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Optimal expansion of an existing electrical power transmission network by multi-objective genetic algorithms

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  • Cadini, F.
  • Zio, E.
  • Petrescu, C.A.

Abstract

In this paper, the optimal expansion of a power transmission network by addition of new connection links is addressed. Optimality is searched with respect to two objectives: the transmission reliability efficiency and the cost of the added transmission links. The multi-objective optimization problem is tackled by means of three different genetic algorithm paradigms, opportunely biased to give preference to solutions with a low number of added links, for practical applicability. The three approaches are applied to a reference power transmission network of the literature, the IEEE RTS 96; the results obtained are compared with respect to the efficacy of driving the search towards the preferred region of the solution space. Finally, an interpretation of the results is offered in terms of a properly defined reliability-based centrality measure.

Suggested Citation

  • Cadini, F. & Zio, E. & Petrescu, C.A., 2010. "Optimal expansion of an existing electrical power transmission network by multi-objective genetic algorithms," Reliability Engineering and System Safety, Elsevier, vol. 95(3), pages 173-181.
    • Handle: RePEc:eee:reensy:v:95:y:2010:i:3:p:173-181
    DOI: 10.1016/j.ress.2009.09.007
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    References listed on IDEAS

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    1. E. Zio, 2007. "From complexity science to reliability efficiency: a new way of looking at complex network systems and critical infrastructures," International Journal of Critical Infrastructures, Inderscience Enterprises Ltd, vol. 3(3/4), pages 488-508.
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    2. Hemmati, Reza & Hooshmand, Rahmat-Allah & Khodabakhshian, Amin, 2016. "Coordinated generation and transmission expansion planning in deregulated electricity market considering wind farms," Renewable Energy, Elsevier, vol. 85(C), pages 620-630.
    3. Bi, Wenzhe & Tian, Li & Li, Chao & Ma, Zhen & Pan, Haiyang, 2023. "Wind-induced failure analysis of a transmission tower-line system with long-term measured data and orientation effect," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    4. Zohre Alipour & Mohammad Ali Saniee Monfared & Enrico Zio, 2014. "Comparing topological and reliability-based vulnerability analysis of Iran power transmission network," Journal of Risk and Reliability, , vol. 228(2), pages 139-151, April.
    5. Han, Fangyuan & Zio, Enrico, 2019. "A multi-perspective framework of analysis of critical infrastructures with respect to supply service, controllability and topology," International Journal of Critical Infrastructure Protection, Elsevier, vol. 24(C), pages 1-13.
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    7. Fang, Yiping & Sansavini, Giovanni, 2017. "Optimizing power system investments and resilience against attacks," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 161-173.
    8. Yiping Fang & Nicola Pedroni & Enrico Zio, 2015. "Optimization of Cascade‐Resilient Electrical Infrastructures and its Validation by Power Flow Modeling," Risk Analysis, John Wiley & Sons, vol. 35(4), pages 594-607, April.
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    10. Linn Svegrup & Jonas Johansson & Henrik Hassel, 2019. "Integration of Critical Infrastructure and Societal Consequence Models: Impact on Swedish Power System Mitigation Decisions," Risk Analysis, John Wiley & Sons, vol. 39(9), pages 1970-1996, September.
    11. 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.
    12. Marie-Louise Kloubert, 2020. "Probabilistic Load Flow Approach Considering Dependencies of Wind Speed, Solar Irradiance, Electrical Load and Energy Exchange with a Joint Probability Distribution Model," Energies, MDPI, vol. 13(7), pages 1-15, April.
    13. Kishore, T.S. & Singal, S.K., 2014. "Optimal economic planning of power transmission lines: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 949-974.
    14. Wu, Jiajing & Fang, Biaoyan & Fang, Junyuan & Chen, Xi & Tse, Chi K., 2019. "Sequential topology recovery of complex power systems based on reinforcement learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    15. Zhou, Jian & Coit, David W. & Felder, Frank A. & Tsianikas, Stamatis, 2023. "Combined optimization of system reliability improvement and resilience with mixed cascading failures in dependent network systems," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    16. Lin, Yi-Kuei & Yeh, Cheng-Ta, 2011. "Maximal network reliability for a stochastic power transmission network," Reliability Engineering and System Safety, Elsevier, vol. 96(10), pages 1332-1339.
    17. Zhou, Jian & Coit, David W. & Felder, Frank A. & Wang, Dali, 2021. "Resiliency-based restoration optimization for dependent network systems against cascading failures," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    18. Compare, Michele & Bellani, Luca & Zio, Enrico, 2019. "Optimal allocation of prognostics and health management capabilities to improve the reliability of a power transmission network," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 164-180.
    19. Zhao, Jiangbin & Si, Shubin & Cai, Zhiqiang, 2019. "A multi-objective reliability optimization for reconfigurable systems considering components degradation," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 104-115.

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