Publication date: 18/03/2024

Summary: An incorporation of renewable energies into existing energy infrastructures plays a significant role in advancing sustainable development. One of the tools that can be employed to operate distribution networks efficiently and yield various technical advantages is the integration of distributed generators based on renewable sources. However, such benefits require their installation in appropriate locations with adequate selection of their dimensions. Another approach that can be employed to ensure efficient network operation is the reconfiguration of the distribution network. Reconfiguration is regarded as an operational approach to minimize electrical losses and enhance network characteristics. Thus, in this study, two multi-objective optimization methods are used to reconfigure the distribution network and simultaneously allocate and size capacitor banks and distributed generators based on photovoltaic systems. The aim is to meet load demand with the lowest operating costs and power losses, while respecting the operational constraints of the system. The multi-objective optimization problem is solved
using optimization methods Non Dominated Sorting Genetic Algorithm II (NSGA II) and Strength Pareto Algoritmo Evolutivo 2 (SPEA 2) and the Fuzzy Decision-Making method. The IEEE 33-bus and 69-bus test networks were used to evaluate the proposed methodology. Different solution approaches were employed to each test distribution network: reconfiguration, allocation and sizing, and the integrated approach combining both former methods simultaneously. Based on the results, the integrated approach showed a significant advantage in terms of solution search space. For the first objective function, the solutions adopted in both methods in the two test systems showed a significant reduction in power losses, an improvement in the voltage profile and an improvement in system performance. In relation to the second objective function, total costs, there were increases associated with the insertion of distributed generator units and capacitor banks. Finally, the results demonstrate that the developed algorithms possess the capability and robustness to solve the proposed problem.

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