The Probabilistic Optimal Integration of Renewable Distributed Generators Considering the Time-Varying Load Based on an Artificial Gorilla Troops Optimizer

Ashraf Ramadan, Mohamed Ebeed, Salah Kamel, Ahmed M. Agwa and Marcos Tostado-Véliz
Additional contact information
Ashraf Ramadan: Department of Electrical Engineering, Aswan University, Aswan 81542, Egypt
Mohamed Ebeed: Faculty of Engineering, Sohag University, Sohag 82524, Egypt
Salah Kamel: Department of Electrical Engineering, Aswan University, Aswan 81542, Egypt
Ahmed M. Agwa: Department of Electrical Engineering, College of Engineering, Northern Border University, Arar 1321, Saudi Arabia
Marcos Tostado-Véliz: Department of Electrical Engineering, University of Jaén, 23700 EPS Linares, Spain

Energies, 2022, vol. 15, issue 4, 1-22

Abstract: Renewable distributed generators (RDGs) are widely embedded in electrical distribution networks due to their economic, technological, and environmental benefits. However, the main problem with RDGs, photovoltaic generators, and wind turbines, in particular, is that their output powers are constantly changing due to variations in sun irradiation and wind speed, leading to power system uncertainty. Such uncertainties should be taken into account when selecting the optimal allocation of RDGs. The main innovation of this paper is a proposed efficient metaheuristic optimization technique for the sizing and placement of RDGs in radial distribution systems considering the uncertainties of the loading and RDG output power. A Monte Carlo simulation method, along with the backward reduction algorithm, is utilized to create a set of scenarios to model these uncertainties. To find the positions and ratings of the RDGs, the artificial gorilla troops optimizer (GTO), a new efficient strategy that minimizes the total cost, is used to optimize a multiobjective function, total emissions, and total voltage deviations, as well as the total voltage stability boosting. The proposed technique is tested on an IEEE 69-bus network and a real Egyptian distribution grid (East Delta Network (EDN) 30-bus network). The results indicate that the proposed GTO can optimally assign the positions and ratings of RDGs. Moreover, the integration of RDGs into an IEEE 69-bus system can reduce the expected costs, emissions, and voltage deviations by 28.3%, 52.34%, and 66.95%, respectively, and improve voltage stability by 5.6%; in the EDN 30-bus system, these values are enhanced by 25.97%, 51.1%, 67.25%, and 7.7%, respectively.

Keywords: renewable energy; solar; wind; DG; uncertainties; gorilla troops optimizer; radial distribution system; backward reduction methodology; Monte Carlo simulation approach (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/4/1302/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/4/1302/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:4:p:1302-:d:746880

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().