IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i4p894-d140587.html
   My bibliography  Save this article

Optimizing Operation Indices Considering Different Types of Distributed Generation in Microgrid Applications

Author

Listed:
  • Niloofar Ghanbari

    (Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC 27695, USA)

  • Hossein Mokhtari

    (Department of Electrical Engineering, Sharif University of Technology, P.O. Box 11365-11155 Tehran, Iran)

  • Subhashish Bhattacharya

    (Department of Electrical Engineering, Sharif University of Technology, P.O. Box 11365-11155 Tehran, Iran)

Abstract

The need for independent power generation has increased in recent years, especially with the growing demand in microgrid systems. In a microgrid with several generations of different types and with all kinds of loads of variable nature, an optimal power balance in the system has to be achieved. This optimal objective, which results in minimal energy losses over a specific period of time, requires an optimal location and sizing of the distributed generations (DGs) in a microgrid. This paper proposes a new optimization method in which both optimal location of the DGs and their generation profile according to the load demand profile as well as the type of DG are determined during the life time of the DGs. The types of DGs that are considered in this paper are diesel generators and wind turbine. The method is based on simultaneously minimizing the cost of the investment and operation of the DGs, the cost of power delivered by the the external grid as well as the cost of power losses in the network. The proposed method is tested on the IEEE standard radial distribution network considering time-varying loads and the wind speed every hour of a day.

