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A MODA and MODE Comparison for Optimal Allocation of Distributed Generations with Different Load Levels

Author

Listed:
  • Salem Alkhalaf

    (Department of Computer Science, Alrass College of Science and Arts, Qassim University, Qassim, Arass 51921, Saudi Arabia)

  • Tomonobu Senjyu

    (Department of Electrical and Electronics Engineering, Faculty of Engineering, University of the Ryukyus, Senbaru 9030213, Japan)

  • Ayat Ali Saleh

    (Department of Electrical Engineering, Faculty of Energy Engineering, Aswan University, Aswan 81528, Egypt)

  • Ashraf M. Hemeida

    (Department of Electrical Engineering, Faculty of Energy Engineering, Aswan University, Aswan 81528, Egypt)

  • Al-Attar Ali Mohamed

    (Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, Egypt)

Abstract

In this paper, the performance of different optimization techniques namely, multi-objective dragonfly algorithm (MODA) and multi-objective differential evolution (MODE) are presented and compared. The uncertainty effect of a wind turbine (WT) on the performance of the distribution system is taken into account. The point estimate method (PEM) is used to model the uncertainty in wind power. Optimization methods are applied to determine the multi-objective optimal allocation of distributed generation (DG) in radial distribution systems at a different load level (light, normal, heavy load level). The multi-objective function is expressed to minimize the total power loss, total operating cost, and improve the voltage stability index of the radial distribution system (RDS). Multi-objective proposed algorithms are used to generate the Pareto optimal solutions; and a fuzzy decision-making function is used to produce a hybrid function for obtaining the best compromise solution. The proposed algorithms are carried out on 33-bus and IEEE-69-bus power systems. The simulation results show the effectiveness of installing the proper size of DG at the suitable location based on different techniques.

Suggested Citation

  • Salem Alkhalaf & Tomonobu Senjyu & Ayat Ali Saleh & Ashraf M. Hemeida & Al-Attar Ali Mohamed, 2019. "A MODA and MODE Comparison for Optimal Allocation of Distributed Generations with Different Load Levels," Sustainability, MDPI, vol. 11(19), pages 1-18, September.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:19:p:5323-:d:271038
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    References listed on IDEAS

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    1. Mohamed A. Tolba & Hegazy Rezk & Vladimir Tulsky & Ahmed A. Zaki Diab & Almoataz Y. Abdelaziz & Artem Vanin, 2018. "Impact of Optimum Allocation of Renewable Distributed Generations on Distribution Networks Based on Different Optimization Algorithms," Energies, MDPI, vol. 11(1), pages 1-33, January.
    2. Haddadian, Hossein & Noroozian, Reza, 2017. "Multi-microgrids approach for design and operation of future distribution networks based on novel technical indices," Applied Energy, Elsevier, vol. 185(P1), pages 650-663.
    3. Wang, Shouxiang & Wang, Kai & Teng, Fei & Strbac, Goran & Wu, Lei, 2018. "An affine arithmetic-based multi-objective optimization method for energy storage systems operating in active distribution networks with uncertainties," Applied Energy, Elsevier, vol. 223(C), pages 215-228.
    4. Navdeep Kaur & Sanjay Kumar Jain, 2017. "Multi-Objective Optimization Approach for Placement of Multiple DGs for Voltage Sensitive Loads," Energies, MDPI, vol. 10(11), pages 1-17, October.
    5. José Raúl Castro & Maarouf Saad & Serge Lefebvre & Dalal Asber & Laurent Lenoir, 2016. "Coordinated Voltage Control in Distribution Network with the Presence of DGs and Variable Loads Using Pareto and Fuzzy Logic," Energies, MDPI, vol. 9(2), pages 1-16, February.
    6. Aghajani, G.R. & Shayanfar, H.A. & Shayeghi, H., 2017. "Demand side management in a smart micro-grid in the presence of renewable generation and demand response," Energy, Elsevier, vol. 126(C), pages 622-637.
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    Citations

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    Cited by:

    1. 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.
    2. Karar Mahmoud & Mohamed Abdel-Nasser & Eman Mustafa & Ziad M. Ali, 2020. "Improved Salp–Swarm Optimizer and Accurate Forecasting Model for Dynamic Economic Dispatch in Sustainable Power Systems," Sustainability, MDPI, vol. 12(2), pages 1-21, January.
    3. Mahmoud Hemeida & Tomonobu Senjyu & Salem Alkhalaf & Asmaa Fawzy & Mahrous Ahmed & Dina Osheba, 2022. "Reactive Power Management Based Hybrid GAEO," Sustainability, MDPI, vol. 14(11), pages 1-17, June.
    4. Ayat Ali Saleh & Tomonobu Senjyu & Salem Alkhalaf & Majed A. Alotaibi & Ashraf M. Hemeida, 2020. "Water Cycle Algorithm for Probabilistic Planning of Renewable Energy Resource, Considering Different Load Models," Energies, MDPI, vol. 13(21), pages 1-24, November.

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