Impact of Optimum Allocation of Renewable Distributed Generations on Distribution Networks Based on Different Optimization Algorithms

Mohamed A. Tolba, Hegazy Rezk, Vladimir Tulsky, Ahmed A. Zaki Diab, Almoataz Y. Abdelaziz and Artem Vanin
Additional contact information
Mohamed A. Tolba: Nuclear Researches Center, Egyptian Atomic Energy Authority (EAEA), 11787 Cairo, Egypt
Hegazy Rezk: College of Engineering at Wadi Aldawaser, Prince Sattam bin Abdulaziz University, 11991 Wadi Aldawaser, Saudi Arabia
Vladimir Tulsky: Electrical Power Systems Department, NRU, Moscow Power Engineering Institute, 111250 Moscow, Russia
Ahmed A. Zaki Diab: Electrical Power Systems Department, NRU, Moscow Power Engineering Institute, 111250 Moscow, Russia
Almoataz Y. Abdelaziz: Electrical Power and Machines Department, Faculty of Engineering, Ain Shams University, 11517 Cairo, Egypt
Artem Vanin: Electrical Power Systems Department, NRU, Moscow Power Engineering Institute, 111250 Moscow, Russia

Energies, 2018, vol. 11, issue 1, 1-33

Abstract: Integration of Renewable Distributed Generations (RDGs) such as photovoltaic (PV) systems and wind turbines (WTs) in distribution networks can be considered a brilliant and efficient solution to the growing demand for energy. This article introduces new robust and effective techniques like hybrid Particle Swarm Optimization in addition to a Gravitational Search Algorithm (PSOGSA) and Moth-Flame Optimization (MFO) that are proposed to deduce the optimum location with convenient capacity of RDGs units for minimizing system power losses and operating cost while improving voltage profile and voltage stability. This paper describes two stages. First, the Loss Sensitivity Factors (LSFs) are employed to select the most candidate buses for RDGs location. In the second stage, the PSOGSA and MFO are implemented to deduce the optimal location and capacity of RDGs from the elected buses. The proposed schemes have been applied on 33-bus and 69-bus IEEE standard radial distribution systems. To insure the suggested approaches validity, the numerical results have been compared with other techniques like Backtracking Search Optimization Algorithm (BSOA), Genetic Algorithm (GA), Particle Swarm Algorithm (PSO), Novel combined Genetic Algorithm and Particle Swarm Optimization (GA/PSO), Simulation Annealing Algorithm (SA), and Bacterial Foraging Optimization Algorithm (BFOA). The evaluated results have been confirmed the superiority with high performance of the proposed MFO technique to find the optimal solutions of RDGs units’ allocation. In this regard, the MFO is chosen to solve the problems of Egyptian Middle East distribution network as a practical case study with the optimal integration of RDGs.

Keywords: renewable distributed generations; loss sensitivity factors; voltage deviation index; power loss index; total operating cost; PSOGSA optimizer; Moth-Flame optimizer (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: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)

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