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Optimal Allocation and Sizing of Distributed Generation Using Interval Power Flow

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    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.
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