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Allocation of Renewable Energy Resources in Distribution Systems While Considering the Uncertainty of Wind and Solar Resources via the Multi-Objective Salp Swarm Algorithm

Author

Listed:
  • Iraj Faraji Davoudkhani

    (Department of Electrical Engineering, University of Mohaghegh Ardabili, Ardabil 5619913131, Iran)

  • Farhad Zishan

    (Department of Electrical Engineering, Sahand University of Technology, Tabriz 5513351996, Iran)

  • Saeedeh Mansouri

    (Faculty of Electrical and Computer engineering, Babol Noushirvaniy University of Technology, Babol 4714873113, Iran)

  • Farzad Abdollahpour

    (Department of Electrical Engineering, University of Kurdistan, Sanandaj 6617715175, Iran)

  • Luis Fernando Grisales-Noreña

    (Department of Electrical Engineering, Faculty of Engineering, Universidad de Talca, Curicó 3340000, Chile)

  • Oscar Danilo Montoya

    (Grupo de Compatibilidad e Interferencia Electromágnetica (GCEM), Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Bogotá 110231, Colombia
    Laboratorio Inteligente de Energía, Facultad de Ingeniería, Universidad Tecnológica de Bolívar, Cartagena 131001, Colombia)

Abstract

Given the importance of renewable energy sources in distribution systems, this article addresses the problem of locating and determining the capacity of these sources, namely, wind turbines and solar panels. To solve this optimization problem, a new algorithm based on the behavior of salp is used. The objective functions include reducing losses, improving voltage profiles, and reducing the costs of renewable energy sources. In this method, the allocation of renewable resources is considered for different load models in distribution systems and different load levels using smart meters. Due to the fact that these objective functions are multi-objective, the fuzzy decision-making method is used to select the optimal solution from the set of Pareto solutions. The considered objective functions lead to loss reduction, voltage profile improvement, and RES cost reduction (A allocating RES resources optimally without resource limitations; B: allocating RES resources optimally with resource limitations). In addition, daily wind, solar radiation, and temperature data are taken into account. The proposed method is applied to the IEEE standard 33-bus system. The simulation results show the better performance of the multi-objective salp swarm algorithm (MSSA) at improving voltage profiles and reducing losses in distribution systems. Lastly, the optimal results of the MSSA algorithm are compared with the PSO and GA algorithms.

Suggested Citation

  • Iraj Faraji Davoudkhani & Farhad Zishan & Saeedeh Mansouri & Farzad Abdollahpour & Luis Fernando Grisales-Noreña & Oscar Danilo Montoya, 2023. "Allocation of Renewable Energy Resources in Distribution Systems While Considering the Uncertainty of Wind and Solar Resources via the Multi-Objective Salp Swarm Algorithm," Energies, MDPI, vol. 16(1), pages 1-17, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:1:p:474-:d:1022186
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    References listed on IDEAS

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    1. Kong, Xue & Wang, Hongye & Li, Nan & Mu, Hailin, 2022. "Multi-objective optimal allocation and performance evaluation for energy storage in energy systems," Energy, Elsevier, vol. 253(C).
    2. Han, Xiaojuan & Zhang, Hua & Yu, Xiaoling & Wang, Lina, 2016. "Economic evaluation of grid-connected micro-grid system with photovoltaic and energy storage under different investment and financing models," Applied Energy, Elsevier, vol. 184(C), pages 103-118.
    3. Zhang, Xingxing & Lovati, Marco & Vigna, Ilaria & Widén, Joakim & Han, Mengjie & Gal, Csilla & Feng, Tao, 2018. "A review of urban energy systems at building cluster level incorporating renewable-energy-source (RES) envelope solutions," Applied Energy, Elsevier, vol. 230(C), pages 1034-1056.
    4. Rahiminejad, A. & Vahidi, B. & Hejazi, M.A. & Shahrooyan, S., 2016. "Optimal scheduling of dispatchable distributed generation in smart environment with the aim of energy loss minimization," Energy, Elsevier, vol. 116(P1), pages 190-201.
    5. Ali, E.S. & Abd Elazim, S.M. & Abdelaziz, A.Y., 2016. "Ant Lion Optimization Algorithm for Renewable Distributed Generations," Energy, Elsevier, vol. 116(P1), pages 445-458.
    6. Oscar Danilo Montoya & Farhad Zishan & Diego Armando Giral-Ramírez, 2022. "Recursive Convex Model for Optimal Power Flow Solution in Monopolar DC Networks," Mathematics, MDPI, vol. 10(19), pages 1-14, October.
    7. Kasaei, Mohammad Javad & Gandomkar, Majid & Nikoukar, Javad, 2017. "Optimal management of renewable energy sources by virtual power plant," Renewable Energy, Elsevier, vol. 114(PB), pages 1180-1188.
    8. Bartolucci, Lorenzo & Cordiner, Stefano & Mulone, Vincenzo & Rocco, Vittorio & Rossi, Joao Luis, 2018. "Hybrid renewable energy systems for renewable integration in microgrids: Influence of sizing on performance," Energy, Elsevier, vol. 152(C), pages 744-758.
    9. Adnan Ahmad & Asif Khan & Nadeem Javaid & Hafiz Majid Hussain & Wadood Abdul & Ahmad Almogren & Atif Alamri & Iftikhar Azim Niaz, 2017. "An Optimized Home Energy Management System with Integrated Renewable Energy and Storage Resources," Energies, MDPI, vol. 10(4), pages 1-35, April.
    10. Pupo-Roncallo, Oscar & Campillo, Javier & Ingham, Derek & Hughes, Kevin & Pourkashanian, Mohammed, 2019. "Large scale integration of renewable energy sources (RES) in the future Colombian energy system," Energy, Elsevier, vol. 186(C).
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    Cited by:

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    2. Jiyong Li & Ran Chen & Chengye Liu & Xiaoshuai Xu & Yasai Wang, 2023. "Capacity Optimization of Independent Microgrid with Electric Vehicles Based on Improved Pelican Optimization Algorithm," Energies, MDPI, vol. 16(6), pages 1-23, March.
    3. Farhad Zishan & Lilia Tightiz & Joon Yoo & Nima Shafaghatian, 2023. "Sustainability of the Permanent Magnet Synchronous Generator Wind Turbine Control Strategy in On-Grid Operating Modes," Energies, MDPI, vol. 16(10), pages 1-18, May.

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