IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v59y2013icp698-707.html
   My bibliography  Save this article

Distribution system reconfiguration for energy loss reduction considering the variability of load and local renewable generation

Author

Listed:
  • Zidan, Aboelsood
  • El-Saadany, Ehab F.

Abstract

The interconnection of renewable energy sources with distribution systems is attracting increasing interest because these renewable sources are inexhaustible and nonpolluting. Wind and photovoltaic are among the most mature of these energy sources, and their penetration continues to increase. In this paper a method based on GA (genetic algorithm) is presented to investigate the distribution system reconfiguration problem taking into consideration the effect of load variation and the stochastic power generation of renewable DG (distributed generators units). The presented method determines the annual distribution network reconfiguration scheme considering switching operation costs in order to minimize annual energy losses by determining the optimal configuration for each season of the year. The uncertainties related to DG power and varying load are considered by the creation of a probabilistic generation-load model that combines all possible operating conditions of the renewable DG units with the probability of their occurrence, followed by the incorporation of this model into the reconfiguration problem. The constraints include the voltage limits, the line current limits, the radial topology, and feeding of all loads. In order to evaluate the effectiveness of the proposed method, both balanced and unbalanced distribution systems are used as case studies.

Suggested Citation

  • Zidan, Aboelsood & El-Saadany, Ehab F., 2013. "Distribution system reconfiguration for energy loss reduction considering the variability of load and local renewable generation," Energy, Elsevier, vol. 59(C), pages 698-707.
  • Handle: RePEc:eee:energy:v:59:y:2013:i:c:p:698-707
    DOI: 10.1016/j.energy.2013.06.061
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544213005604
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2013.06.061?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. Niknam, Taher & Golestaneh, Faranak & Malekpour, Ahmadreza, 2012. "Probabilistic energy and operation management of a microgrid containing wind/photovoltaic/fuel cell generation and energy storage devices based on point estimate method and self-adaptive gravitational," Energy, Elsevier, vol. 43(1), pages 427-437.
    2. Salgado, R.S. & Rangel, E.L., 2012. "Optimal power flow solutions through multi-objective programming," Energy, Elsevier, vol. 42(1), pages 35-45.
    3. Soares, J. & Silva, M. & Sousa, T. & Vale, Z. & Morais, H., 2012. "Distributed energy resource short-term scheduling using Signaled Particle Swarm Optimization," Energy, Elsevier, vol. 42(1), pages 466-476.
    4. Fonseca, Jimeno A. & Schlueter, Arno, 2013. "Novel approach for decentralized energy supply and energy storage of tall buildings in Latin America based on renewable energy sources: Case study – Informal vertical community Torre David, Caracas – ," Energy, Elsevier, vol. 53(C), pages 93-105.
    5. 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.
    6. Doagou-Mojarrad, Hasan & Gharehpetian, G.B. & Rastegar, H. & Olamaei, Javad, 2013. "Optimal placement and sizing of DG (distributed generation) units in distribution networks by novel hybrid evolutionary algorithm," Energy, Elsevier, vol. 54(C), pages 129-138.
    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. Lobão, J.A. & Devezas, T. & Catalão, J.P.S., 2014. "Influence of cable losses on the economic analysis of efficient and sustainable electrical equipment," Energy, Elsevier, vol. 65(C), pages 145-151.
    2. Ferrari, M.L. & Cuneo, A. & Pascenti, M. & Traverso, A., 2017. "Real-time state of charge estimation in thermal storage vessels applied to a smart polygeneration grid," Applied Energy, Elsevier, vol. 206(C), pages 90-100.
    3. Zidan, Aboelsood & Gabbar, Hossam A. & Eldessouky, Ahmed, 2015. "Optimal planning of combined heat and power systems within microgrids," Energy, Elsevier, vol. 93(P1), pages 235-244.
    4. Hashim, Haslenda & Ho, Wai Shin & Lim, Jeng Shiun & Macchietto, Sandro, 2014. "Integrated biomass and solar town: Incorporation of load shifting and energy storage," Energy, Elsevier, vol. 75(C), pages 31-39.
    5. Bornapour, Mosayeb & Hooshmand, Rahmat-Allah, 2015. "An efficient scenario-based stochastic programming for optimal planning of combined heat, power, and hydrogen production of molten carbonate fuel cell power plants," Energy, Elsevier, vol. 83(C), pages 734-748.
    6. Tolabi, Hajar Bagheri & Ali, Mohd Hasan & Shahrin Bin Md Ayob, & Rizwan, M., 2014. "Novel hybrid fuzzy-Bees algorithm for optimal feeder multi-objective reconfiguration by considering multiple-distributed generation," Energy, Elsevier, vol. 71(C), pages 507-515.
    7. Fathabadi, Hassan, 2015. "Utilization of electric vehicles and renewable energy sources used as distributed generators for improving characteristics of electric power distribution systems," Energy, Elsevier, vol. 90(P1), pages 1100-1110.
    8. Koh, Siong Lee & Lim, Yun Seng, 2016. "Methodology for assessing viability of energy storage system for buildings," Energy, Elsevier, vol. 101(C), pages 519-531.
    9. Mukhopadhyay, Bineeta & Das, Debapriya, 2020. "Multi-objective dynamic and static reconfiguration with optimized allocation of PV-DG and battery energy storage system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    10. Zare, Mohsen & Niknam, Taher & Azizipanah-Abarghooee, Rasoul & Amiri, Babak, 2014. "Multi-objective probabilistic reactive power and voltage control with wind site correlations," Energy, Elsevier, vol. 66(C), pages 810-822.
    11. 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.
    12. 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.
    13. kianmehr, Ehsan & Nikkhah, Saman & Rabiee, Abbas, 2019. "Multi-objective stochastic model for joint optimal allocation of DG units and network reconfiguration from DG owner’s and DisCo’s perspectives," Renewable Energy, Elsevier, vol. 132(C), pages 471-485.
    14. Manvir Kaur & Smarajit Ghosh, 2017. "Effective Loss Minimization and Allocation of Unbalanced Distribution Network," Energies, MDPI, vol. 10(12), pages 1-17, November.
    15. 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.
    16. Gutiérrez-Alcaraz, G. & Galván, E. & González-Cabrera, N. & Javadi, M.S., 2015. "Renewable energy resources short-term scheduling and dynamic network reconfiguration for sustainable energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 256-264.
    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. Badran, Ola & Mekhilef, Saad & Mokhlis, Hazlie & Dahalan, Wardiah, 2017. "Optimal reconfiguration of distribution system connected with distributed generations: A review of different methodologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 854-867.

