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Optimal Conductor Size Selection in Distribution Networks with High Penetration of Distributed Generation Using Adaptive Genetic Algorithm

Author

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  • Zhenghui Zhao

    (School of Electrical & Electronic Engineering, University of Manchester, Sackville Street, Manchester M13 9PL, UK)

  • Joseph Mutale

    (School of Electrical & Electronic Engineering, University of Manchester, Sackville Street, Manchester M13 9PL, UK)

Abstract

The widespread deployment of distributed generation (DG) has significantly impacted the planning and operation of current distribution networks. The environmental benefits and the reduced installation cost have been the primary drivers for the investment in large-scale wind farms and photovoltaics (PVs). However, the distribution network operators (DNOs) face the challenge of conductor upgrade and selection problems due to the increasing capacity of DG. In this paper, a hybrid optimization approach is introduced to solve the optimal conductor size selection (CSS) problem in the distribution network with high penetration of DGs. An adaptive genetic algorithm (AGA) is employed as the primary optimization strategy to find the optimal conductor sizes for distribution networks. The aim of the proposed approach is to minimize the sum of life-cycle cost (LCC) of the selected conductor and the total energy procurement cost during the expected operation periods. Alternating current optimal power flow (AC-OPF) analysis is applied as the secondary optimization strategy to capture the economic dispatch (ED) and return the results to the primary optimization process when a certain conductor arrangement is assigned by AGA. The effectiveness of the proposed algorithm for optimal CSS is validated through simulations on modified IEEE 33-bus and IEEE 69-bus distribution systems.

Suggested Citation

  • Zhenghui Zhao & Joseph Mutale, 2019. "Optimal Conductor Size Selection in Distribution Networks with High Penetration of Distributed Generation Using Adaptive Genetic Algorithm," Energies, MDPI, vol. 12(11), pages 1-20, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:11:p:2065-:d:235608
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    Citations

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    Cited by:

    1. Moradi-Sarvestani, Sajjad & Jooshaki, Mohammad & Fotuhi-Firuzabad, Mahmud & Lehtonen, Matti, 2023. "Incorporating direct load control demand response into active distribution system planning," Applied Energy, Elsevier, vol. 339(C).
    2. 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.
    3. Julián David Pradilla-Rozo & Julián Alejandro Vega-Forero & Oscar Danilo Montoya, 2023. "Application of the Gradient-Based Metaheuristic Optimizerto Solve the Optimal Conductor Selection Problemin Three-Phase Asymmetric Distribution Networks," Energies, MDPI, vol. 16(2), pages 1-29, January.
    4. Yang Wang & Yifan Wang & Zhenghui Zhao & Zhiquan Zhou & Zhihao Hou, 2023. "Multi-Timescale Optimal Operation Strategy for Renewable Energy Power Systems Based on Inertia Evaluation," Energies, MDPI, vol. 16(8), pages 1-15, April.
    5. Luis A. Gallego Pareja & Jesús M. López-Lezama & Oscar Gómez Carmona, 2023. "Optimal Integration of Distribution Network Reconfiguration and Conductor Selection in Power Distribution Systems via MILP," Energies, MDPI, vol. 16(19), pages 1-25, October.
    6. Lewis Waswa & Munyaradzi Justice Chihota & Bernard Bekker, 2021. "A Probabilistic Conductor Size Selection Framework for Active Distribution Networks," Energies, MDPI, vol. 14(19), pages 1-19, October.
    7. Yi, Ji Hyun & Cherkaoui, Rachid & Paolone, Mario & Shchetinin, Dmitry & Knezovic, Katarina, 2022. "Expansion planning of active distribution networks achieving their dispatchability via energy storage systems," Applied Energy, Elsevier, vol. 326(C).

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