IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i5p2444-d1087394.html
   My bibliography  Save this article

Optimal Planning of Solar Photovoltaic (PV) and Wind-Based DGs for Achieving Techno-Economic Objectives across Various Load Models

Author

Listed:
  • Habib Ur Rehman

    (US-Pakistan Center for Advanced Studies in Energy (USPCAS-E), National University of Sciences and Technology (NUST), H-12, Islamabad 44000, Pakistan)

  • Arif Hussain

    (Department of Electrical and Computer Engineering, Sungkyunkwan University, Seoul 16419, Republic of Korea)

  • Waseem Haider

    (Department of Electrical and Computer Engineering, Sungkyunkwan University, Seoul 16419, Republic of Korea)

  • Sayyed Ahmad Ali

    (US-Pakistan Center for Advanced Studies in Energy (USPCAS-E), National University of Sciences and Technology (NUST), H-12, Islamabad 44000, Pakistan)

  • Syed Ali Abbas Kazmi

    (US-Pakistan Center for Advanced Studies in Energy (USPCAS-E), National University of Sciences and Technology (NUST), H-12, Islamabad 44000, Pakistan)

  • Muhammad Huzaifa

    (US-Pakistan Center for Advanced Studies in Energy (USPCAS-E), National University of Sciences and Technology (NUST), H-12, Islamabad 44000, Pakistan)

Abstract

Over the last few decades, distributed generation (DG) has become the most viable option in distribution systems (DSs) to mitigate the power losses caused by the substantial increase in electricity demand and to improve the voltage profile by enhancing power system reliability. In this study, two metaheuristic algorithms, artificial gorilla troops optimization (GTO) and Tasmanian devil optimization (TDO), are presented to examine the utilization of DGs, as well as the optimal placement and sizing in DSs, with a special emphasis on maximizing the voltage stability index and minimizing the total operating cost index and active power loss, along with the minimizing of voltage deviation. The robustness of the algorithms is examined on the IEEE 33-bus and IEEE 69-bus radial distribution networks (RDNs) for PV- and wind-based DGs. The obtained results are compared with the existing literature to validate the effectiveness of the algorithms. The reduction in active power loss is 93.15% and 96.87% of the initial value for the 33-bus and 69-bus RDNs, respectively, while the other parameters, i.e., operating cost index, voltage deviation, and voltage stability index, are also improved. This validates the efficiency of the algorithms. The proposed study is also carried out by considering different voltage-dependent load models, including industrial, residential, and commercial types.

