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Architecture of the Florida power grid as a complex network

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  • Xu, Yan
  • Gurfinkel, Aleks Jacob
  • Rikvold, Per Arne

Abstract

We study the Florida high-voltage power grid as a technological network embedded in space. Measurements of geographical lengths of transmission lines, the mixing of generators and loads, the weighted clustering coefficient, as well as the organization of edge conductance weights show a complex architecture quite different from random-graph models usually considered. In particular, we introduce a parametrized mixing matrix to characterize the mixing pattern of generators and loads in the Florida Grid, which is intermediate between the random mixing case and the semi-bipartite case where generator–generator transmission lines are forbidden. Our observations motivate an investigation of optimization (design) principles leading to the structural organization of power grids. We thus propose two network optimization models for the Florida Grid as a case study. Our results show that the Florida Grid is optimized not only by reducing the construction cost (measured by the total length of power lines), but also through reducing the total pairwise edge resistance in the grid, which increases the robustness of power transmission between generators and loads against random line failures. We then embed our models in spatial areas of different aspect ratios and study how this geometric factor affects the network structure, as well as the box-counting fractal dimension of the grids generated by our models.

Suggested Citation

  • Xu, Yan & Gurfinkel, Aleks Jacob & Rikvold, Per Arne, 2014. "Architecture of the Florida power grid as a complex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 401(C), pages 130-140.
    • Handle: RePEc:eee:phsmap:v:401:y:2014:i:c:p:130-140
    DOI: 10.1016/j.physa.2014.01.035
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    References listed on IDEAS

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    1. Gallos, Lazaros K. & Song, Chaoming & Makse, Hernán A., 2007. "A review of fractality and self-similarity in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 386(2), pages 686-691.
    2. Pagani, Giuliano Andrea & Aiello, Marco, 2013. "The Power Grid as a complex network: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(11), pages 2688-2700.
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    Cited by:

    1. Abedi, Amin & Gaudard, Ludovic & Romerio, Franco, 2019. "Review of major approaches to analyze vulnerability in power system," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 153-172.
    2. Lucas Cuadra & Miguel Del Pino & José Carlos Nieto-Borge & Sancho Salcedo-Sanz, 2017. "Optimizing the Structure of Distribution Smart Grids with Renewable Generation against Abnormal Conditions: A Complex Networks Approach with Evolutionary Algorithms," Energies, MDPI, vol. 10(8), pages 1-31, July.
    3. Shahpari, Alireza & Khansari, Mohammad & Moeini, Ali, 2019. "Vulnerability analysis of power grid with the network science approach based on actual grid characteristics: A case study in Iran," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 14-21.
    4. Lucas Cuadra & Sancho Salcedo-Sanz & Javier Del Ser & Silvia Jiménez-Fernández & Zong Woo Geem, 2015. "A Critical Review of Robustness in Power Grids Using Complex Networks Concepts," Energies, MDPI, vol. 8(9), pages 1-55, August.
    5. Elisa Frutos Bernal & Angel Martín del Rey, 2019. "Study of the Structural and Robustness Characteristics of Madrid Metro Network," Sustainability, MDPI, vol. 11(12), pages 1-24, June.
    6. Hu, Jianqiang & Yu, Jie & Cao, Jinde & Ni, Ming & Yu, Wenjie, 2014. "Topological interactive analysis of power system and its communication module: A complex network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 99-111.

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