IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v320y2025ics0360544225010138.html
   My bibliography  Save this article

Optimal allocation of distributed generation and storage systems using multi-dimensional weighted least distance optimization and water droplet algorithms

Author

Listed:
  • Kim, Insu

Abstract

The installation of distributed generation and energy storage systems inevitably affects power system conditions such as short-circuit current, but other previous studies do not consider such an operational constraint (e.g., short-circuit current) in an optimization problem of distributed generation and energy storage systems. Therefore, the objective of this study is to optimize the type and total capacity of distributed generation and energy storage systems in terms of life cycle costs, voltage magnitude and loss variations, reliability (e.g., energy not supplied index), and protection (e.g., short-circuit current). For this purpose, this study presents a multidimensional optimization method that measures the weighted least distance in each objective function (e.g., cost, voltage, loss, reliability, and short-circuit current). This study also extends the traditional enumeration method with the water droplet algorithm. Our optimization results also show that the optimal type and capacity of a microturbine (an example of distributed generation) and a flywheel battery (an example of energy storage) can be successfully determined by the proposed approach. For example, for a residential community in Atlanta, USA with a total capacity of 2.76 MW and a radial distribution system (e.g., IEEE 34-bus test feeder), microturbines with a total capacity of 0.19 p.u. or 520 kW are optimal. If energy storage is to be installed, a combination of microturbine (0.14 p.u. or 390 kW) and flywheel (0.009 p.u. or 25 kWh) is optimal.

