IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i6p5171-d1097379.html
   My bibliography  Save this article

Optimal Allocation and Sizing of Distributed Generation Using Interval Power Flow

Author

Listed:
  • Wallisson C. Nogueira

    (Electrical, Mechanical and Computer Engineering School, Federal University of Goiás, Av. Universitária No. 1488, Goiânia 74605-010, Brazil)

  • Lina P. Garcés Negrete

    (Electrical, Mechanical and Computer Engineering School, Federal University of Goiás, Av. Universitária No. 1488, Goiânia 74605-010, Brazil)

  • Jesús M. López-Lezama

    (Grupo en Manejo Eficiente de la Energía (GIMEL), Departamento de Ingeniería Eléctrica, Universidad de Antioquia (UdeA), Calle 70 No. 52-21, Medellin 050010, Colombia)

Abstract

Modern distribution systems and microgrids must deal with high levels of uncertainty in their planning and operation. These uncertainties are mainly due to variations in loads and distributed generation (DG) introduced by new technologies. This scenario brings new challenges to planners and system operators that need new tools to perform more assertive analyses of the grid state. This paper presents an optimization methodology capable of considering uncertainties in the optimal allocation and sizing problem of DG in distribution networks. The proposed methodology uses an interval power flow (IPF) that adds uncertainties to the combinatorial optimization problem in charge of sizing and allocating DG units in the network. Two metaheuristics were implemented for comparative purposes, namely, symbiotic organism search (SOS) and particle swarm optimization (PSO). The proposed methodology was implemented in Python ® using as benchmark distribution systems the IEEE 33-bus and IEEE 69-bus test distribution networks. The objective function consists of minimizing technical losses and regulating network voltage levels. The results obtained from the proposed IPF on the tested networks are compatible with those obtained by the PPF, thus evidencing the robustness and applicability of the proposed method. For the solution of the optimization problem, the SOS metaheuristic proved to be robust, since it was able to find the best solutions (lowest losses) while keeping voltage levels within the predetermined range. On the other hand, the PSO metaheuristic showed less satisfactory results, since for all test systems, the solutions found were of lower quality than the ones found by the SOS.

Suggested Citation

  • Wallisson C. Nogueira & Lina P. Garcés Negrete & Jesús M. López-Lezama, 2023. "Optimal Allocation and Sizing of Distributed Generation Using Interval Power Flow," Sustainability, MDPI, vol. 15(6), pages 1-24, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:5171-:d:1097379
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/6/5171/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/6/5171/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Weijie Cheng & Renli Cheng & Jun Shi & Cong Zhang & Gaoxing Sun & Dong Hua, 2018. "Interval Power Flow Analysis Considering Interval Output of Wind Farms through Affine Arithmetic and Optimizing-Scenarios Method," Energies, MDPI, vol. 11(11), pages 1-23, November.
    2. Andrés Felipe Pérez Posada & Juan G. Villegas & Jesús M. López-Lezama, 2017. "A Scatter Search Heuristic for the Optimal Location, Sizing and Contract Pricing of Distributed Generation in Electric Distribution Systems," Energies, MDPI, vol. 10(10), pages 1-16, September.
    3. Wallisson C. Nogueira & Lina Paola Garcés Negrete & Jesús M. López-Lezama, 2021. "Interval Load Flow for Uncertainty Consideration in Power Systems Analysis," Energies, MDPI, vol. 14(3), pages 1-14, January.
    4. Lizi Luo & Wei Gu & Yonghui Wang & Chunxi Chen, 2017. "An Affine Arithmetic-Based Power Flow Algorithm Considering the Regional Control of Unscheduled Power Fluctuation," Energies, MDPI, vol. 10(11), pages 1-6, November.
    5. 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.
    6. Luis A. Gallego Pareja & Jesús M. López-Lezama & Oscar Gómez Carmona, 2022. "A Mixed-Integer Linear Programming Model for the Simultaneous Optimal Distribution Network Reconfiguration and Optimal Placement of Distributed Generation," Energies, MDPI, vol. 15(9), pages 1-26, April.
    7. Arul Rajagopalan & Dhivya Swaminathan & Meshal Alharbi & Sudhakar Sengan & Oscar Danilo Montoya & Walid El-Shafai & Mostafa M. Fouda & Moustafa H. Aly, 2022. "Modernized Planning of Smart Grid Based on Distributed Power Generations and Energy Storage Systems Using Soft Computing Methods," Energies, MDPI, vol. 15(23), pages 1-18, November.
    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. Mahmoud A. Elsadd & Ahmed F. Zobaa & Heba A. Khattab & Ahmed M. Abd El Aziz & Tamer Fetouh, 2023. "Communicationless Overcurrent Relays Coordination for Active Distribution Network Considering Fault Repairing Periods," Energies, MDPI, vol. 16(23), pages 1-32, November.
    2. Wenlei Bai & Wen Zhang & Richard Allmendinger & Innocent Enyekwe & Kwang Y. Lee, 2024. "A Comparative Study of Optimal PV Allocation in a Distribution Network Using Evolutionary Algorithms," Energies, MDPI, vol. 17(2), pages 1-19, January.