Suggested Citation

  • Niloofar Ghanbari & Hossein Mokhtari & Subhashish Bhattacharya, 2018. "Optimizing Operation Indices Considering Different Types of Distributed Generation in Microgrid Applications," Energies, MDPI, vol. 11(4), pages 1-12, April.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:4:p:894-:d:140587
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/4/894/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/4/894/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Soares, João & Fotouhi Ghazvini, Mohammad Ali & Vale, Zita & de Moura Oliveira, P.B., 2016. "A multi-objective model for the day-ahead energy resource scheduling of a smart grid with high penetration of sensitive loads," Applied Energy, Elsevier, vol. 162(C), pages 1074-1088.
    2. Parvizimosaed, M. & Farmani, F. & Monsef, H. & Rahimi-Kian, A., 2017. "A multi-stage Smart Energy Management System under multiple uncertainties: A data mining approach," Renewable Energy, Elsevier, vol. 102(PA), pages 178-189.
    3. Bendato, Ilaria & Bonfiglio, Andrea & Brignone, Massimo & Delfino, Federico & Pampararo, Fabio & Procopio, Renato, 2017. "A real-time Energy Management System for the integration of economical aspects and system operator requirements: Definition and validation," Renewable Energy, Elsevier, vol. 102(PB), pages 406-416.
    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. Ghaeth Fandi & Ibrahim Ahmad & Famous O. Igbinovia & Zdenek Muller & Josef Tlusty & Vladimir Krepl, 2018. "Voltage Regulation and Power Loss Minimization in Radial Distribution Systems via Reactive Power Injection and Distributed Generation Unit Placement," Energies, MDPI, vol. 11(6), pages 1-17, May.
    2. Dolatiary, Soheil & Rahmani, Javad & Khalilzad, Zahra, 2018. "Optimum location of DG units considering operation conditions," MPRA Paper 94194, University Library of Munich, Germany, revised 2018.
    3. Farzaneh Pourahmadi & Payman Dehghanian, 2018. "A Game-Theoretic Loss Allocation Approach in Power Distribution Systems with High Penetration of Distributed Generations," Mathematics, MDPI, vol. 6(9), pages 1-14, September.
    4. Rahmani, Fatemeh, 2018. "Electric Vehicle Charger Based on DC/DC Converter Topology," MPRA Paper 108310, University Library of Munich, Germany, revised 01 Jul 2018.
    5. Kaitlyn J. Bunker & Wayne W. Weaver, 2018. "Optimal Multidimensional Droop Control for Wind Resources in DC Microgrids," Energies, MDPI, vol. 11(7), pages 1-20, July.
    6. Fatemeh Ghalavand & Behzad Asle Mohammadi Alizade & Hossam Gaber & Hadis Karimipour, 2018. "Microgrid Islanding Detection Based on Mathematical Morphology," Energies, MDPI, vol. 11(10), pages 1-18, 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. Sirat, Ali Parsa, 2018. "Loss Minimization through the Allocation of DGs Considering the Stochastic Nature of Units," MPRA Paper 87636, University Library of Munich, Germany.
    2. Motalleb, Mahdi & Thornton, Matsu & Reihani, Ehsan & Ghorbani, Reza, 2016. "A nascent market for contingency reserve services using demand response," Applied Energy, Elsevier, vol. 179(C), pages 985-995.
    3. Asaad Mohammad & Ramon Zamora & Tek Tjing Lie, 2020. "Integration of Electric Vehicles in the Distribution Network: A Review of PV Based Electric Vehicle Modelling," Energies, MDPI, vol. 13(17), pages 1-20, September.
    4. Thomas, Dimitrios & D’Hoop, Gaspard & Deblecker, Olivier & Genikomsakis, Konstantinos N. & Ioakimidis, Christos S., 2020. "An integrated tool for optimal energy scheduling and power quality improvement of a microgrid under multiple demand response schemes," Applied Energy, Elsevier, vol. 260(C).
    5. La Fata, Alice & Brignone, Massimo & Procopio, Renato & Bracco, Stefano & Delfino, Federico & Barbero, Giulia & Barilli, Riccardo, 2024. "An energy management system to schedule the optimal participation to electricity markets and a statistical analysis of the bidding strategies over long time horizons," Renewable Energy, Elsevier, vol. 228(C).
    6. 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.
    7. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    8. Cassettari, Lucia & Bendato, Ilaria & Mosca, Marco & Mosca, Roberto, 2017. "Energy Resources Intelligent Management using on line real-time simulation: A decision support tool for sustainable manufacturing," Applied Energy, Elsevier, vol. 190(C), pages 841-851.
    9. Wagner, Lukas Peter & Reinpold, Lasse Matthias & Kilthau, Maximilian & Fay, Alexander, 2023. "A systematic review of modeling approaches for flexible energy resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    10. Li, Pei-Hao & Pye, Steve, 2018. "Assessing the benefits of demand-side flexibility in residential and transport sectors from an integrated energy systems perspective," Applied Energy, Elsevier, vol. 228(C), pages 965-979.
    11. Tabar, Vahid Sohrabi & Ghassemzadeh, Saeid & Tohidi, Sajjad, 2019. "Energy management in hybrid microgrid with considering multiple power market and real time demand response," Energy, Elsevier, vol. 174(C), pages 10-23.
    12. Shanmugarajah Vinothine & Lidula N. Widanagama Arachchige & Athula D. Rajapakse & Roshani Kaluthanthrige, 2022. "Microgrid Energy Management and Methods for Managing Forecast Uncertainties," Energies, MDPI, vol. 15(22), pages 1-22, November.
    13. Ba Hung, Nguyen & Jaewon, Sung & Lim, Ocktaeck, 2017. "A study of the effects of input parameters on the dynamics and required power of an electric bicycle," Applied Energy, Elsevier, vol. 204(C), pages 1347-1362.
    14. Ameer A. Kareim Al-Sahlawi & Shahrin Md. Ayob & Chee Wei Tan & Hussein Mohammed Ridha & Dhafer Manea Hachim, 2024. "Optimal Design of Grid-Connected Hybrid Renewable Energy System Considering Electric Vehicle Station Using Improved Multi-Objective Optimization: Techno-Economic Perspectives," Sustainability, MDPI, vol. 16(6), pages 1-35, March.
    15. Leszczyński, Jacek S. & Gryboś, Dominik & Markowski, Jan, 2023. "Analysis of optimal expansion dynamics in a reciprocating drive for a micro-CAES production system," Applied Energy, Elsevier, vol. 350(C).
    16. Ju, Liwei & Zhao, Rui & Tan, Qinliang & Lu, Yan & Tan, Qingkun & Wang, Wei, 2019. "A multi-objective robust scheduling model and solution algorithm for a novel virtual power plant connected with power-to-gas and gas storage tank considering uncertainty and demand response," Applied Energy, Elsevier, vol. 250(C), pages 1336-1355.
    17. Rosini, A. & Labella, A. & Bonfiglio, A. & Procopio, R. & Guerrero, Josep M., 2021. "A review of reactive power sharing control techniques for islanded microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    18. Geng, Zhaowei & Conejo, Antonio J. & Chen, Qixin & Xia, Qing & Kang, Chongqing, 2017. "Electricity production scheduling under uncertainty: Max social welfare vs. min emission vs. max renewable production," Applied Energy, Elsevier, vol. 193(C), pages 540-549.
    19. Aghajani, Saemeh & Kalantar, Mohsen, 2017. "Operational scheduling of electric vehicles parking lot integrated with renewable generation based on bilevel programming approach," Energy, Elsevier, vol. 139(C), pages 422-432.
    20. Yim, Jaeyun & You, Sesun & Blaabjerg, Frede & Lee, Youngwoo & Gui, Yonghao & Kim, Wonhee, 2024. "Energy management systems for forecasted demand error compensation using hybrid energy storage system in nanogrid," Renewable Energy, Elsevier, vol. 221(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:gam:jeners:v:11:y:2018:i:4:p:894-:d:140587. 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.