Suggested Citation

  • Habib Ur Rehman & Arif Hussain & Waseem Haider & Sayyed Ahmad Ali & Syed Ali Abbas Kazmi & Muhammad Huzaifa, 2023. "Optimal Planning of Solar Photovoltaic (PV) and Wind-Based DGs for Achieving Techno-Economic Objectives across Various Load Models," Energies, MDPI, vol. 16(5), pages 1-38, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2444-:d:1087394
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/5/2444/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/5/2444/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Arun Onlam & Daranpob Yodphet & Rongrit Chatthaworn & Chayada Surawanitkun & Apirat Siritaratiwat & Pirat Khunkitti, 2019. "Power Loss Minimization and Voltage Stability Improvement in Electrical Distribution System via Network Reconfiguration and Distributed Generation Placement Using Novel Adaptive Shuffled Frogs Leaping," Energies, MDPI, vol. 12(3), pages 1-12, February.
    2. 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.
    3. Mahmoud G. Hemeida & Salem Alkhalaf & Al-Attar A. Mohamed & Abdalla Ahmed Ibrahim & Tomonobu Senjyu, 2020. "Distributed Generators Optimization Based on Multi-Objective Functions Using Manta Rays Foraging Optimization Algorithm (MRFO)," Energies, MDPI, vol. 13(15), pages 1-37, July.
    4. Minh Quan Duong & Thai Dinh Pham & Thang Trung Nguyen & Anh Tuan Doan & Hai Van Tran, 2019. "Determination of Optimal Location and Sizing of Solar Photovoltaic Distribution Generation Units in Radial Distribution Systems," Energies, MDPI, vol. 12(1), pages 1-24, January.
    5. Abinands Ramshanker & Jacob Raglend Isaac & Belwin Edward Jeyeraj & Jose Swaminathan & Ravi Kuppan, 2022. "Optimal DG Placement in Power Systems Using a Modified Flower Pollination Algorithm," Energies, MDPI, vol. 15(22), pages 1-17, November.
    6. Mohsin Shahzad & Waseem Akram & Muhammad Arif & Uzair Khan & Barkat Ullah, 2021. "Optimal Siting and Sizing of Distributed Generators by Strawberry Plant Propagation Algorithm," Energies, MDPI, vol. 14(6), pages 1-13, March.
    7. Thangaraj Yuvaraj & Kaliaperumal Rukmani Devabalaji & Natarajan Prabaharan & Hassan Haes Alhelou & Asokkumar Manju & Poushali Pal & Pierluigi Siano, 2021. "Optimal Integration of Capacitor and Distributed Generation in Distribution System Considering Load Variation Using Bat Optimization Algorithm," Energies, MDPI, vol. 14(12), pages 1-24, June.
    8. Oludamilare Bode Adewuyi & Ayooluwa Peter Adeagbo & Isaiah Gbadegesin Adebayo & Harun Or Rashid Howlader & Yanxia Sun, 2021. "Modified Analytical Approach for PV-DGs Integration into a Radial Distribution Network Considering Loss Sensitivity and Voltage Stability," Energies, MDPI, vol. 14(22), pages 1-20, November.
    9. 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.
    10. Ehsan, Ali & Yang, Qiang, 2018. "Optimal integration and planning of renewable distributed generation in the power distribution networks: A review of analytical techniques," Applied Energy, Elsevier, vol. 210(C), pages 44-59.
    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. Faris E. Alfaris & Faris Almutairi, 2024. "Performance Assessment User Interface to Enhance the Utilization of Grid-Connected Residential PV Systems," Sustainability, MDPI, vol. 16(5), pages 1-26, February.