    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. Wallisson C. Nogueira & Lina Paola Garcés Negrete & Jesús M. López-Lezama, 2021. "Interval Load Flow for Uncertainty Consideration in Power Systems Analysis," Energies, MDPI, vol. 14(3), pages 1-14, January.
    2. Matheus Diniz Gonçalves-Leite & Edgar Manuel Carreño-Franco & Jesús M. López-Lezama, 2023. "Impact of Distributed Generation on the Effectiveness of Electric Distribution System Reconfiguration," Energies, MDPI, vol. 16(17), pages 1-20, August.
    3. Mahmoud M. Badr & Mohamed I. Ibrahem & Hisham A. Kholidy & Mostafa M. Fouda & Muhammad Ismail, 2023. "Review of the Data-Driven Methods for Electricity Fraud Detection in Smart Metering Systems," Energies, MDPI, vol. 16(6), pages 1-18, March.
    4. Paliwal, Priyanka & Patidar, N.P. & Nema, R.K., 2014. "Planning of grid integrated distributed generators: A review of technology, objectives and techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 557-570.
    5. Rezaee Jordehi, Ahmad, 2016. "Allocation of distributed generation units in electric power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 893-905.
    6. 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.
    7. Pavlos S. Georgilakis, 2020. "Review of Computational Intelligence Methods for Local Energy Markets at the Power Distribution Level to Facilitate the Integration of Distributed Energy Resources: State-of-the-art and Future Researc," Energies, MDPI, vol. 13(1), pages 1-37, January.
    8. Ziqi Liu & Tingting Su & Zhiying Quan & Quanli Wu & Yu Wang, 2023. "Review on the Optimal Configuration of Distributed Energy Storage," Energies, MDPI, vol. 16(14), pages 1-17, July.
    9. Luis A. Gallego Pareja & Jesús M. López-Lezama & Oscar Gómez Carmona, 2022. "A Mixed-Integer Linear Programming Model for the Simultaneous Optimal Distribution Network Reconfiguration and Optimal Placement of Distributed Generation," Energies, MDPI, vol. 15(9), pages 1-26, April.
    10. 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.
    11. Muttaqi, K.M. & Le, An D.T. & Aghaei, J. & Mahboubi-Moghaddam, E. & Negnevitsky, M. & Ledwich, G., 2016. "Optimizing distributed generation parameters through economic feasibility assessment," Applied Energy, Elsevier, vol. 165(C), pages 893-903.
    12. Dhivya Swaminathan & Arul Rajagopalan & Oscar Danilo Montoya & Savitha Arul & Luis Fernando Grisales-Noreña, 2023. "Distribution Network Reconfiguration Based on Hybrid Golden Flower Algorithm for Smart Cities Evolution," Energies, MDPI, vol. 16(5), pages 1-24, March.
    13. Karabacak, Kerim & Cetin, Numan, 2014. "Artificial neural networks for controlling wind–PV power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 804-827.
    14. Abdullah Shaheen & Ragab El-Sehiemy & Salah Kamel & Ali Selim, 2022. "Optimal Operational Reliability and Reconfiguration of Electrical Distribution Network Based on Jellyfish Search Algorithm," Energies, MDPI, vol. 15(19), pages 1-14, September.
    15. José R. Ortiz-Castrillón & Sergio D. Saldarriaga-Zuluaga & Nicolás Muñoz-Galeano & Jesús M. López-Lezama & Santiago Benavides-Córdoba & Juan B. Cano-Quintero, 2023. "Optimal Sliding-Mode Control of Semi-Bridgeless Boost Converters Considering Power Factor Corrections," Energies, MDPI, vol. 16(17), pages 1-24, August.
    16. Rasheed Abdulkader & Hayder M. A. Ghanimi & Pankaj Dadheech & Meshal Alharbi & Walid El-Shafai & Mostafa M. Fouda & Moustafa H. Aly & Dhivya Swaminathan & Sudhakar Sengan, 2023. "Soft Computing in Smart Grid with Decentralized Generation and Renewable Energy Storage System Planning," Energies, MDPI, vol. 16(6), pages 1-24, March.
    17. Francisco G. Montoya & Raúl Baños & Alfredo Alcayde & Francisco Manzano-Agugliaro, 2019. "Optimization Methods Applied to Power Systems," Energies, MDPI, vol. 12(12), pages 1-8, June.
    18. Sarimuthu, Charles R. & Ramachandaramurthy, Vigna K. & Agileswari, K.R. & Mokhlis, Hazlie, 2016. "A review on voltage control methods using on-load tap changer transformers for networks with renewable energy sources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 1154-1161.
    19. Jordehi, A. Rezaee, 2015. "Optimisation of electric distribution systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1088-1100.
    20. Luis A. Gallego Pareja & Jesús M. López-Lezama & Oscar Gómez Carmona, 2023. "A MILP Model for Optimal Conductor Selection and Capacitor Banks Placement in Primary Distribution Systems," Energies, MDPI, vol. 16(11), pages 1-21, May.

    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:jsusta:v:15:y:2023:i:6:p:5171-:d:1097379. 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.