-grids) considering electro-thermal model of PV (photovoltaic)," Energy, Elsevier, vol. 97(C), pages 444-459.
    13. Kavousi-Fard, Abdollah & Khodaei, Amin, 2016. "Efficient integration of plug-in electric vehicles via reconfigurable microgrids," Energy, Elsevier, vol. 111(C), pages 653-663.
    14. Sarshar, Javad & Moosapour, Seyyed Sajjad & Joorabian, Mahmood, 2017. "Multi-objective energy management of a micro-grid considering uncertainty in wind power forecasting," Energy, Elsevier, vol. 139(C), pages 680-693.
    15. Aziz, Muhammad & Oda, Takuya & Ito, Masakazu, 2016. "Battery-assisted charging system for simultaneous charging of electric vehicles," Energy, Elsevier, vol. 100(C), pages 82-90.
    16. Wen, Xin & Abbes, Dhaker & Francois, Bruno, 2021. "Modeling of photovoltaic power uncertainties for impact analysis on generation scheduling and cost of an urban micro grid," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 183(C), pages 116-128.
    17. Li, Shuangqi & Zhao, Pengfei & Gu, Chenghong & Huo, Da & Zeng, Xianwu & Pei, Xiaoze & Cheng, Shuang & Li, Jianwei, 2022. "Online battery-protective vehicle to grid behavior management," Energy, Elsevier, vol. 243(C).
    18. Yin, Yue & Liu, Tianqi & He, Chuan, 2019. "Day-ahead stochastic coordinated scheduling for thermal-hydro-wind-photovoltaic systems," Energy, Elsevier, vol. 187(C).
    19. Aghajani, Saemeh & Kalantar, Mohsen, 2017. "Optimal scheduling of distributed energy resources in smart grids: A complementarity approach," Energy, Elsevier, vol. 141(C), pages 2135-2144.
    20. Yong Zeng & Yanpeng Cai & Guohe Huang & Jing Dai, 2011. "A Review on Optimization Modeling of Energy Systems Planning and GHG Emission Mitigation under Uncertainty," Energies, MDPI, vol. 4(10), pages 1-33, October.

    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:94:y:2016:i:c:p:29-37. 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.