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Integration of Renewable Based Distributed Generation for Distribution Network Expansion Planning

Author

Listed:
  • Mulusew Ayalew

    (Department of Electrical and Computer Engineering, Hawassa University, Hawassa 1530, Ethiopia)

  • Baseem Khan

    (Department of Electrical and Computer Engineering, Hawassa University, Hawassa 1530, Ethiopia)

  • Issaias Giday

    (Department of Electrical and Computer Engineering, Hawassa University, Hawassa 1530, Ethiopia)

  • Om Prakash Mahela

    (Power System Planning Division, Rajasthan Rajya Vidyut Prasaran Nigam Ltd., Jaipur 302005, India)

  • Mahdi Khosravy

    (Cross Labs, Cross-Compass Ltd., Tokyo 104-0045, Japan)

  • Neeraj Gupta

    (Computer Science and Engineering Department, Oakland University, Rochester, NY 48309, USA)

  • Tomonobu Senjyu

    (Faculty of Engineering, University of the Ryukyus, Okinawa 903-0213, Japan)

Abstract

Electrical energy is critical to a country’s socioeconomic progress. Distribution system expansion planning addresses the services that must be installed for the distribution networks to meet the expected load need, while also meeting different operational and technical limitations. The incorporation of distributed generation sources (DGs) alters the operating characteristics of modern power systems, resulting in major economic and technical benefits, such as simplified distribution network expansion planning, lower power losses, and improved voltage profile. Thus, in this study, an analytical method is used to design the expansion planning of the Addis North distribution network considering the integration of optimal sizes of distributed generations for the projected demand growths. To evaluate the capability of the existing Addis North distribution network and its capability to supply reliable power considering future expansion, the load demand forecast for the years 2020–2030 is done using the least square method. The performance evaluation of the existing and the upgraded network considering the existing and forecasted load demand for the years 2030 is done using ETAP software. Accordingly, the results revealed that the existing networks cannot meet the existing load demand of the town, with major problems of increased power loss and a reduced voltage profile. To mitigate this problem, the Addis North feeder-1 distribution network is upgraded and for each study case, the balanced and positive sequence load flow analysis was executed and the maximum total real and reactive power losses were found at bus 29. The result shows that the upgraded network of bus 29 was the optimal location of DG and its size was 9.93 MW. After the optimal size of DG was placed at this bus, the real and reactive power losses of the upgraded networks were 0.2939 MW and 0.219 MVAr, respectively. At bus 29 the maximum power losses reduction and voltage profile improvements were found. The active and reactive power losses were minimized by 21.285% and 19.633% respectively and the voltage profiles were improved by 8.78%. Thus, in the predicted year 2030, DG power sources could cover 61.12% of the feeder-1 power requirements.

Suggested Citation

  • Mulusew Ayalew & Baseem Khan & Issaias Giday & Om Prakash Mahela & Mahdi Khosravy & Neeraj Gupta & Tomonobu Senjyu, 2022. "Integration of Renewable Based Distributed Generation for Distribution Network Expansion Planning," Energies, MDPI, vol. 15(4), pages 1-17, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1378-:d:749014
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    References listed on IDEAS

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    1. Amit Kumer Podder & Sayemul Islam & Nallapaneni Manoj Kumar & Aneesh A. Chand & Pulivarthi Nageswara Rao & Kushal A. Prasad & T. Logeswaran & Kabir A. Mamun, 2020. "Systematic Categorization of Optimization Strategies for Virtual Power Plants," Energies, MDPI, vol. 13(23), pages 1-46, November.
    2. Rui Li & Wei Wang & Zhe Chen & Jiuchun Jiang & Weige Zhang, 2017. "A Review of Optimal Planning Active Distribution System: Models, Methods, and Future Researches," Energies, MDPI, vol. 10(11), pages 1-27, October.
    3. Ahmadigorji, Masoud & Amjady, Nima, 2016. "A multiyear DG-incorporated framework for expansion planning of distribution networks using binary chaotic shark smell optimization algorithm," Energy, Elsevier, vol. 102(C), pages 199-215.
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    Citations

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    Cited by:

    1. Joseph P. Varghese & Kumaravel Sundaramoorthy & Ashok Sankaran, 2023. "Development and Validation of a Load Flow Based Scheme for Optimum Placing and Quantifying of Distributed Generation for Alleviation of Congestion in Interconnected Power Systems," Energies, MDPI, vol. 16(6), pages 1-24, March.
    2. Tomonobu Senjyu & Mahdi Khosravy, 2022. "Power System Planning and Quality Control," Energies, MDPI, vol. 15(14), pages 1-2, July.
    3. Soheil Younesi & Bahman Ahmadi & Oguzhan Ceylan & Aydogan Ozdemir, 2022. "Optimum Parallel Processing Schemes to Improve the Computation Speed for Renewable Energy Allocation and Sizing Problems," Energies, MDPI, vol. 15(24), pages 1-18, December.
    4. Adolfo Blengini Neto & Maria Beatriz Barbosa & Lia Moreira Mota & Marina Lavorato & Marcius F. H. de Carvalho, 2022. "Optimal Power Flow Technique for Distribution System Considering Distributed Energy Resources (DER)," Energies, MDPI, vol. 15(22), pages 1-16, November.
    5. Fathy, Ahmed, 2022. "A novel artificial hummingbird algorithm for integrating renewable based biomass distributed generators in radial distribution systems," Applied Energy, Elsevier, vol. 323(C).

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