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

Filipe F. C. Silva, Pedro M. S. Carvalho and Luís A. F. M. Ferreira
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Filipe F. C. Silva: INESC-ID, Sustainable Power Systems Group, 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
Luís A. F. M. Ferreira: INESC-ID, Sustainable Power Systems Group, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal

Energies, 2021, vol. 14, issue 4, 1-15

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.

Keywords: low-carbon network; distributed photovoltaic generation; dynamic reconfiguration; computational complexity; quantum computing; quantum annealing (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: 2021
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