A Mixed-Integer Convex Optimization Framework for Cost-Effective Conductor Selection in Radial Distribution Networks While Considering Load and Renewable Variations

The optimal selection of conductors (OCS) in radial distribution networks is a critical aspect of system planning, directly impacting both investment costs and energy losses. This paper proposed a mixed-integer convex (MI-Convex) optimization framework to solve the OCS problem under balanced operati...

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Bibliographic Details
Published in:Sci Vol. 7; no. 2; p. 72
Main Authors: Montoya, Oscar Danilo, Florez-Cediel, Oscar David, Grisales-Noreña, Luis Fernando, Gil-González, Walter, Giral-Ramírez, Diego Armando
Format: Journal Article
Language:English
Published: Basel MDPI AG 03.06.2025
Subjects:
Accuracy
Approximation
Convex analysis
Costs
Efficiency
electric distribution networks
energy losses costs
Genetic algorithms
Heuristic
investment costs in conductors
Linear programming
Methods
mixed-integer convex programming
optimal selection of conductors
Optimization techniques
radial distribution grids
Reproducibility
Simulation
Test systems
ISSN:2413-4155, 2413-4155
Online Access:Get full text
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Summary:The optimal selection of conductors (OCS) in radial distribution networks is a critical aspect of system planning, directly impacting both investment costs and energy losses. This paper proposed a mixed-integer convex (MI-Convex) optimization framework to solve the OCS problem under balanced operating conditions, integrating the costs of conductor investment and energy losses into a single convex objective. This formulation leveraged second-order conic constraints and was solved using a combination of branch-and-bound and interior-point methods. Numerical validations on standard 27-, 33-, and 85-bus test systems confirmed the effectiveness of the proposal. In the 27-bus grid, the MI-Convex approach achieved a total cost of $550,680.25, outperforming or matching the best results reported by state-of-the-art metaheuristic algorithms, including the vortex search algorithm (VSA), Newton’s metaheuristic algorithm (NMA), the generalized normal distribution optimizer (GNDO), and the tabu search algorithm (TSA). The MI-Convex method demonstrated consistent and repeatable results, in contrast to the variability observed in heuristic techniques. Further analyses considering three-period and daily load profiles led to cost reductions of up to 27.6%, and incorporating distributed renewable generation into the 85-bus system achieved a total cost of $705,197.06—approximately 22.97% lower than under peak-load planning. Moreover, the methodology proved computationally efficient, requiring only 1.84 s for the 27-bus and 12.27 s for the peak scenario of the 85-bus. These results demonstrate the superiority of the MI-Convex approach in achieving globally optimal, reproducible, and computationally tractable solutions for cost-effective conductor selection.
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ISSN:2413-4155
2413-4155
DOI:10.3390/sci7020072