    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. Oludamilare Bode Adewuyi & Ayooluwa Peter Adeagbo & Isaiah Gbadegesin Adebayo & Harun Or Rashid Howlader & Yanxia Sun, 2021. "Modified Analytical Approach for PV-DGs Integration into a Radial Distribution Network Considering Loss Sensitivity and Voltage Stability," Energies, MDPI, vol. 14(22), pages 1-20, November.
    2. 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.
    3. Nasreddine Belbachir & Mohamed Zellagui & Samir Settoul & Claude Ziad El-Bayeh & Ragab A. El-Sehiemy, 2023. "Multi Dimension-Based Optimal Allocation of Uncertain Renewable Distributed Generation Outputs with Seasonal Source-Load Power Uncertainties in Electrical Distribution Network Using Marine Predator Al," Energies, MDPI, vol. 16(4), pages 1-24, February.
    4. Sachin Kumar & Kumari Sarita & Akanksha Singh S Vardhan & Rajvikram Madurai Elavarasan & R. K. Saket & Narottam Das, 2020. "Reliability Assessment of Wind-Solar PV Integrated Distribution System Using Electrical Loss Minimization Technique," Energies, MDPI, vol. 13(21), pages 1-30, October.
    5. Stavros Lazarou & Vasiliki Vita & Lambros Ekonomou, 2018. "Protection Schemes of Meshed Distribution Networks for Smart Grids and Electric Vehicles," Energies, MDPI, vol. 11(11), pages 1-17, November.
    6. Mahmoud Aref & Vladislav Oboskalov & Adel El-Shahat & Almoataz Y. Abdelaziz, 2023. "Modified Analytical Technique for Multi-Objective Optimal Placement of High-Level Renewable Energy Penetration Connected to Egyptian Power System," Mathematics, MDPI, vol. 11(4), pages 1-31, February.
    7. Jingjing Tu & Yonghai Xu & Zhongdong Yin, 2018. "Data-Driven Kernel Extreme Learning Machine Method for the Location and Capacity Planning of Distributed Generation," Energies, MDPI, vol. 12(1), pages 1-21, December.
    8. 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.
    9. Mohammadhafez Bazrafshan & Likhitha Yalamanchili & Nikolaos Gatsis & Juan Gomez, 2019. "Stochastic Planning of Distributed PV Generation," Energies, MDPI, vol. 12(3), pages 1-20, January.
    10. Kadir Doğanşahin & Bedri Kekezoğlu & Recep Yumurtacı & Ozan Erdinç & João P. S. Catalão, 2018. "Maximum Permissible Integration Capacity of Renewable DG Units Based on System Loads," Energies, MDPI, vol. 11(1), pages 1-16, January.
    11. Mouwafi, Mohamed T. & El-Sehiemy, Ragab A. & El-Ela, Adel A. Abou, 2022. "A two-stage method for optimal placement of distributed generation units and capacitors in distribution systems," Applied Energy, Elsevier, vol. 307(C).
    12. Weifeng Xu & Bing Yu & Qing Song & Liguo Weng & Man Luo & Fan Zhang, 2022. "Economic and Low-Carbon-Oriented Distribution Network Planning Considering the Uncertainties of Photovoltaic Generation and Load Demand to Achieve Their Reliability," Energies, MDPI, vol. 15(24), pages 1-15, December.
    13. Jingjing Tu & Zhongdong Yin & Yonghai Xu, 2018. "Study on the Evaluation Index System and Evaluation Method of Voltage Stability of Distribution Network with High DG Penetration," Energies, MDPI, vol. 11(1), pages 1-15, January.
    14. Rong-Ceng Leou, 2022. "A Substation-Based Optimal Photovoltaic Generation System Placement Considering Multiple Evaluation Indices," Energies, MDPI, vol. 15(15), pages 1-18, August.
    15. Fan Li & Dong Liu & Boyu Qin & Ke Sun & Dan Wang & Hanqing Liang & Cheng Zhang & Taikun Tao, 2022. "Multi-Objective Energy Optimal Scheduling of Multiple Pulsed Loads in Isolated Power Systems," Sustainability, MDPI, vol. 14(23), pages 1-15, November.
    16. Mohseni, Soheil & Brent, Alan C. & Burmester, Daniel, 2020. "A comparison of metaheuristics for the optimal capacity planning of an isolated, battery-less, hydrogen-based micro-grid," Applied Energy, Elsevier, vol. 259(C).
    17. Gonçalves Rigueira Pinheiro Castro, Pedro Henrique & Filho, Delly Oliveira & Rosa, André Pereira & Navas Gracia, Luis Manuel & Almeida Silva, Thais Cristina, 2024. "Comparison of externalities of biogas and photovoltaic solar energy for energy planning," Energy Policy, Elsevier, vol. 188(C).
    18. Xueping Li & Gerald Jones, 2022. "Optimal Sizing, Location, and Assignment of Photovoltaic Distributed Generators with an Energy Storage System for Islanded Microgrids," Energies, MDPI, vol. 15(18), pages 1-22, September.
    19. Samson Oladayo Ayanlade & Funso Kehinde Ariyo & Abdulrasaq Jimoh & Kayode Timothy Akindeji & Adeleye Oluwaseye Adetunji & Emmanuel Idowu Ogunwole & Dolapo Eniola Owolabi, 2023. "Optimal Allocation of Photovoltaic Distributed Generations in Radial Distribution Networks," Sustainability, MDPI, vol. 15(18), pages 1-26, September.
    20. Gubbala Venkata Naga Lakshmi & Askani Jaya Laxmi & Venkataramana Veeramsetty & Surender Reddy Salkuti, 2022. "Optimal Placement of Distributed Generation Based on Power Quality Improvement Using Self-Adaptive Lévy Flight Jaya Algorithm," Clean Technol., MDPI, vol. 4(4), pages 1-13, November.

    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:5:p:2444-:d:1087394. 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.