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

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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)

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.

Suggested Citation

  • Thiago Eliandro de Oliveira Gomes & André Ross Borniatti & Vinícius Jacques Garcia & Laura Lisiane Callai dos Santos & Nelson Knak Neto & Rui Anderson Ferrarezi Garcia, 2023. "Clustering Electrical Customers with Source Power and Aggregation Constraints: A Reliability-Based Approach in Power Distribution Systems," Energies, MDPI, vol. 16(5), pages 1-20, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2485-:d:1088487
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    References listed on IDEAS

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    1. Sebastián Moreno & Jordi Pereira & Wilfredo Yushimito, 2020. "A hybrid K-means and integer programming method for commercial territory design: a case study in meat distribution," Annals of Operations Research, Springer, vol. 286(1), pages 87-117, March.
    2. Silvia Corigliano & Federico Rosato & Carla Ortiz Dominguez & Marco Merlo, 2021. "Clustering Techniques for Secondary Substations Siting," Energies, MDPI, vol. 14(4), pages 1-18, February.
    3. Al-Wakeel, Ali & Wu, Jianzhong & Jenkins, Nick, 2017. "k-means based load estimation of domestic smart meter measurements," Applied Energy, Elsevier, vol. 194(C), pages 333-342.
    4. Rajabi, Amin & Eskandari, Mohsen & Ghadi, Mojtaba Jabbari & Li, Li & Zhang, Jiangfeng & Siano, Pierluigi, 2020. "A comparative study of clustering techniques for electrical load pattern segmentation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 120(C).
    5. Alexander Tureczek & Per Sieverts Nielsen & Henrik Madsen, 2018. "Electricity Consumption Clustering Using Smart Meter Data," Energies, MDPI, vol. 11(4), pages 1-18, April.
    6. Ahmadi, Samad & Osman, Ibrahim H., 2005. "Greedy random adaptive memory programming search for the capacitated clustering problem," European Journal of Operational Research, Elsevier, vol. 162(1), pages 30-44, April.
    7. Hsu, David, 2015. "Comparison of integrated clustering methods for accurate and stable prediction of building energy consumption data," Applied Energy, Elsevier, vol. 160(C), pages 153-163.
    8. Barbosa, Ailson de Souza & Shayani, Rafael Amaral & Oliveira, Marco Aurélio Gonçalves de, 2018. "A multi-criteria decision analysis method for regulatory evaluation of electricity distribution service quality," Utilities Policy, Elsevier, vol. 53(C), pages 38-48.
    9. Teichgraeber, Holger & Brandt, Adam R., 2019. "Clustering methods to find representative periods for the optimization of energy systems: An initial framework and comparison," Applied Energy, Elsevier, vol. 239(C), pages 1283-1293.
    10. Rhodes, Joshua D. & Cole, Wesley J. & Upshaw, Charles R. & Edgar, Thomas F. & Webber, Michael E., 2014. "Clustering analysis of residential electricity demand profiles," Applied Energy, Elsevier, vol. 135(C), pages 461-471.
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