Clustering Electrical Customers with Source Power and Aggregation Constraints: A Reliability-Based Approach in Power Distribution Systems

Thiago Eliandro de Oliveira Gomes (), André Ross Borniatti, Vinícius Jacques Garcia, Laura Lisiane Callai dos Santos, Nelson Knak Neto and Rui Anderson Ferrarezi Garcia
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Thiago Eliandro de Oliveira Gomes: Graduate Program in Production Engineering, Federal University of Santa Maria, Santa Maria 97105-900, Brazil
André Ross Borniatti: Graduate Program in Production Engineering, Federal University of Santa Maria, Santa Maria 97105-900, Brazil
Vinícius Jacques Garcia: Graduate Program in Production Engineering, Federal University of Santa Maria, Santa Maria 97105-900, Brazil
Laura Lisiane Callai dos Santos: Academic Coordination, Campus Cachoeira do Sul, Federal University of Santa Maria, Santa Maria 97105-900, Brazil
Nelson Knak Neto: Academic Coordination, Campus Cachoeira do Sul, Federal University of Santa Maria, Santa Maria 97105-900, Brazil
Rui Anderson Ferrarezi Garcia: State Electric Power Company (CEEE), Equatorial Energy Group, Porto Alegre 91410-400, Brazil

Energies, 2023, vol. 16, issue 5, 1-20

Abstract: Reliability is an important issue in electricity distribution systems, with strict regulatory policies and investments needed to improve it. This paper presents a mixed integer linear programming (MILP) model for clustering electrical customers, maximizing system reliability and minimizing outage costs. However, the evaluation of reliability and its corresponding nonlinear function represent a significant challenge, making the use of mathematical programming models difficult. The proposed heuristic procedure overcomes this challenge by using a linear formulation of reliability indicators and incorporating them into the MILP model for clustering electrical customers. The model is mainly defined on a density-based heuristic that constrains the set of possible medians, thus dealing with the combinatorial complexity associated with the problem of empowered p-medians. The proposed model proved to be effective in improving the reliability of real electrical distribution systems and reducing compensation costs. Three substation cluster scenarios were explored, in which the total utility compensations were reduced by approximately USD 86,000 (1.80%), USD 67,400 (1.41%), and USD 64,000 (1.3%). The solutions suggest a direct relationship between the reduction in the compensation costs and the system reliability. In addition, the alternative modeling approach to the problem served to match the performance between the distribution system reliability indicators.

Keywords: clustering electrical customers; reliability in power distribution systems; nonlinear; financial compensation (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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