    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. Bornapour, Mosayeb & Hooshmand, Rahmat-Allah, 2015. "An efficient scenario-based stochastic programming for optimal planning of combined heat, power, and hydrogen production of molten carbonate fuel cell power plants," Energy, Elsevier, vol. 83(C), pages 734-748.
    2. 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).
    3. Deihimi, Ali & Keshavarz Zahed, Babak & Iravani, Reza, 2016. "An interactive operation management of a micro-grid with multiple distributed generations using multi-objective uniform water cycle algorithm," Energy, Elsevier, vol. 106(C), pages 482-509.
    4. Kim, Jonghoon & Cho, B.H., 2013. "Screening process-based modeling of the multi-cell battery string in series and parallel connections for high accuracy state-of-charge estimation," Energy, Elsevier, vol. 57(C), pages 581-599.
    5. Zidan, Aboelsood & El-Saadany, Ehab F., 2013. "Incorporating load variation and variable wind generation in service restoration plans for distribution systems," Energy, Elsevier, vol. 57(C), pages 682-691.
    6. 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.
    7. Sousa, Tiago & Vale, Zita & Carvalho, Joao Paulo & Pinto, Tiago & Morais, Hugo, 2014. "A hybrid simulated annealing approach to handle energy resource management considering an intensive use of electric vehicles," Energy, Elsevier, vol. 67(C), pages 81-96.
    8. 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.
    9. Khan, Mohd Shariq & Lee, Moonyong, 2013. "Design optimization of single mixed refrigerant natural gas liquefaction process using the particle swarm paradigm with nonlinear constraints," Energy, Elsevier, vol. 49(C), pages 146-155.
    10. Wang, Xiaoxiao & Liu, Xiangfeng, 2015. "Blue Star: The proposed energy efficient tall building in Chicago and vertical city strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 241-259.
    11. Barik, Soumyabrata & Das, Debapriya, 2020. "A novel Q−PQV bus pair method of biomass DGs placement in distribution networks to maintain the voltage of remotely located buses," Energy, Elsevier, vol. 194(C).
    12. Taimoor Ahmad Khan & Amjad Ullah & Ghulam Hafeez & Imran Khan & Sadia Murawwat & Faheem Ali & Sajjad Ali & Sheraz Khan & Khalid Rehman, 2022. "A Fractional Order Super Twisting Sliding Mode Controller for Energy Management in Smart Microgrid Using Dynamic Pricing Approach," Energies, MDPI, vol. 15(23), pages 1-14, November.
    13. 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.
    14. Narang, Nitin & Dhillon, J.S. & Kothari, D.P., 2012. "Multiobjective fixed head hydrothermal scheduling using integrated predator-prey optimization and Powell search method," Energy, Elsevier, vol. 47(1), pages 237-252.
    15. J. Rajalakshmi & S. Durairaj, 2021. "Application of multi-objective optimization algorithm for siting and sizing of distributed generations in distribution networks," Journal of Combinatorial Optimization, Springer, vol. 41(2), pages 267-289, February.
    16. Firouzmakan, Pouya & Hooshmand, Rahmat-Allah & Bornapour, Mosayeb & Khodabakhshian, Amin, 2019. "A comprehensive stochastic energy management system of micro-CHP units, renewable energy sources and storage systems in microgrids considering demand response programs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 355-368.
    17. Miguel Carpintero-Rentería & David Santos-Martín & Josep M. Guerrero, 2019. "Microgrids Literature Review through a Layers Structure," Energies, MDPI, vol. 12(22), pages 1-22, November.
    18. Cui, Yunfei & Geng, Zhiqiang & Zhu, Qunxiong & Han, Yongming, 2017. "Review: Multi-objective optimization methods and application in energy saving," Energy, Elsevier, vol. 125(C), pages 681-704.
    19. Niknam, Taher & Azizipanah-Abarghooee, Rasoul & Narimani, Mohammad Rasoul, 2012. "Reserve constrained dynamic optimal power flow subject to valve-point effects, prohibited zones and multi-fuel constraints," Energy, Elsevier, vol. 47(1), pages 451-464.
    20. Zare, Mohsen & Niknam, Taher, 2013. "A new multi-objective for environmental and economic management of Volt/Var Control considering renewable energy resources," Energy, Elsevier, vol. 55(C), pages 236-252.

    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:59:y:2013:i:c:p:698-707. 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.