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Improving PV Resilience by Dynamic Reconfiguration in Distribution Grids: Problem Complexity and Computation Requirements

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  • Filipe F. C. Silva

    (INESC-ID, Sustainable Power Systems Group, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal
    Instituto de Telecomunicações, Physics of Information and Quantum Technologies Group, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal
    Instituto Superior Técnico, University of Lisbon, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal)

  • Pedro M. S. Carvalho

    (INESC-ID, Sustainable Power Systems Group, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal
    Instituto Superior Técnico, University of Lisbon, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal)

  • Luís A. F. M. Ferreira

    (INESC-ID, Sustainable Power Systems Group, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal
    Instituto Superior Técnico, University of Lisbon, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal)

Abstract

The dissemination of low-carbon technologies, such as urban photovoltaic distributed generation, imposes new challenges to the operation of distribution grids. Distributed generation may introduce significant net-load asymmetries between feeders in the course of the day, resulting in higher losses. The dynamic reconfiguration of the grid could mitigate daily losses and be used to minimize or defer the need for network reinforcement. Yet, dynamic reconfiguration has to be carried out in near real-time in order to make use of the most updated load and generation forecast, this way maximizing operational benefits. Given the need to quickly find and update reconfiguration decisions, the computational complexity of the underlying optimal scheduling problem is studied in this paper. The problem is formulated and the impact of sub-optimal solutions is illustrated using a real medium-voltage distribution grid operated under a heavy generation scenario. The complexity of the scheduling problem is discussed to conclude that its optimal solution is infeasible in practical terms if relying upon classical computing. Quantum computing is finally proposed as a way to handle this kind of problem in the future.

Suggested Citation

  • Filipe F. C. Silva & Pedro M. S. Carvalho & Luís A. F. M. Ferreira, 2021. "Improving PV Resilience by Dynamic Reconfiguration in Distribution Grids: Problem Complexity and Computation Requirements," Energies, MDPI, vol. 14(4), pages 1-15, February.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:830-:d:493906
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    References listed on IDEAS

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    1. Lueken, Colleen & Carvalho, Pedro M.S. & Apt, Jay, 2012. "Distribution grid reconfiguration reduces power losses and helps integrate renewables," Energy Policy, Elsevier, vol. 48(C), pages 260-273.
    2. Manvir Kaur & Smarajit Ghosh, 2017. "Effective Loss Minimization and Allocation of Unbalanced Distribution Network," Energies, MDPI, vol. 10(12), pages 1-17, November.
    3. Artur Łukaszewski & Łukasz Nogal & Sylwester Robak, 2020. "Weight Calculation Alternative Methods in Prime’s Algorithm Dedicated for Power System Restoration Strategies," Energies, MDPI, vol. 13(22), pages 1-20, November.
    4. Damir Jakus & Rade Čađenović & Josip Vasilj & Petar Sarajčev, 2020. "Optimal Reconfiguration of Distribution Networks Using Hybrid Heuristic-Genetic Algorithm," Energies, MDPI, vol. 13(7), pages 1-21, March.
    5. Wei-Tzer Huang & Tsai-Hsiang Chen & Hong-Ting Chen & Jhih-Siang Yang & Kuo-Lung Lian & Yung-Ruei Chang & Yih-Der Lee & Yuan-Hsiang Ho, 2015. "A Two-stage Optimal Network Reconfiguration Approach for Minimizing Energy Loss of Distribution Networks Using Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 8(12), pages 1-17, December.
    6. Rade Čađenović & Damir Jakus & Petar Sarajčev & Josip Vasilj, 2018. "Optimal Distribution Network Reconfiguration through Integration of Cycle-Break and Genetic Algorithms," Energies, MDPI, vol. 11(5), pages 1-19, May.
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