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Minimizing Power Losses for Distributed Generation (DG) Placements by Considering Voltage Profiles on Distribution Lines for Different Loads Using Genetic Algorithm Methods

Author

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  • Ramdhan Halid Siregar

    (Doctoral School of Engineering Science, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
    Department of Electrical and Computer Engineering, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia)

  • Yuwaldi Away

    (Doctoral School of Engineering Science, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
    Department of Electrical and Computer Engineering, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia)

  • Tarmizi

    (Doctoral School of Engineering Science, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
    Department of Electrical and Computer Engineering, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia)

  • Akhyar

    (Doctoral School of Engineering Science, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
    Department of Mechanical Engineering, Universitas Syiah Kuala, Jl. Syech Abdurrauf No.7 Darussalam, Banda Aceh 23111, Indonesia)

Abstract

The need for electrical energy is increasing in line with the increase in population and increasing progress in welfare. On the other hand, the availability of fossil fuels as the main fuel in generating electricity is dwindling; so, there is a need for policies that require the use of environmentally friendly renewable energy. The utilization of renewable energy does not necessarily apply freely due to several constraints. One effort is a generator or distributed generation (DG) which is placed in the distribution line close to the load. The utilization of DG must go through planning, especially the large capacity and position on the bus and on the feeder, which will result in small network losses and a voltage profile that meets tolerance limits. Thus, the purpose of this study is to optimize to obtain the capacity and location of the DG calculated by considering the variation in the load through the genetic algorithm method. As a result, the optimal DG position for normal load is obtained on bus 18, bus 20, and bus 32 with capacities of 190 kW, 463 kW, and 370 kW, respectively. The losses obtained decreased from 54.6733 kW to 9.9447 kW, and the voltage profile was maintained within the specified limits. Optimization was carried out for decreasing and increasing loads in percent. The result is that losses can be minimized, and the voltage profile remains within the required limits. The lower the load, the more stable the voltage and the smaller the losses; meanwhile, the larger the load, the more fluctuating the voltage is, but still within the limits specified in the optimization.

Suggested Citation

  • Ramdhan Halid Siregar & Yuwaldi Away & Tarmizi & Akhyar, 2023. "Minimizing Power Losses for Distributed Generation (DG) Placements by Considering Voltage Profiles on Distribution Lines for Different Loads Using Genetic Algorithm Methods," Energies, MDPI, vol. 16(14), pages 1-25, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:14:p:5388-:d:1194419
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    References listed on IDEAS

    as
    1. Ye Jin & Yuehong Sun & Hongjiao Ma, 2018. "A Developed Artificial Bee Colony Algorithm Based on Cloud Model," Mathematics, MDPI, vol. 6(4), pages 1-18, April.
    2. George Halkos & Eleni-Christina Gkampoura, 2023. "Assessing Fossil Fuels and Renewables’ Impact on Energy Poverty Conditions in Europe," Energies, MDPI, vol. 16(1), pages 1-16, January.
    3. Ali Selim & Salah Kamel & Amal A. Mohamed & Ehab E. Elattar, 2021. "Optimal Allocation of Multiple Types of Distributed Generations in Radial Distribution Systems Using a Hybrid Technique," Sustainability, MDPI, vol. 13(12), pages 1-31, June.
    4. Zhang, Shenxi & Cheng, Haozhong & Li, Ke & Tai, Nengling & Wang, Dan & Li, Furong, 2018. "Multi-objective distributed generation planning in distribution network considering correlations among uncertainties," Applied Energy, Elsevier, vol. 226(C), pages 743-755.
    5. Jerry L. Holechek & Hatim M. E. Geli & Mohammed N. Sawalhah & Raul Valdez, 2022. "A Global Assessment: Can Renewable Energy Replace Fossil Fuels by 2050?," Sustainability, MDPI, vol. 14(8), pages 1-22, April.
    6. Razavi, Seyed-Ehsan & Rahimi, Ehsan & Javadi, Mohammad Sadegh & Nezhad, Ali Esmaeel & Lotfi, Mohamed & Shafie-khah, Miadreza & Catalão, João P.S., 2019. "Impact of distributed generation on protection and voltage regulation of distribution systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 157-167.
    7. Vasiliki Vita, 2017. "Development of a Decision-Making Algorithm for the Optimum Size and Placement of Distributed Generation Units in Distribution Networks," Energies, MDPI, vol. 10(9), pages 1-13, September.
    8. Luigi Schirone & Filippo Pellitteri, 2017. "Energy Policies and Sustainable Management of Energy Sources," Sustainability, MDPI, vol. 9(12), pages 1-13, December.
    9. Nayeripour, Majid & Mahboubi-Moghaddam, Esmaeil & Aghaei, Jamshid & Azizi-Vahed, Ali, 2013. "Multi-objective placement and sizing of DGs in distribution networks ensuring transient stability using hybrid evolutionary algorithm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 25(C), pages 759-767.
    10. Asad Abbas & Saeed Mian Qaisar & Asad Waqar & Nasim Ullah & Ahmad Aziz Al Ahmadi, 2022. "Min-Max Regret-Based Approach for Sizing and Placement of DGs in Distribution System under a 24 h Load Horizon," Energies, MDPI, vol. 15(10), pages 1-32, May.
    11. A. Rashedi & Taslima Khanam & Mirjam Jonkman, 2020. "On Reduced Consumption of Fossil Fuels in 2020 and Its Consequences in Global Environment and Exergy Demand," Energies, MDPI, vol. 13(22), pages 1-14, November.
    12. 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.
    13. Eshan Karunarathne & Jagadeesh Pasupuleti & Janaka Ekanayake & Dilini Almeida, 2020. "Optimal Placement and Sizing of DGs in Distribution Networks Using MLPSO Algorithm," Energies, MDPI, vol. 13(23), pages 1-25, November.
    14. Esmaili, Masoud & Firozjaee, Esmail Chaktan & Shayanfar, Heidar Ali, 2014. "Optimal placement of distributed generations considering voltage stability and power losses with observing voltage-related constraints," Applied Energy, Elsevier, vol. 113(C), pages 1252-1260.
    15. Serhat Yarat & Sibel Senan & Zeynep Orman, 2021. "A Comparative Study on PSO with Other Metaheuristic Methods," International Series in Operations Research & Management Science, in: Burcu Adıgüzel Mercangöz (ed.), Applying Particle Swarm Optimization, edition 1, chapter 0, pages 49-72, Springer.
    16. Gitizadeh, Mohsen & Vahed, Ali Azizi & Aghaei, Jamshid, 2013. "Multistage distribution system expansion planning considering distributed generation using hybrid evolutionary algorithms," Applied Energy, Elsevier, vol. 101(C), pages 655-666.
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