Multi Dimension-Based Optimal Allocation of Uncertain Renewable Distributed Generation Outputs with Seasonal Source-Load Power Uncertainties in Electrical Distribution Network Using Marine Predator Algorithm

Nasreddine Belbachir, Mohamed Zellagui (), Samir Settoul, Claude Ziad El-Bayeh and Ragab A. El-Sehiemy
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Nasreddine Belbachir: Department of Electrotechnics, University of Mostaganem, Mostaganem 27000, Algeria
Mohamed Zellagui: Department of Electrical Engineering, University of Batna 2, Batna 05078, Algeria
Samir Settoul: Department of Electrotechnics, University of Constantine 1, Constantine 27017, Algeria
Claude Ziad El-Bayeh: Canada Excellence Research Chairs Team, Concordia University, Montreal, QC H3G 1M8, Canada
Ragab A. El-Sehiemy: Department of Electrical Engineering, Kafrelsheikh University, Kafr El Sheikh 6860404, Egypt

Energies, 2023, vol. 16, issue 4, 1-24

Abstract: In the last few years, the integration of renewable distributed generation (RDG) in the electrical distribution network (EDN) has become a favorable solution that guarantees and keeps a satisfying balance between electrical production and consumption of energy. In this work, various metaheuristic algorithms were implemented to perform the validation of their efficiency in delivering the optimal allocation of both RDGs based on multiple photovoltaic distributed generation (PVDG) and wind turbine distributed generation (WTDG) to the EDN while considering the uncertainties of their electrical energy output as well as the load demand’s variation during all the year’s seasons. The convergence characteristics and the results reveal that the marine predator algorithm was effectively the quickest and best technique to attain the best solutions after a small number of iterations compared to the rest of the utilized algorithms, including particle swarm optimization, the whale optimization algorithm, moth flame optimizer algorithms, and the slime mold algorithm. Meanwhile, as an example, the marine predator algorithm minimized the seasonal active losses down to 56.56% and 56.09% for both applied networks of IEEE 33 and 69-bus, respectively. To reach those results, a multi-objective function (MOF) was developed to simultaneously minimize the technical indices of the total active power loss index (APLI) and reactive power loss index (RPLI), voltage deviation index (VDI), operating time index (OTI), and coordination time interval index (CTII) of overcurrent relay in the test system EDNs, in order to approach the practical case, in which there are too many parameters to be optimized, considering different constraints, during the uncertain time and variable data of load and energy production.

Keywords: renewable distributed generation; hybrid PVDG-WTDG units; electrical distribution network; seasonal uncertainties; marine predator 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: 2023
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