Impact of Optimum Allocation of Renewable Distributed Generations on Distribution Networks Based on Different Optimization Algorithms Article Swipe
Mohamed A. Tolba
,
Hegazy Rezk
,
Vladimir N. Tulsky
,
Ahmed A. Zaki Diab
,
Almoataz Y. Abdelaziz
,
Artem Vanin
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.3390/en11010245
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.3390/en11010245
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/en11010245
- https://www.mdpi.com/1996-1073/11/1/245/pdf?version=1516602266
- OA Status
- gold
- Cited By
- 57
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2786375577
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2786375577Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/en11010245Digital Object Identifier
- Title
-
Impact of Optimum Allocation of Renewable Distributed Generations on Distribution Networks Based on Different Optimization AlgorithmsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-01-19Full publication date if available
- Authors
-
Mohamed A. Tolba, Hegazy Rezk, Vladimir N. Tulsky, Ahmed A. Zaki Diab, Almoataz Y. Abdelaziz, Artem VaninList of authors in order
- Landing page
-
https://doi.org/10.3390/en11010245Publisher landing page
- PDF URL
-
https://www.mdpi.com/1996-1073/11/1/245/pdf?version=1516602266Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1996-1073/11/1/245/pdf?version=1516602266Direct OA link when available
- Concepts
-
Particle swarm optimization, Mathematical optimization, Simulated annealing, Computer science, Genetic algorithm, Distributed generation, Photovoltaic system, Meta-optimization, Algorithm, Renewable energy, Engineering, Mathematics, Electrical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
57Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 5, 2024: 8, 2023: 6, 2022: 8, 2021: 8Per-year citation counts (last 5 years)
- References (count)
-
31Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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