Suggested Citation

  • Kim, Insu, 2025. "Optimal allocation of distributed generation and storage systems using multi-dimensional weighted least distance optimization and water droplet algorithms," Energy, Elsevier, vol. 320(C).
  • Handle: RePEc:eee:energy:v:320:y:2025:i:c:s0360544225010138
    DOI: 10.1016/j.energy.2025.135371
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225010138
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2025.135371?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Azeredo, Lucas F.S. & Yahyaoui, Imene & Fiorotti, Rodrigo & Fardin, Jussara F. & Garcia-Pereira, Hilel & Rocha, Helder R.O., 2023. "Study of reducing losses, short-circuit currents and harmonics by allocation of distributed generation, capacitor banks and fault current limiters in distribution grids," Applied Energy, Elsevier, vol. 350(C).
    2. Ferminus Raj, A. & Gnana Saravanan, A., 2023. "An optimization approach for optimal location & size of DSTATCOM and DG," Applied Energy, Elsevier, vol. 336(C).
    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. Jiaxin Lu & Weijun Wang & Yingchao Zhang & Song Cheng, 2017. "Multi-Objective Optimal Design of Stand-Alone Hybrid Energy System Using Entropy Weight Method Based on HOMER," Energies, MDPI, vol. 10(10), pages 1-17, October.
    5. Haesung Jo & Jaemin Park & Insu Kim, 2021. "Environmentally Constrained Optimal Dispatch Method for Combined Cooling, Heating, and Power Systems Using Two-Stage Optimization," Energies, MDPI, vol. 14(14), pages 1-20, July.
    6. Liu, Zhijian & Fan, Guangyao & Sun, Dekang & Wu, Di & Guo, Jiacheng & Zhang, Shicong & Yang, Xinyan & Lin, Xianping & Ai, Lei, 2022. "A novel distributed energy system combining hybrid energy storage and a multi-objective optimization method for nearly zero-energy communities and buildings," Energy, Elsevier, vol. 239(PE).
    7. Lai, Ching-Ming & Teh, Jiashen, 2022. "Network topology optimisation based on dynamic thermal rating and battery storage systems for improved wind penetration and reliability," Applied Energy, Elsevier, vol. 305(C).
    8. Dini, Anoosh & Hassankashi, Alireza & Pirouzi, Sasan & Lehtonen, Matti & Arandian, Behdad & Baziar, Ali Asghar, 2022. "A flexible-reliable operation optimization model of the networked energy hubs with distributed generations, energy storage systems and demand response," Energy, Elsevier, vol. 239(PA).
    9. Mohamad, Farihan & Teh, Jiashen & Lai, Ching-Ming, 2021. "Optimum allocation of battery energy storage systems for power grid enhanced with solar energy," Energy, Elsevier, vol. 223(C).
    10. Jaemin Park & Haesung Jo & Insu Kim, 2021. "The Selection of the Most Cost-Efficient Distributed Generation Type for a Combined Cooling Heat and Power System Used for Metropolitan Residential Customers," Energies, MDPI, vol. 14(18), pages 1-25, September.
    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. Insu Kim & Beopsoo Kim & Denis Sidorov, 2022. "Machine Learning for Energy Systems Optimization," Energies, MDPI, vol. 15(11), pages 1-8, June.
    2. Tostado-Véliz, Marcos & Rezaee Jordehi, Ahmad & Amir Mansouri, Seyed & Jurado, Francisco, 2022. "Day-ahead scheduling of 100% isolated communities under uncertainties through a novel stochastic-robust model," Applied Energy, Elsevier, vol. 328(C).
    3. Meng, He & Jia, Hongjie & Xu, Tao & Wei, Wei & Wu, Yuhan & Liang, Lemeng & Cai, Shuqi & Liu, Zuozheng & Wang, Rujing & Li, Mengchao, 2022. "Optimal configuration of cooperative stationary and mobile energy storage considering ambient temperature: A case for Winter Olympic Game," Applied Energy, Elsevier, vol. 325(C).
    4. Jaemin Park & Haesung Jo & Insu Kim, 2021. "The Selection of the Most Cost-Efficient Distributed Generation Type for a Combined Cooling Heat and Power System Used for Metropolitan Residential Customers," Energies, MDPI, vol. 14(18), pages 1-25, September.
    5. Megersa Tesfaye Boke & Semu Ayalew Moges & Zeleke Agide Dejen, 2022. "Optimizing renewable-based energy supply options for power generation in Ethiopia," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-15, January.
    6. Abedrabboh, Khaled & Al-Fagih, Luluwah, 2023. "Applications of mechanism design in market-based demand-side management: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 171(C).
    7. Rafaela Nascimento & Felipe Ramos & Aline Pinheiro & Washington de Araujo Silva Junior & Ayrlw M. C. Arcanjo & Roberto F. Dias Filho & Mohamed A. Mohamed & Manoel H. N. Marinho, 2022. "Case Study of Backup Application with Energy Storage in Microgrids," Energies, MDPI, vol. 15(24), pages 1-12, December.
    8. Lin, Yu-Hsiu & Shen, Ting-Yu, 2023. "Novel cell screening and prognosing based on neurocomputing-based multiday-ahead time-series forecasting for predictive maintenance of battery modules in frequency regulation-energy storage systems," Applied Energy, Elsevier, vol. 351(C).
    9. Shafiekhani, Morteza & Qadrdan, Meysam, 2025. "Addressing electricity transmission network congestions using battery energy storage systems – a case study of great Britain," Applied Energy, Elsevier, vol. 384(C).
    10. Wang, Chong & Ju, Ping & Wu, Feng & Pan, Xueping & Wang, Zhaoyu, 2022. "A systematic review on power system resilience from the perspective of generation, network, and load," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    11. Azhdari, Armaghan & Ardakan, Mostafa Abouei & Najafi, Mojtaba, 2023. "An approach for reliability optimization of a multi-state centralized network," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    12. Moradi-Sepahvand, Mojtaba & Amraee, Turaj, 2021. "Integrated expansion planning of electric energy generation, transmission, and storage for handling high shares of wind and solar power generation," Applied Energy, Elsevier, vol. 298(C).
    13. Feras Alasali & Mohammad Salameh & Ali Semrin & Khaled Nusair & Naser El-Naily & William Holderbaum, 2022. "Optimal Controllers and Configurations of 100% PV and Energy Storage Systems for a Microgrid: The Case Study of a Small Town in Jordan," Sustainability, MDPI, vol. 14(13), pages 1-20, July.
    14. Reguieg, Zakaria & Bouyakoub, Ismail & Mehedi, Fayçal, 2025. "Integrated optimization of power quality and energy management in a photovoltaic-battery microgrid," Renewable Energy, Elsevier, vol. 241(C).
    15. 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.
    16. Liu, Jiejie & Li, Yao & Ma, Yanan & Qin, Ruomu & Meng, Xianyang & Wu, Jiangtao, 2023. "Two-layer multiple scenario optimization framework for integrated energy system based on optimal energy contribution ratio strategy," Energy, Elsevier, vol. 285(C).
    17. Assareh, Ehsanolah & Hoseinzadeh, Siamak & Agarwal, Saurabh & keykhah, Mohammad & Agarwal, Neha & Heydari, Azim & Astiaso Garcia, Davide, 2025. "Assessment of a wind energy installation for powering a residential building in Rome, Italy: Incorporating wind turbines, compressed air energy storage, and a compression chiller based on a machine le," Energy, Elsevier, vol. 320(C).
    18. Yushi Wang & Beining Hu & Xianhai Meng & Runjin Xiao, 2024. "A Comprehensive Review on Technologies for Achieving Zero-Energy Buildings," Sustainability, MDPI, vol. 16(24), pages 1-26, December.
    19. Zhang, Tairan & Sobhani, Behrouz, 2023. "Optimal economic programming of an energy hub in the power system while taking into account the uncertainty of renewable resources, risk-taking and electric vehicles using a developed routing method," Energy, Elsevier, vol. 271(C).
    20. Zhang, Beiyuan & Wang, Jianru & Li, Zhicheng & Gao, Tongtong & Zhang, Weijun & Xu, Chao & Ju, Xing, 2025. "Optimal configuration scheme for multi-hybrid energy storage system containing ground source heat pumps and hydrogen-doped gas turbine," Energy, Elsevier, vol. 321(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:320:y:2025:i:c:s0360544225010138. 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.