Optimal allocation of renewable DGs using artificial hummingbird algorithm under uncertainty conditions
Renewable distributed generators (RDGs) have been widely used in distribution networks for technological, economic, and environmental reasons. The main concern with renewable-based distributed generators, particularly photovoltaic and wind systems, is their intermittent nature, which causes output p...
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| Published in: | Ain Shams Engineering Journal Vol. 14; no. 2; p. 101872 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.03.2023
Elsevier |
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| ISSN: | 2090-4479 |
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| Abstract | Renewable distributed generators (RDGs) have been widely used in distribution networks for technological, economic, and environmental reasons. The main concern with renewable-based distributed generators, particularly photovoltaic and wind systems, is their intermittent nature, which causes output power to fluctuate, increasing power system uncertainty. As a result, it's critical to think about the resource's uncertainty when deciding where it should go in the grid. The main innovation of this paper is proposing an efficient and the most recent technique for optimal sizing and placement of the RDGs in radial distribution systems considering the uncertainties of the loading and RDGs output powers. Monte-Carlo simulation approach and backward reduction algorithm are used to generate 12 scenarios to model the uncertainties of loading and RDG output power. The artificial hummingbird algorithm (AHA), which is considered the most recent and efficient technique, is used to determine the RDG ratings and placements for a multi-objective function that includes minimizing expected total cost, the expected total emissions, and the expected total voltage deviation, as well as improving expected total voltage stability with considering the uncertainties of loading and RDGs output powers. The proposed technique is tested using an IEEE 33-bus network and an actual distribution system in Portugal (94-bus network). Simulations show that the suggested method effectively solves the problem of optimal DG allocation. In addition of that the expected costs, the emissions, the voltage deviation, are reduced considerably and the voltage stability is also enhanced with inclusion of RDGs in the tested systems. |
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| AbstractList | Renewable distributed generators (RDGs) have been widely used in distribution networks for technological, economic, and environmental reasons. The main concern with renewable-based distributed generators, particularly photovoltaic and wind systems, is their intermittent nature, which causes output power to fluctuate, increasing power system uncertainty. As a result, it's critical to think about the resource's uncertainty when deciding where it should go in the grid. The main innovation of this paper is proposing an efficient and the most recent technique for optimal sizing and placement of the RDGs in radial distribution systems considering the uncertainties of the loading and RDGs output powers. Monte-Carlo simulation approach and backward reduction algorithm are used to generate 12 scenarios to model the uncertainties of loading and RDG output power. The artificial hummingbird algorithm (AHA), which is considered the most recent and efficient technique, is used to determine the RDG ratings and placements for a multi-objective function that includes minimizing expected total cost, the expected total emissions, and the expected total voltage deviation, as well as improving expected total voltage stability with considering the uncertainties of loading and RDGs output powers. The proposed technique is tested using an IEEE 33-bus network and an actual distribution system in Portugal (94-bus network). Simulations show that the suggested method effectively solves the problem of optimal DG allocation. In addition of that the expected costs, the emissions, the voltage deviation, are reduced considerably and the voltage stability is also enhanced with inclusion of RDGs in the tested systems. |
| ArticleNumber | 101872 |
| Author | Kamel, Salah Ahmed, Emad M. Tostado-Véliz, Marcos Ramadan, Ashraf Ebeed, Mohamed |
| Author_xml | – sequence: 1 givenname: Ashraf surname: Ramadan fullname: Ramadan, Ashraf email: ashraframadanragab@gmail.com organization: Department of Electrical Engineering, Aswan University, Aswan 81542, Egypt – sequence: 2 givenname: Mohamed surname: Ebeed fullname: Ebeed, Mohamed email: mebeed@eng.sohag.edu.eg organization: Faculty of Engineering, Sohag University, Sohag 82524, Egypt – sequence: 3 givenname: Salah surname: Kamel fullname: Kamel, Salah email: skamel@aswu.edu.eg organization: Department of Electrical Engineering, Aswan University, Aswan 81542, Egypt – sequence: 4 givenname: Emad M. surname: Ahmed fullname: Ahmed, Emad M. email: emamahmoud@ju.edu.sa organization: Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia – sequence: 5 givenname: Marcos surname: Tostado-Véliz fullname: Tostado-Véliz, Marcos organization: Department of Electrical Engineering, University of Jaén, Jaén, Spain |
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| Keywords | Wind Backward reduction algorithm Artificial hummingbird algorithm Solar Renewable Energy Uncertainties Monte-Carlo simulation |
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banks using probabilistic generation models with correlations publication-title: Appl Energy – volume: 148 start-page: 1194 year: 2017 ident: 10.1016/j.asej.2022.101872_b0105 article-title: Optimal power flow solutions incorporating stochastic wind and solar power publication-title: Energy Convers Manag doi: 10.1016/j.enconman.2017.06.071 – ident: 10.1016/j.asej.2022.101872_b0060 doi: 10.1007/978-981-16-1642-6_19 – volume: 7 start-page: 164887 year: 2019 ident: 10.1016/j.asej.2022.101872_b0120 article-title: Optimal planning of renewable energy-integrated distribution system considering uncertainties publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2947308 – volume: 14 start-page: 5206 issue: 16 year: 2021 ident: 10.1016/j.asej.2022.101872_b0015 article-title: Solar-Based DG Allocation Using Harris Hawks Optimization While Considering Practical Aspects publication-title: Energies doi: 10.3390/en14165206 – volume: 4 start-page: 185 year: 2019 ident: 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| SubjectTerms | Artificial hummingbird algorithm Backward reduction algorithm Monte-Carlo simulation Renewable Energy Solar Uncertainties Wind |
| Title | Optimal allocation of renewable DGs using artificial hummingbird algorithm under uncertainty conditions |
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