IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i20p7127-d1261823.html
   My bibliography  Save this article

Optimal Allocation of Battery Energy Storage Systems to Enhance System Performance and Reliability in Unbalanced Distribution Networks

Author

Listed:
  • Dong Zhang

    (School of Engineering and Energy, College of Science, Technology, Engineering and Mathematics, Murdoch University, Perth, WA 6150, Australia)

  • GM Shafiullah

    (School of Engineering and Energy, College of Science, Technology, Engineering and Mathematics, Murdoch University, Perth, WA 6150, Australia)

  • Choton Kanti Das

    (School of Engineering, Edith Cowan University, Perth, WA 6027, Australia)

  • Kok Wai Wong

    (School of Information Technology, College of Science, Technology, Engineering and Mathematics, Murdoch University, Perth, WA 6150, Australia)

Abstract

The continuously increasing renewable distributed generation (DG) penetration rate significantly reduces environmental pollution and power generation cost and satisfies society’s rapid growth in electricity demand. Nevertheless, high penetration of renewable DGs, such as wind power and photovoltaics (PV), might deteriorate the system’s efficiency and reliability due to its intermittent and stochastic natures. Introducing battery energy storage systems (BESSs) to the distribution system provides a practical method to compensate for the above deficiency since it can deliver and absorb power when needed. Hence, it is important to determine the optimal allocation of BESS to achieve maximum assistance in the grid. This study proposes an optimal BESS allocation methodology to improve reliability and economics in unbalanced distribution systems. The optimal BESS allocation problem is solved by simultaneously minimizing the cost of energy interruption, expected energy not supplied, power loss, line loading, voltage deviation, and BESS cost. The proposed technique is implemented and analyzed on a high renewable DG penetrated unbalanced IEEE-33 bus network using DIgSILENT PowerFactory software (version 2020 SP2A). An enhanced grey wolf optimization (EGWO) algorithm is developed to optimize BESS location and size according to the selected objective function. The simulation results show that the proposed optimal BESS optimization technique significantly improves the economics and reliability in unbalanced distribution systems and the EGWO outperforms the gray wolf optimization (GWO) and particle swarm optimization (PSO) algorithms.

Suggested Citation

  • Dong Zhang & GM Shafiullah & Choton Kanti Das & Kok Wai Wong, 2023. "Optimal Allocation of Battery Energy Storage Systems to Enhance System Performance and Reliability in Unbalanced Distribution Networks," Energies, MDPI, vol. 16(20), pages 1-35, October.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:20:p:7127-:d:1261823
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/20/7127/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/20/7127/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Saboori, Hedayat & Hemmati, Reza & Jirdehi, Mehdi Ahmadi, 2015. "Reliability improvement in radial electrical distribution network by optimal planning of energy storage systems," Energy, Elsevier, vol. 93(P2), pages 2299-2312.
    2. Shafiullah, G.M., 2016. "Hybrid renewable energy integration (HREI) system for subtropical climate in Central Queensland, Australia," Renewable Energy, Elsevier, vol. 96(PA), pages 1034-1053.
    3. Kim, Insu, 2018. "Optimal capacity of storage systems and photovoltaic systems able to control reactive power using the sensitivity analysis method," Energy, Elsevier, vol. 150(C), pages 642-652.
    4. Ikeda, Shintaro & Ooka, Ryozo, 2015. "Metaheuristic optimization methods for a comprehensive operating schedule of battery, thermal energy storage, and heat source in a building energy system," Applied Energy, Elsevier, vol. 151(C), pages 192-205.
    5. Mingrui Zhang & Ming Gan & Luyao Li, 2019. "Sizing and Siting of Distributed Generators and Energy Storage in a Microgrid Considering Plug-in Electric Vehicles," Energies, MDPI, vol. 12(12), pages 1-17, June.
    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. Adefarati, T. & Bansal, R.C., 2017. "Reliability and economic assessment of a microgrid power system with the integration of renewable energy resources," Applied Energy, Elsevier, vol. 206(C), pages 911-933.
    8. Adefarati, T. & Bansal, R.C., 2017. "Reliability assessment of distribution system with the integration of renewable distributed generation," Applied Energy, Elsevier, vol. 185(P1), pages 158-171.
    9. Guido Carpinelli & Fabio Mottola & Christian Noce & Angela Russo & Pietro Varilone, 2018. "A New Hybrid Approach Using the Simultaneous Perturbation Stochastic Approximation Method for the Optimal Allocation of Electrical Energy Storage Systems," Energies, MDPI, vol. 11(6), pages 1-20, June.
    10. Das, Choton K. & Bass, Octavian & Kothapalli, Ganesh & Mahmoud, Thair S. & Habibi, Daryoush, 2018. "Optimal placement of distributed energy storage systems in distribution networks using artificial bee colony algorithm," Applied Energy, Elsevier, vol. 232(C), pages 212-228.
    11. 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.
    Full references (including those not matched with items on IDEAS)

