Optimal Allocation and Planning of Distributed Power Generation Resources in a Smart Distribution Network Using the Manta Ray Foraging Optimization Algorithm

Masoud Zahedi Vahid, Ziad M. Ali, Ebrahim Seifi Najmi, Abdollah Ahmadi, Foad H. Gandoman and Shady H. E. Abdel Aleem
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Masoud Zahedi Vahid: Department of Electrical and Computer Engineering, University of Sistan and Baluchestan, Zahedan 9816745785, Iran
Ziad M. Ali: Electrical Engineering Department, Aswan faculty of Engineering, Aswan University, Aswan 81542, Egypt
Ebrahim Seifi Najmi: Roshdieh Higher Institue of Education, Tabriz 5166616471, Iran
Abdollah Ahmadi: School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney 2052, Australia
Foad H. Gandoman: Research Group MOBI–Mobility, Logistics, and Automotive Technology Research Centre, Vrije Universiteit Brussel, 1050 Brussels, Belgium
Shady H. E. Abdel Aleem: Department of Electrical Engineering and Electronics, Valley Higher Institute of Engineering and Technology, Science Valley Academy, Al-Qalyubia 44971, Egypt

Energies, 2021, vol. 14, issue 16, 1-25

Abstract: In this study, optimal allocation and planning of power generation resources as distributed generation with scheduling capability (DGSC) is presented in a smart environment with the objective of reducing losses and considering enhancing the voltage profile is performed using the manta ray foraging optimization (MRFO) algorithm. The DGSC refers to resources that can be scheduled and their generation can be determined based on network requirements. The main purpose of this study is to schedule and intelligent distribution of the DGSCs in the smart and conventional distribution network to enhance its operation. First, allocation of the DGSCs is done based on weighted coefficient method and then the scheduling of the DGSCs is implemented in the 69-bus distribution network. In this study, the effect of smart network by providing real load in minimizing daily energy losses is compared with the network includes conventional load (estimated load as three-level load). The simulation results cleared that optimal allocation and planning of the DGSCs can be improved the distribution network operation with reducing the power losses and also enhancing the voltage profile. The obtained results confirmed superiority of the MRFO compared with well-known particle swarm optimization (PSO) in the DGSCs allocation. The results also showed that increasing the number of DGSCs reduces more losses and improves more the network voltage profile. The achieved results demonstrated that the energy loss in smart network is less than the network with conventional load. In other words, any error in forecasting load demand leads to non-optimal operating point and more energy losses.

Keywords: smart distribution network; distributed generation with scheduling capability; power generation resources; manta ray foraging optimization algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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