    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. Mendoza-Vizcaino, Javier & Raza, Muhammad & Sumper, Andreas & Díaz-González, Francisco & Galceran-Arellano, Samuel, 2019. "Integral approach to energy planning and electric grid assessment in a renewable energy technology integration for a 50/50 target applied to a small island," Applied Energy, Elsevier, vol. 233, pages 524-543.
    2. Adefarati, T. & Bansal, R.C., 2019. "Reliability, economic and environmental analysis of a microgrid system in the presence of renewable energy resources," Applied Energy, Elsevier, vol. 236(C), pages 1089-1114.
    3. Escalera, Alberto & Hayes, Barry & Prodanović, Milan, 2018. "A survey of reliability assessment techniques for modern distribution networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 344-357.
    4. Mousavi, Navid & Kothapalli, Ganesh & Habibi, Daryoush & Das, Choton K. & Baniasadi, Ali, 2020. "A novel photovoltaic-pumped hydro storage microgrid applicable to rural areas," Applied Energy, Elsevier, vol. 262(C).
    5. Singh, Pushpendra & Meena, Nand K. & Yang, Jin & Vega-Fuentes, Eduardo & Bishnoi, Shree Krishna, 2020. "Multi-criteria decision making monarch butterfly optimization for optimal distributed energy resources mix in distribution networks," Applied Energy, Elsevier, vol. 278(C).
    6. Elkholy, M.H. & Metwally, Hamid & Farahat, M.A. & Senjyu, Tomonobu & Elsayed Lotfy, Mohammed, 2022. "Smart centralized energy management system for autonomous microgrid using FPGA," Applied Energy, Elsevier, vol. 317(C).
    7. Hossain, Eklas & Roy, Shidhartho & Mohammad, Naeem & Nawar, Nafiu & Dipta, Debopriya Roy, 2021. "Metrics and enhancement strategies for grid resilience and reliability during natural disasters," Applied Energy, Elsevier, vol. 290(C).
    8. Mohammad Mehdi Lotfinejad & Reza Hafezi & Majid Khanali & Seyed Sina Hosseini & Mehdi Mehrpooya & Shahaboddin Shamshirband, 2018. "A Comparative Assessment of Predicting Daily Solar Radiation Using Bat Neural Network (BNN), Generalized Regression Neural Network (GRNN), and Neuro-Fuzzy (NF) System: A Case Study," Energies, MDPI, vol. 11(5), pages 1-15, May.
    9. Adefarati, T. & Bansal, R.C. & Bettayeb, M. & Naidoo, R., 2021. "Optimal energy management of a PV-WTG-BSS-DG microgrid system," Energy, Elsevier, vol. 217(C).
    10. Hassen Soualah & Gurvan Jodin & Roman Le Goff Latimier & Hamid Ben Ahmed, 2023. "Energy Not Exchanged: A Metric to Quantify Energy Resilience in Smart Grids," Sustainability, MDPI, vol. 15(3), pages 1-18, February.
    11. Shen, Boyang & Chen, Yu & Li, Chuanyue & Wang, Sheng & Chen, Xiaoyuan, 2021. "Superconducting fault current limiter (SFCL): Experiment and the simulation from finite-element method (FEM) to power/energy system software," Energy, Elsevier, vol. 234(C).
    12. Maciej Kuboń & Zbigniew Skibko & Sylwester Tabor & Urszula Malaga-Toboła & Andrzej Borusiewicz & Wacław Romaniuk & Janusz Zarajczyk & Pavel Neuberger, 2023. "Analysis of Voltage Distortions in the Power Grid Arising from Agricultural Biogas Plant Operation," Energies, MDPI, vol. 16(17), pages 1-21, August.
    13. Olga A. Filina & Nikita V. Martyushev & Boris V. Malozyomov & Vadim Sergeevich Tynchenko & Viktor Alekseevich Kukartsev & Kirill Aleksandrovich Bashmur & Pavel P. Pavlov & Tatyana Aleksandrovna Panfil, 2023. "Increasing the Efficiency of Diagnostics in the Brush-Commutator Assembly of a Direct Current Electric Motor," Energies, MDPI, vol. 17(1), pages 1-24, December.
    14. Sun, Xu & Liu, Yanli & Deng, Liangchen, 2020. "Reliability assessment of cyber-physical distribution network based on the fault tree," Renewable Energy, Elsevier, vol. 155(C), pages 1411-1424.
    15. Panyawoot Boonluk & Sirote Khunkitti & Pradit Fuangfoo & Apirat Siritaratiwat, 2021. "Optimal Siting and Sizing of Battery Energy Storage: Case Study Seventh Feeder at Nakhon Phanom Substation in Thailand," Energies, MDPI, vol. 14(5), pages 1-20, March.
    16. Nikita V. Martyushev & Boris V. Malozyomov & Olga A. Filina & Svetlana N. Sorokova & Egor A. Efremenkov & Denis V. Valuev & Mengxu Qi, 2023. "Stochastic Models and Processing Probabilistic Data for Solving the Problem of Improving the Electric Freight Transport Reliability," Mathematics, MDPI, vol. 11(23), pages 1-19, November.
    17. Khalid Almutairi & Salem Algarni & Talal Alqahtani & Hossein Moayedi & Amir Mosavi, 2022. "A TLBO-Tuned Neural Processor for Predicting Heating Load in Residential Buildings," Sustainability, MDPI, vol. 14(10), pages 1-19, May.
    18. Fitsum Salehu Kebede & Jean-Christophe Olivier & Salvy Bourguet & Mohamed Machmoum, 2021. "Reliability Evaluation of Renewable Power Systems through Distribution Network Power Outage Modelling," Energies, MDPI, vol. 14(11), pages 1-25, May.
    19. Parwal, Arvind & Fregelius, Martin & Temiz, Irinia & Göteman, Malin & Oliveira, Janaina G. de & Boström, Cecilia & Leijon, Mats, 2018. "Energy management for a grid-connected wave energy park through a hybrid energy storage system," Applied Energy, Elsevier, vol. 231(C), pages 399-411.
    20. Merad, Faycel & Labar, Hocine & Samira KELAIAIA, Mounia & Necaibia, Salah & Djelailia, Okba, 2019. "A maximum power control based on flexible collector applied to concentrator solar power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 315-331.

    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:16:y:2023:i:20:p:7127-:d:1261823. 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.